Techstrong TV – April 8, 2024

Hey, everyone. Happy Monday, and it's a futuristic Monday. Today we're gonna be talking about superhuman ai, invasion of the drone body snatchers, um, as well as carbon counting.

It's 2024. What else can we do? You are watching Textron Gang.

Hi, everyone. Happy Monday to you. Welcome here to the Tech Strung Gang.

As I mentioned in the opening, we've got some really kind of futuristic hope, you know, topics we're gonna be hitting on today, but if, of course, the future is now, and so they're very timely. Let me introduce you though, to our gang for today. First of all, joining us from the road up in New York, uh, the one and only John Willis.

John, welcome, welcome back to Textron Gang. Hey gang, what's up? Very well, good here.

Um, pleasure to have you here, John, joining John and I is our, uh, echo Insights editor and, uh, professional, you know, when it comes to carbon and, and, uh, climate change and so forth. Bonnie Schneider, welcome, Bonnie. How are you?

I'm well, thanks for having me. Fantastic. And then last but not least, again, hailing from the Bronx.

That's left throws, right? Batting 3 48. Yep.

Batting three 40. It's early in the season. We'll see.

Talk to me after the all-star break, our chief content officer, Mike Baard. It's clearly Yankee Celebration Day, right? Yes, It is.

Yes, it is. The, the yas are, are back, yanks are back. Um, but, you know, we could do another show on baseball.

We're gonna have to focus on, on what we've got on our tech strong agenda today. Mike, I wanted to kick it over to you on this superhuman AI story. Yeah, yeah.

It turns out that the State Department is issuing some sort of framework document that's suggests that, um, the AI may spin out of beyond our control. And there's all kinds of calamities that may ensue and it's theoretical, but they're calling for the government to start preparing for that possibility. And secondarily, you're seeing the UN is, uh, calling for a ban on any AI system that violates human rights.

But the definition of that will be, uh, hard to say. But John, I know you've been following this space closely. What is your sense of, you know, how much are we concerned about maybe these systems spinning out of our human control?

Because theoretically, the most important job in the world could be the guy standing next to the plug, right? Yeah, yeah, yeah. Well, you know, the, so I I was wondering if the, um, if the guys can do the wavy Scooby-Doo thing to say that I'm really not that scared.

Um, yeah, I read the report, you know, it's a couple interesting things. Uh, I think they conflate, um, the, you know, this, this is like, depending on whose definition, there's sort of three definitions of ai, right? There's just ai, what they call narrow ai, which, you know, sort of the Siri, all the sort of expert systems.

There's, uh, what they call a GI, right? Which is theoretically where they, they call it sort of like, um, a strong ai and that that's sort of where the computers or the AI are matching human capability, but very sort of focused plan. And then the questions come up with, you know, are, can the machines learn?

Can the machines, um, you know, use examples? Can they plan? Um, the experts would still argue that we're not at a GI yet, right?

I, you know, I think that's an interesting debate. We're somewhere between AI and a GI, you know, depending on what you're thinking about. But this article, and then the, you know, so the, the, the report conflates a GI with the third category, which is super intelligence, and that, I mean, there, there's still about a 50, I, I, I'll just use my numbers.

There's a half debate of the experts that say that we're not a GI, in fact, Elon Musk's lawsuit against, um, OpenAI is based on that. He claims that once we made a GI, they were supposed to turn it all back to open source, right? And that's a whole nother, so, but the article really just says a GI, but it, it throws in super intelligence periodically.

Um, I, you know, I, I don't think anybody believes super intelligence is an issue right now. I mean, it's, it's phenomenal what these machines are doing. Um, but you know, when they use things like, you know, power seeking and, you know, like the, how, you know, like the, the sort of the, the 2001 Space Odyssey, like, it won't allow us to turn it off.

Yeah. It's run by electricity, you know? Um, you know, and I was thinking about this too, like, I worked with a large financial institution, right?

They, they, they've been doing Al Gore trading forever. You know, Knight Capital Story, right? Is a company that literally, uh, and an algar are in an exchange, literally went wild.

And, and, you know, they, they went outta business in 24 hours. So it's like $400 million, like 45 minutes. I mean, how banks deal with that.

They have border routers so open, I has border routers. You basically turn 'em off, you know, and guess what? The, the, it can't take over the world if, if there's like a, you know, if they've got, you know, a number of border routers and you, and, and any responsible organization, and, and most of their infrastructure from what I hear is run by Microsoft, they're gonna have kill switches on routers and, you know, and again, turn electricity off.

One other point, mark, is that I went and read the report, and that report is written by the, um, it was all written by the one company, uh, you know, the, the, the, what is it, the Glacier or what the Glad Gladi Gladstone a ai, so all the, the three authors of that paper or Gladstone ai, well, if you look at Gladstone's website, they sell ai, you know, so I like, and the, the fact that, you know, all the sort of UN and US government was sort of, you know, pointing to this, this paper is that, you know, I was expecting to see a consortium of like, you know, industry experts, maybe like a Josh Corman on it or, or something like that. And, you know, and then last but not least, you know, like I think their outline, you know, like, you know, like one of them is, um, basically, um, outlaw open source of advanced data. That's exactly the opposite of what you want to if's open.

If there's any savior, if there is an a SI coming in the future, or if a GI gets so crazy that we can't manage it. I mean, the, the anybody who's playing with this stuff right now will tell you the answer is to have the models open so we can see how they're weighed. We can see how we can at least, we'll never understand how they're working at scale, but at least we have a cha a fighting chance.

But if it's just, if it's, you know, open AI and it's, it's Amazon Running Claw or Tropic code, you know, like then we, you know, now we, we can't even tell what's going on. So, yeah. So I, I think there's a little bit of hype.

I think we've been, we've been sort of mentally trained of this whole like, sci-fi universe with this stuff. Um, but I'll, I'll just end with the, you know, there are phenomenal things going on in these like neural networks and the people who have built them and maintain 'em will tell you they don't really understand how they're doing the things they do. So I don't know what the right answer is, but I'm, I'm really not that worried.

Look, I, anybody who sits who thinks that they're going to, you know, lay down on the railroad tracks and and stop the AI train Yeah, that's True. They're dead. The train's not stopping.

So, you know, you could write That's right. You could write till the cows come home. I have two questions.

First of all, you said right now, okay, I'm with you on that. I tend to agree, but if not, now, when, when do you think any of this might actually come to a head? John?

I, it, it, I mean that this is like a really, like terrible answer, but it depends, right? But I mean, humans are pretty resilient, right? I mean, if you think about like, like I said, Al Gore training, right?

We've had like flight, automated flight system. I know it's an, it's like multiple orders of magnitude more complex. But we've been living with automation now, again, think go back 50 years and think about somebody explaining to you that we're gonna let planes fly themselves, right?

You'd be like, oh, you kidding? That's insane. What if the plane, you know, I, I remember when I, I sold Chef, um, I, I'd go to these old Linux, you know, sort of like stodgy CIS admins, and they'd be like, what if Chef just starts creating servers?

What if it just starts deleting servers? I'm like, yes, probably not gonna do that. And again, I'm not comparing what Chef does to what, like, you know, sort of a GPT-4 does, and completely different universe.

My point is that we've been living with algorithmic compute power forever, and humans are very resilient. Um, you know, I, I think we'll just sort of maintain, we'll figure it out. We'll absorb and we'll sort of adapt.

But I Think there's also gonna be a lot of pressure, um, politically, uh, you know, how do we regulate what, who's, who's deciding what's ethical, what isn't, and going back and forth with that. Because as we know, you know, there's always a human behind the AI originally that's putting all the data in. So going back to the, the idea of regulating ethics, that that's gonna be also as this, as Alan said, the train is going, I think that's gonna evolve pretty much at the same time or maybe behind it.

We can't regulate human ethics. So it makes you think you're gonna regulate AI's ethics. Yeah.

That's, That's we the, here in the US we have no political will to get things done. The EU might, but certainly Russia and India and China and Iran, and the axis of evil and all these other things, you think they give that crap what any of these people say they're gonna use AI to their own best benefits. And if it comes up in swallows them one day as a result.

Oh, well, second question, John. Yeah. You were talking about putting the controls in and the routers around the edges to prevent anything from spinning outta control.

Uh, humans are gonna do that. So do we need some way to make sure that those things are actually operating? 'cause the last time I checked, humans make more errors than machines, so, Yeah.

Yeah. And, and, and we're, we're both equally gonna make mistakes, right? That, you know, um, but, you know, I think it, it goes back to our resiliency, right?

Like, how did, uh, uh, uh, somebody like Barclays learn to build? In fact, it, it, so at some of these larger financial institutions, they actually have like a two factor human to turn a border switch off, right? They didn't, they didn't do that at day one, right?

They had some real bad issues with algorithms going nuts. And, and, and, you know, and, and over the years they've built these incredibly complex systems, you know, like literally, um, heartbeats within the exchange, heartbeats without the exchange exchange. Like, and so, um, I think our, you we're going to adapt to these things that are going to be mistakes, and there's gonna be a ton of mistakes.

There's gonna, uh, you know, my, one of my biggest fears right now is, you know, as we're accidentally putting this is a more, more scarier thing, is as we're sort of interacting with these things, we don't know what our organization's putting in these models. Now they say they don't train on your data, but they don't know for sure. So I don't know that we're not gonna find out sometime later this year, early next year, that somebody at JP Morgan Chase is gonna be typing in something and they're gonna get output from Goldman Sachs.

Or even worse, an adversary will go say, I am, I'm in funds at JP Morgan Trace. I work in 33 Broadway, and, and I would like to know how to configure the servers for fun. And it answer the question.

I think that's more likely to happen than the computer basically turning off all our lights and, and making us pay a ransom to, uh, you know, some, some Election. And that may happen once or twice, and, and they'll figure it out and fix it. You know, they'll put in some safeguards or whatever.

Um, but let, let's not, you know, invoke images of guardians of the Galaxy coming in here to, to, yeah. How transparent should we be with folks about how that process is evolving? Because let's be honest, we're doing a lot of trial and error here.

And a lot of it is, you know, not an experiment in a lab. It's live. And so, I don't know if we're being candid enough with the average person, you know, I'm not a fan of Elon Musk, and I wrote an article recently in defense of his lawsuit.

If you read the lawsuit, it's pretty interesting. Now, again, like I can go down the list of all the reasons I don't like Elon Musk, and you know, Alan, you know this, right? Mm-Hmm.

But, but I think his, you know, and, and whether he's doing it for his own gain, he owns that X he's an investor in X ai, does he want open AI to just expose the model so that he can learn things? But, but at the core of what he's saying is, um, and he had this argument with the founders of Google. You know, there's a great article that in some ways, I think he believes what I think a lot of people believe is the transparency gonna be in the openness.

And unfortunately, right now, the, the, the frontier models, you know, the, the GPT fours, the, uh, Gini, the, you know, the, the, the Anthropics, um, Claude, those are not open. And, and, you know, and you could argue somebody who's like a data scientist might be ar listening and going a, that d it doesn't know. It's really hard to train.

I know that, you know, like, it's not simple to take Claude, but at least we have a fighting chance, you know? Um, and so I, I think the transparency really has to be driven by, um, like opening up these models and if, if, if, right? And so again, the opposite thing of what the, um, the Gladstone report said, it, it shouldn't be that we need to, um, you know, uh, you know, outlaw open source advanced models.

It's, I think it's the opposite, right? So John, let me set you at is Elon Musk does nothing. That's not for his own benefit.

I know. And this lawsuit is strictly for his own benefit. You know, he wouldn't mind if it wasn't open as long as he had access to it.

But the world doesn't have to be open that, that, you know, again, you think Russia's gonna open up what they're doing on it. You think China gonna open up China? I mean, what they're doing on it, Or Any of these folks agree, but at least come On.

At least we have a, why should we have to play with one hand tied behind our back so we could see the leadership to these other, you know, I geopolitical rivals, Do we wanna get real matters? 'cause that's what we do. We're better at, we're better at being humans than some of these other C We're all humans.

They're all humans. Humans come know in all shapes and sizes and colors. Persuasions, I know we're going left you, but I, I think it's our responsibility to do it the right way.

So, So what about, we've seen in the past with open source, right? Eventually it supersedes in terms of innovation, the thing that came before. So Lennox is, That's the only reason to do it, right?

Well, then it may come down to the point where everybody collectively contributing to an LLM may accelerate the pace at which a proprietary LLM can advance Perhaps. And if it will, it will. But to think we're going to do it for some super sense of ethics, we're not communist, right?

We're not a, a society of atheistic saints. We, at the end of the day, market forces will control this. And if there's a lot more profit to be made, well, it'll be made.

Government Forces will control it too, right? There's market forces that Yes, the government has the will to control it. Um, but I mean, that's my point is there might be, And you don't think the market forces have a say on what the government's forces do through lobbying and everything else?

Come on. Who's being naive now, Kate? No, I mean, but, but I'm, I, what I'm not being naive about is the idea that the collaborative nature of people trying to understand what these things do is going to be our best defense Absolutely.

Of understanding. Absolutely. And at least having, if there is, if the a AI is inevitable, and I don't know, I'm not smart enough to tell you if it is or it isn't.

But, um, I do think at least having fighting chance would be having models that least the collective of millions of people could at least try to understand any aggregate. All right. Our AI tells us, we gotta take a break here.

Hold one second here. In the immortal words of Arnold Schwarzenegger. What?

We'll be back. Okay. I didn't know where you were going with that.

All right. As promised, we are back, and we're gonna be talking about the invasion of the drones. Turns out that, uh, Walmart Chick-fil-A and um, seven 11, and all kinds of folks these days are experimenting with drone delivery, and they're gonna have all these drones that are gonna be flying around our neighborhoods, dropping off your lunch, and all kinds of fun things that will go with that.

But I guess my, my first question is, and I'll throw it to you, but, uh, you know, where are all these drones gonna fly around and how are they not gonna fly into each other and Or into other things, right? Or do we need congestion pricing for drones now? Shades, shades of George Jetson.

Yes. You know, when I was, when I used to watch the Jet Jetsons, that was always something I thought about. Like, there's no traffic lights or lane markers, right?

How did they not collide? You know? Now theoretically, the drones using some superhuman AI might be able to avoid, you know, by talking to each other or whatever.

But, um, I, I really feel like we have to give Amazon credit here, right? Because Amazon came out with drone delivery, or started talking about drone delivery, I guess it had to be seven, eight more years ago. And everyone said, nah, never happened.

Never hap Well, it's happening, it's here. I mean, if we could deliver bombs with drones, I, we should be able to deliver groceries. Um, but there's going to have to be some autonomous driving ability built into these drone deliveries so that we don't, you know, you can have congestion, but I, I would imagine, you know, nothing works perfectly.

You're going to have collisions. Well, That's what I was gonna say. The one of the other X factors here is the weather.

Yes. How are they gonna navigate through, uh, hurricane storms? I mean, I guess that's mostly when people like to, when they're stuck home when during a storm they like to order food.

Um, yeah. But, but when you have strong winds, this is a big issue with, with planes. Well, but I, I could see that.

But you could write software for that really easy, right? You have a wind detector. If it goes over 20 knots doing my nautical thing, um, boom, go to the ground, right?

And don't, and don't start back up until you get an okay or whatever. I, you, you could build those safeguards in. I'm more worried about Mike's drone.

He got a cheap drone, drone from some third, you know, rate country, and, and it stopped working good. And all of a sudden it goes berserk and takes out my beautiful Walmart drone. This is your boating issue, isn't it?

Yeah. This Boat. Well, but it's the same thing with autonomous driving and trucks and everything.

Yeah. It is similar issue. You know, you guys remember a couple episodes ago I was telling you about a presentation I saw from the DOD about military drones, and, and their biggest problem right now is, uh, the commercial create the problem is there's no collective or collaborative intelligence.

So like, when we see these spectacular things with drones where they light up the sky and, and create, you know, images of Penn State University or whatever, right? Um, they're all individually programmed. And this is a similar problem with autonomous vehicles.

I'm not an expert there, but like, one of the hardest problems of, of collective autonomous vehicles is how are they all aware of each other? And so the, the thing I would sort of question is, if DDOD is trying to build, you know, weaponry around drones, and they're struggling because they can't get pro, everything's proprietary. They even get, they're trying to build open architectures to try to get people who create the technology for drones to start creating open, you know, collaborative, um, capabilities and frameworks for these things.

I'm not sure what, you know, you know, what the, the vendors, you know, the e-commerce vendors are gonna do in that regard. Are they gonna struggle with the same thing? Are they creating collaborative nature for, 'cause if, if Chick-fil-A just sends out their drones, Amazon sends their drones and they're all working on different networks and they're not communicating, and you know, regardless of the weather, they're gonna be smashing into each other.

Yeah. Target's gonna deliberately smash into the Amazon Jones, right? Yeah.

And you also have, there Have an army. They'll take the military route, they'll take out, uh, yeah, there you go. That'd be a good sci-fi like target taking out Amazon delivery services.

So, well, You also have the, uh, the human factor into, and let's say you're getting your food delivery wrong order, your neighbor sees the drone, says, gets the drone to come to their, their house. I mean, there's a lot of things that could also happen with the human factor hacking trucks. Yeah.

Yeah. Hacking the drone. Well, hacking I'm drones is one thing, or, you know, up in the Appalachian Hills, some drones flying by my property and, and on the Hatfields, and he's headed to the McCoys.

I'm gonna shoot that sucker out of the sky. I might just do that for fun. Or just People, right?

You're hungry, the gun from a Christmas story and let's go h drone shooting. Uh, you know, So I'll tell you what I am really dubious about though. So we can't even figure out an algorithm that consistently manages traffic flow in a city as it is.

This is with cars and lanes and things that are reasonably structured. So I don't see how we're gonna manage drones who have, can go any which way they want in any kind of collective fashion, Except they are computer controlled. So you could say, look, all Walmart drones have to fly at 2,500 to 2,600 feet.

Uh, Chick-fil-A phone, uh, drones get 1500 feet, Amazon drones fly it. So you, you have that three dimensional kind of aspect to it speeds, you know, pathways. So you can, you know, using a grid like that, I think really kind of segregate your traffic a lot more.

You can than on a city block. Yeah. But how do they drop this stuff off?

Well, that, that's a whole nother story, controllers. I, I think you gotta start making drone ports on top of your house or your board or Something. That's the collective, right?

I think that's the problem that DOD is, um, dealing with. Like, you know, how can you manage these things at scale? Mm-Hmm.

And, and you're going to, they're, they're going have to be a network of intelligence Superhuman ai. Why? Well, according to DOD, it doesn't exist yet for, um, for drones, least according to presentation I saw.

Yep. Is kite flying gonna become illegal then? Because I gotta, like, only have a certain amount of space.

You Know what? Kite flying is illegal in certain airspace. Mm-Hmm.

I, you're talking to someone who grew up near Kennedy Airport. I know this. Um, so it has been illegal and, and I guess it will be unless the, the kites have AI and, and kind of intelligence built in them to avoid drone avoidance.

Um, it's a crazy world. It is. It's a crazy world where you live in it.

And, and my biggest thing is, given all of that, are they, you know, we we're gonna put all of these Amazon drivers and all these, you know, uh, DoorDash people and this whole thing outta business by drones. Is it really that much more efficient? I don't know.

Well, it, it's another thing that might be driving it is not only that, that AI and the technology, but the, the movement to be more sustainable, less traffic, less transportation, less emissions. Um, one of the problems in cities is you have all these delivery trucks double parked. It's more congestion.

So they wanna have more bike lanes. I just lived in Manhattan, so I know this was an issue there. So I think that that's part of what's behind it.

How do we get more cars off the road? Okay, we'll get them in the air. Yeah.

Well, well, why stop at drones then? Let's, let's go full on Jetsons, right? And have flying cars.

That would be cool. Sounds good. I think that was coming At least it was promised like 25 years ago.

25 we're older than that, Mike. It's closer to 50 years ago. Um, but anyway, yes, it was.

And you know, I want my flying car guard on it. Anyway, we gotta take a break here. We're going to, uh, be right back on Textron Gang, and we're coming back with some Echo Insight.

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Well, now we're talking about sustainability. And as organizations strive to be more sustainable, a lot of folks are wondering, where do we begin? How do we know how much carbon we're emitting at this point?

And then once we do know that and we can measure it, then what, and Dynatrace has offered some great solutions about how to do this with their new carbon impact app. And I recently spoke to Klaus Ensen Hoffer, who's the product lead of business analytics, to talk more about the challenges and the capabilities of implementing this app with Dynatrace, You have to balance between customer experience, availability of IT systems, business needs, SLAs, and all of that. And with that, you are adding carbon to the, to the mix as a, as a part of, of, of, of the game.

And you have to balance out between these dimensions. And the beauty is you have to have everything in context of IT systems into, and give every stakeholder a different perspective. Because when you think about it, you have green engineering, green coating, which is aiming for optimization done by developers, by engineers, and you have initiative.

These initiatives do go into IT operations as well, where you try to optimize, uh, the data centers that you're running, the machinery that you have there. All that needs to work together in order to really, uh, produce a reduction. But everybody needs to have the same KPI and focus.

Everybody needs to have the same view on the state of the corporation. And, uh, that way really step by step optimizing, uh, the carbon footprint. You know, Dynatrace also says that, uh, there's a lot of, uh, global mandates that are happening, and that's one of the reasons that they've moved towards this app.

And, um, it's really interesting because it's bringing more attention to IT departments, which a lot of people are focused on, and how we can go ahead and take the insights that the app provides and then make these changes and also report back to shareholders regarding ESG, which is a big commitment as well. So, sure. Um, do you guys think that we're seeing a lot more of this?

I think we are, but I wonder if it's gonna be like going to the restaurant now and everything has a calorie listing on it, right? And every little thing. And so maybe every little thing is gonna have some sort of carbon counting mechanism on it where we're all gonna know exactly, you know, how much quote unquote harm we're doing to the environment.

And maybe we're all gonna owe carbon credits or something because, you know, somebody has to offset that and becomes part of your digital wallet. But it, you know, you can imagine how far it can go. Exactly.

I'm not saying it's bad, I'm just saying I've seen that with shopping. That Is your carbon. Yeah, I think we're already starting to see it.

I, I'll tell you, you know, we're just back from Europe. We were around Paris for Q Con, and then I did some other traveling through Europe. And, and I'll tell you, they're again, they're much further ahead on, they're much further ahead than we are on this.

And they're already thinking like that. What is the impact? You know, I went for a dinner boat crews in Paris, and a big selling point is the boat that I went on was all electric and the amount of carbon it saved versus a, uh, you know, non-electric boat.

And they, that was their major. Well, that, and it had really good food. But, but that was the major selling point.

And, and there were plans within Paris to make all those boats electric. And they had i that one boat saved something like 540,000 tons of, of carbon a year, um, by doing that. And if they did, and you know, the government, the local government had it, if they, all of the boats are electric, the amount of savings would was substantial.

So I think that's the world we're living in. Yeah. We're moving towards if we, if we're gonna be successful, we, we need to do this.

You know what, then you got Sam Waltman wanting to do the $7 trillion project, which is basically gonna be a massive, you know, we're talking about GPUs that, I mean, I think what's interesting, I think what Dynatrace did, which was fabulous, right? Like I, I've been waiting for somebody to do this. I have a friend of mine who talks about this all the time.

Like, could we actually calculate the carbon footprint from like, what, I mean, you can get those APIs from Google and Amazon and, and, um, but the, the areas that I, I think we're just getting such a tip of the iceberg is like, what about all the mainframe use in, in a corporation? com and then now Ed, how do we even come close to calculating the carbon footprint of copilots? I don't, I don't think there is an API for any of that.

There isn't a calculation. I think the real answer is some of the research I've been doing on, on how data center, uh, you know, like taking advantage of, you know, um, composable data centers and, and, and rethinking how we're sort of building the infrastructure. Um, that's where we're gonna basically get, you know, 'cause like to what Alan said earlier, know it says all the time and earlier is that, you know, we're not gonna stop this train.

I mean, right now the NVIDIA and gp Nvidia GPUs are scorching the earth. I mean that there's no, until somebody figures out a better way to do ai, um, you know, we are just, you know, just consuming power at, at a, an alarming rate. In fact, some of my research is that, you know, that it used to be Pete was a concern.

Now it's a major concern in data centers. Yeah, right. Um, you know, so, and, but there's some interesting stuff going on.

Um, you know, that, I'm just gonna write some articles for you about this stuff, but there's really, really interesting stuff going on that, so I, I think calculating and, and, and sort of ESG and having, um, you know, having metrics for your carbon footprint is, it's like anything else. Like it, it's good, right? The more, you know, I, I think, you know, it's interesting.

Dynatrace also put out some statistics that, uh, I thought were interesting about moving your workload to the cloud can reduce the carbon pri footprint by up to 96%. And when we also, um, talk about carbon emissions and what, what's the biggest problem, transportation always comes at the top of the list, especially air the airline industry, for example, um, they found that cloud computing has a greater far carbon print footprint than the airline industry. And average cloud server idle time exceeds 70%.

So, um, there, there's a lot more pointing towards this is something that, that needs to be done. I think generally, as a general population, we, we definitely have our eyes on transportation on, and our last segment, we were talking about drones. Um, but, uh, the amount of energy that, um, that's going into the, our IT carbon footprint, um, the focus is on measuring it.

And then how do we, um, what are the plans that we can make to reduce it? So some of it, um, involves recycling, um, and rethinking how we do with, uh, do way with waste. That's a big issue as well.

And the reason that, yeah, and the reason that footprint is reduced is that these, uh, organizations, you know, Google and Amazon, they really focus on composable infrastructure. So they don't waste, I mean, the problem that most large organizations have have is their, you know, their CPUs are idle, but they're still paying power for it, right? Because they don't have really good sort of ban and, and, and infrastructure in place that, that doesn't starve the cpu.

And that's the biggest problem with GPUs right now is, is the sort of this GPU starvation is that now these things are like power hung, like compute hungry, and, and you know, like if you're paying that kind of power, uh, tax on A GPU versus a CPU U, and it's basically idle 60% of the time because you can't get the storage and the memory and the shared memory. So anyway, again, that, that, I think that's for people who are still gonna run their own data centers. There's their sort of advantage is, um, you know, trying to figure out how to create composable infrastructure so that you can sort of make use of, you know, sort of the storage to CPU, all those things.

Uh, and again, that's really fascinating stuff going on right now. I Think networking has a big part of this conversation. 'cause we saw early on people were building data centers in places like Iceland or upstate New York near water sources.

But then it became an issue about how quickly could I access that compute over network. It was too slow. So you saw kind of a shift back to mirror metropolitan areas where I was closer to where people were consuming the data.

If we wanna build more efficient data centers, whether they're in Canada or wherever else, we gotta have better networks. 'cause otherwise we're stuck. Agreed.

Well, that's the, that, that's the thing about the sort of composable, right? Which is the bottleneck is the storage to compute, or the memory to compute, right? Or, and, and so being able to create sort of multichannel environments where you can really have high speed, high bandwidth, high, you know, um, that, that's gonna sort of reduce, in sense, reduce the power consumption because you know, you're getting more value if you, if you're, you're running, you know, uh, a million GPUs and they're only based and they're being star of 60 or 70%, I don't know the exact numbers.

You know, what would happen if you didn't star if you could get their sort of utilizations up to 70, 80, 90%? You know? And again, that's I think what, you know, what Google and Amazon have perfected over the years is the ability to build network and infrastructure like that.

You know, that that's typical of sort of first generation, early on stuff. Like, first you want to get just the functionality, then you start optimizing. So I, you know, again, John, to your point about ingenuity of human minds, human, I think that'll be the, you know, the gen two, gen three, gen four will continue optimizing and making them more energy efficient, letting them throw off less heat, you know, because someone will build a better mouse trap otherwise, and that, again, market, market forces that work there.

All right, so buy land in northern Canada today, so you can sell it to somebody who's gonna build a really big ass data center tomorrow, Or it could be beachfront in 50 years. You know, I don't know, the climate change might not be good For me. If, if, if you, any of people listening there, kids are going into engineering.

Um, you know, this friend of mine, Pacific Crest, who, who gave me a whole bunch of analytics on, on this whole subject, he said that right now one of the hottest jobs is a, is a job that people weren't even taking, is getting trained at heat entrance engineering. It's like, you know, something that nobody really cared about for the last 10 years, and now all of a sudden it's probably one of the hottest positions you can get, you know, um, you know, and so Crazy. We all, you know, that begs the question of the jobs of tomorrow.

All of these things that we spoke about today, superhuman ai, drone delivery, and, and regulating and controlling, you know, at scale carbon footprint and measuring all of that, they're gonna create jobs that don't exist today. I agree. Mm-Hmm.

But the John's point, the guy driving the HVAC van around town, you ever see those guys? Yep. He's laughing all the way home.

Yeah, totally. You know what, you're still gonna need those guys too. Absolutely.

Anyway, if we don't have anything else, I think that's gonna call a wrap on today's Textron Gang. Stay tuned. We have a full, uh, Textron TV schedule for you for the next couple hours.

Um, and then we'll be back tomorrow with another fresh episode of the Textron Gang. Until then, John, thank you very much. Bonnie.

Michael, thank you very much. This is Alan Shimel. We're out.

All right. I hope you enjoyed today's text Drunk Gang. We had some real futuristic stuff there with superhuman ai, drone delivery, carbon counting and, and more.

But now it's time for the rest of our Text Drunk TV show. And let me tell you what we have in store. Uh, first of all, an interview I did with Comrade Comrade's, a large company based over in Europe and all over, I had a chance to talk to their COO of Vlad, and it's a tough last name for me, Vlad and Tan Civic.

And we spoke about, you know, today's disaster recovery versus, you know, the old days where we had big tapes. Good conversation with Vlad. He's a really bright guy, and, uh, I recommend this.

This is Textron tv. Hi everyone. Welcome back here to Techron tv.

I want to introduce you to Vladin, and I'm gonna do my best on this Vladin v den. I maybe we're gonna call him Vlad. Vlad, welcome to Text Drug tv.

Just for the record, say your name correctly, so your family hears it. They, they don't think, oh, that guy butchered it. Thank you.

So my last name is Vic. Sorry, little bit long. My name is Vlada, but you can call me What?

So, you know, there was one very good famous basketball player in NBA. He played the Lakers and Sacramento kids. So V is pretty to Pronounce.

Yeah, no, that Vlad has become, especially for basketball fans. Uh, a very popular name. Right.

So good, good for you. And Vlad, thank you. And I appreciate you, you coming on and, and talking to us today.

So Vlad, you are the, uh, COO Chief Operations Officer at a company called Comrade Group. And before we get into Comrade Group, let's talk a little bit about your background. How did you come to become a COO over here?

Well, we need to go back to, I'm more than 30 years in this business. Originally, I'm a software engineer, so I was educated from the military engineering. So my background was military engineering.

And actually I started carrying my career in gun factory. So I ended up producing this software for, for the, you know, the gap picture is very, very old, established 1853 in my hometown. This is where I'm original.

So basically, I think it's pretty good basic, you know, if you are starting to become the military region to, to produce the software, you need to think about a lot of things. Not only about the innovation, but you also need to think about reliability. So I start by that group and then, you know, to different, different stories.

10 years ago I started with the software engineer. Nine years ago I was on C Chief Corporation officer for the whole S group. So you maybe remember the Yugo s So that company was producing the Stan cast.

So you, after nine years, I started as a programmer, but then I was on still level, uh, in the board of directors possible for the, call it, there was about 46 companies in that room. But then I do the contract delete, and then I start my own company, stop the outsourcing call. So that was the huge start.

Sure. You know, like typically to go out to your conference zone. So I step out, I created the software outsourcing company, but we were entertained at that time, the Microsoft boutique, very, very profitable Microsoft technology.

And we, our clients was through the premise of that company, our company, we were creating this software from the HP but also from the Microsoft themself. And then five years later they acquired my company, then company with a few steps. I was the GM of few companies, then I stepped out, rejoined started, I did a lot of things from the corporate perspective, but also, I don't know, did I short share my story?

But actually it's A fascinating story. I haven't heard people talk about the Yugo in a long time. I'll be honest with you.

It's been a long time. But in many ways that Harold did a lot of the, you know, the small smart card, the minis, all those, I mean those that, that's taken us back. Sola Comrade.

Yeah. Some of our audience is probably familiar, but I'm gonna bet a lot of our audience. Is it, how would you describe Comrade to them?

COMT is company born in 1990s and we have, I would say the two major, major footprint. One is in distribution, and the second one other one is the stock develop. So, you know, we start with one branch as a distributor of the well known brand they're thinking about at the Arctic region.

This is where the original, the company was bought. And then there was two companies, one focused on distribution of the title Dell, uh, compact, Like channel, Like channel. And then the company who was doing the stock develop more than 1000 engine.

And then because joined 2008 company acquired the other companies and became the biggest company in this area, providing the services global from the early beginning in 1990s, we start working with, and therefore HP for could create one of their products for the disaster tower called Data Protect. Because this is the original, how we start being really deep, deep in this disaster recovery back the globe. Then came the other customers that we were providing and develop stock for that such as automotive industry in industry, you know, then we created several companies back in this 30 years, three zero years, we spin off several companies.

For instance, in 2020 we spin off company called Company Digital Service to End and company listed the new stock exchange. So we spin off that company then, uh, 2021, the spin of the other company called ku. Ku, very well known in disaster recovery world and actually capital KU for us.

So, you know, you see 30 years ago we start doing software develop and business from the real tech company called, there is still of several companies, but still some of them don't even scared. Why? Because the, the one part of the company type services remain to Quantum.

And then we the company for three six. So the quantity 360 was what we actually speaking about all the knowledge from those 30 years. When we start to building some specific solutions, the product develop is still here.

But in short, I don't know that I give you any idea about the content or if we are, think about Thes revenue is around 500 million euro thousand million of per year. And we have around 3000 employee in different Wow. That's sizable.

That's a big company. It's a big company. So, and, and well what's funny is even though you've spun off companies that got acquired by Bain and stuff like that, but your heart though, there's still the uh, Dr.

Disaster recovery kind of expertise within the company. Um, and, and I wanted to talk to you a little bit about that today, right? Look, I'm like you, I've, I've been in technology a couple, couple minutes myself, right?

A long time. 30 plus years. And over that time, you know, there was a time where disaster recovery to me meant you had big tape machines, right?

And a tape library. And then that tape library was very automated. There'd be like a robotic arm that would pull that tape out.

And, but you know, and, and thank God we never had to really restore very often. 'cause what we found out is it could take weeks to restore something if it, if it went bad, right? The so, but DR has always been i, 23 years, I'm down here in Florida now, right?

And our fear is always that some big hurricane is gonna come and wipe out the data center. And how are we gonna, well, you know, cut off all the power. We need generators, we need backup, we need offsite backup, we need everything, right?

The cloud came along. The cloud made a huge difference in terms of DR because we were no longer tied to a data center or even a company's data centers. We, we could be in this region and that region and mirrored here and mirrored there.

And it kind of changed the DR game. Then we sort started things, things like ransomware and all of a sudden having a DR that was off Preem not connected. Wow.

What a lifesaver that was for a lot of companies. So we've seen a, an evolution of, it's not, it's not your father's disaster recovery anymore. Talk to us, you know what, but I'm not in it.

You are in it Vlad, You know, what's the latest and greatest. Yeah. Funny thing when I start my professional career, I started in IBM computer center.

So I remember the things. You remember those, huh? Me too by my own, you know, Uhhuh and yes, as you said, you know, we in the, for instance, in one of the companies, we have two computer center and there was one other, you know, disaster recovery center because we replicated the data mostly over the weekend, you know, Sunday, Saturday, Sunday, like, and yeah, but the technology dramatically changes.

You said were the internet, then the cloud, you know, all those dramatically changed, but what didn't change for all those years didn't change. The reason why you need to have disaster economy. So first of all, you need to have the business.

So the business continuity is the best. So it was 30 years ago, 2000 today, what's changed? The speed of technology change and the impact of technology on whatever business you're doing is not much big.

What does it mean? It means that you really need to invest in advance to the disaster. What is a typical mistake making 30 years ago of now it should be invest in disaster recovery before the disaster happen.

Or it's always, always smarter and cheaper to invest in, in front of, you know, to be, to, to act in the front of the energy disaster. So this is what was happening, but now based on the cloud technology, it brings us a lot of, let's say, decrease of the cost. You know, it's much easier now not to have all the premises.

You remember how it was, you know, given the cooler as as a house B, so now you don't need to think about it. But now it's really the matter of the privacy because when you order your own, you know, the disaster recover as a service, it's very nice. It's easy to, you know, just click and tab it.

But on the other hand, you know, it's about two, I would say very, very challenging things. One is about the privacy of the data and the second who is responsible for the data. Meaning it's somebody who is your cloud provider.

They are not responsible to keep your data back up from the data is not only about the data, also the application. You have all your business application in the cloud and you know, they said, okay, it might lose. We don't guarantee you that it'll be there.

So what does it mean is the queue. If you want to prevent disaster, we need to stick, okay, how can I really have the full operated disaster account? And then now we are planning to this either hybrid month, however specific service product.

So this is where we as the 360, let's say the company experience, okay, let's do the analysis. What is very, what you need, what kind of the data application they need to recover, how fast need to do it, and then put different product to, I dunno, did I a little bit? No, No, I I think you're dead on V These are, these are the things that we, we need to do here.

So you, you know what I see, you didn't mention AI though. Everything we hear about today is ai, but I've heard people say AI is gonna make DR much easier. It'll be totally automated, easier to back up, easier to restore.

What are you saying on that? I have to come back to my history because in some particular, I was running system integration in the 12 countries around the Southeast Europe and uh, we have major clients in banking division. Most of them are sensitive deal, different type of sensitivity.

Why? Because in the banks, you know, if, if the bank is not running all the time, they're losing the line, then there is a reputation. If they lost the data, it's totally disaster.

Now why, why am explaining those two? It's about the business continuity and it's about the data. Now for the banks, they wanted to have disaster recovery because the, the, the sector banks and the regulators forced to have, for us as the system rate was easy money because we offered itself.

But then problems came. The problem came in the moment when they really need to prove if something happened, they need to run the, they're speaking about the needs. Now in the government, it's totally different.

The is okay for us to, they start offering the service to citizens in a one day or three days. It's not critical as that. But we need to recover and to secure all the data.

Don, you must secure, there is no data breaks. So don things actually forced us about this. All those things.

Business, how to ensure that the service will be in the banks of the real time industry will continuously, and then in the government to keep the data monitor. Then you can force to, let's say, to postpone the, let's say the recovery process as the answer of those questions and what you need to kick mark, how the solutions that we provide, how together, this is actually what we do believe that it's most critical model. Now we said about the ai, AI I don't think can replicate the, the human in all in the aspects to do this replication of the database, of the dates.

Just do the snapshot of some application. It's fine. And then when it comes to run the service around it, I'm not sure that it'll be running.

Just like, so we are also using a lot of machine learning AI stuff to make it very efficient, to productive, to do this application. But when it comes to run the service, then somebody needs to monitor and then jump in and to, this is our do. Understood.

You know, Vlad, I I feel like I, I we missed, I, not that we missed, but I didn't explain it good to the people out there. When we talk about comrade, it's Comrade group is sort of the parent organization, right? Comrade group and you're the COO of comrade group.

But the whole DR thing that we're talking about is part of Comrade 360. Is, is that correct? One part is because we are, we are, uh, we can take to enjoy the product development for some different vendors such as Tructure, well such as hp, such as cyber forecast.

This is what we are doing in the other companies we offer, we have system integrators and then we are offering those services. So we are using some of the products from the vendor. We are the, and then implementing and giving the business.

So there are different things that we are offering. The first we approach to the customers, they want to go some for something. The business plan, what, what type of disaster recovery solution we would like to set up.

It's one thing, if there is a way we would like to build some product because we did it in past several times, then system come in and say then all the requirements then, then all product develop the cloud you do is the answer. Because the cloud, yes, it's time to tell, but the end of the day, you know, data like to keep from the premises because of different reason. Maybe the volume of data.

If you have ations very, very, very cost expensive for to put all more data in the, maybe the, You know, you think about it, right? The cloud was supposed to take the expense side of that. But what we found out is it's still cheaper to keep the stuff in your own data center than into cloud.

You need to really understand what, what impact your business from the critical point of the cost point of, and then to do the best for your business. Absolutely. Vlad, we're almost out of time, but I wanna make sure for people out here watching who's, you know, look, Dr is every single company watching this needs a DR plan, right?

What's the best, like what's the path? What website should they go to to engage with Compr? What's the website?

They go? Yeah, website is pump com. Our quarter of that company is in Boston, Massachusetts.

And we have the GM store that running operation. The US they can find is easy to find, to understand how to us, what to see, what would be the, the, the right question. They carried the case studies.

That's the best way to know about that. And what, what's the web address? I didn't hear it colleague.

com? Compre 360 what? Comp 360.

Do com. Dot com, perfect. Alright, Vlad, that's the one thing if I, if I don't get anything else out, I want them to know where to go get that.

So it's Comrade 360. com. Vlad, I want to thank you for coming on today and giving us What a fascinating story.

You know, like, like a lot of work in it. A lot of it isn't sexy. A lot of it is just the nuts and bolts of how, how we do business.

Disaster recovery is one of those things, right? We kind of take it for granted. Yeah, of course we gotta do it, but people sometimes need to be reminded.

Unfortunately, it's when disaster strikes that they get reminded. Yeah. But it, it's good to know when you don't need it, that it's there and, and how important it is and work goes, and how people like you and the folks are comp trade, you know, work on it so that when we do need it, it works the way we want it to.

Thank you. Keep up the great work and we'll have you again soon. Thank you.

Next up we're gonna start running some of our cube con Paris. Hold on, hold on. Videos.

And, uh, the first one out of the, out of the shoot is one I did with my good friend, Tali Notman. Not Notman. Tali of course, is the CRO at j Rog.

She was employee number six there. Fascinating story. Her background, you know, salespeople born, not trained or trained, not born.

She was the HR person, but she took on sales and man, she, she, you know, commands a sales force that's responsible for hundreds of millions of dollars of revenue right now. I love talking to Tally. She's great, great person.

She's really the power behind the throne there. Here's my interview with Tally Notman. This is Textron tv.

Hi everyone. We're back here, live in Paris on the busy buzzing show floor of Q Con. I am really happy to introduce our next guest.

com. March is 10 years. 10 years already.

Oh, yeah. And if you've watched us, we always have on Shlomi be my friend who's the CEO at Jfr. We've had Y Laman many times.

My good Fred, Fred Prince, the three, those are the three co-founders. We've had Stephen Chin, we've had countless people from Jfr, but this is the first time we've had this woman right here. And I will tell you a dirty little secret.

She's really the power behind J ffr. She's their CRO. Her name is Tali Notman.

And we are, I'm thrilled to have Tali here with us. Hey, Tali, welcome. Hey, thank you so much.

I can get used to it. All these compliments. Yeah, no, don't let it go to your head.

We'll keep you about. So, Tali, let, let's start with kind of your Jfr journey, right? You've been with Jfr, what about 11 years?

12 years? Well, You won't believe it. It's been 14 years now.

14 years, Yes. Yes. Almost from the beginning.

Right? Since the very beginning of the commercial, uh, the Go-to-market journey. This is where I joined.

Absolutely. Yes. And, and your journey from Jfr has seen you kind of take you and your family from Israel move to the Bay Area, though.

When are you ever home with running around, but Right, right. I mean, you more as much as any of the other people I mentioned symbolize really the success. Thank you.

Thank you for that. Of Jfr. I'll tell you the first thing that it symbolized is that, uh, I'm a follower.

I'm a follower of my co-founders, my CEO and for the good reasons, uh, you know, the success comes second. The first thing that comes is, uh, really the people and their values and what they bring. And I follow them from the previous company to Jfr.

And not only that, as you mentioned, right? I follow them between, you know, moving from one country to another, um, and 14 years later who would believe that this is where we are at an amazing journey. It's been an amazing journey.

An amazing Journey. Definitely. Definitely.

But I've, I've always told Shlomi this, you know, I have a good seat. Not today, this is not as comfortable, but I have a good seat of the DevOps world. Yes.

And I get to talk to a lot of all the companies in DevOps for my money j Frog's culture that they've built. And, and it starts with Shlomi and, and comes down the, the culture at Jfr is really when, when they talk about the frogs, right? Right.

They talk about that feeling of we're on a mission together. Yes. It's very strong there.

And, You know, uh, my personal story is a little more unique than you can find in the market, because I actually moved from HR to sales first time with J Rog. Really? That's really the story.

Yes. And, um, I think that one of the special thing about it is that, uh, it's all about people. So I'm here now at Q Con following a few weeks of events, as you mentioned, right.

Traveling quite a lot. But the last weeks I was, uh, hosting our top customers events in Europe and in the us uh, speaking with our top customers, hearing what is it that they have to share with us? And it's all about relationship building.

Yes. But from there to last night, frogs and friends hosting the, uh, community leaders Yeah. Together with you.

Definitely. That was an amazing event. Thank you.

And it's really's all about hearing, getting the opportunity to share the highlights of our roadmap, hear the feedback from the audience, and now here in this, uh, exhibition hall to hear the, uh, the, the pains and the challenges of the users is something that drive our growth. This is what really fuels our growth. So it starts with this value of people to people Right.

And their relationship and being a good listener to what are your pains? What are your needs? And then from here, put the investment in the right areas to meet your commitment to your audience.

Absolutely. But that's, that's kind of the secret sauce behind it is jfa, you're not supposed to tell anyone. Oh, yeah.

But, um, but it, it, it's true because it, it is a very community led, customer focused kind of organization. As part of that, you guys recently did a study Yes. Of kind of what were the priorities?

What were the, the, the, the aims, the goals, the technologies that customers want to do, you know, have more success with, more involvement with. Yes. Yeah.

Tell us about it a little. Yeah. So there are, you know, I mentioned before being a good listener in collecting the feedback and understanding the needs.

Uh, one of the, the additional things that we've done is that we met with CIOs, of course, our customers, but we then we actually reached out to our, uh, partners on the, uh, banking side, the, uh, research side of the banking here. And, uh, we asked them to share with us the survey of 2024 of what is, uh, CIO's top of mind, right. And the amazing thing.

And we said again, my team analyzed the, uh, the, the results of the survey, um, by six or seven different, uh, banks. And part of what I was looking is to figure, you know, eventually the audience that we have here at CubeCon will get priorities from the top as well, right? This will come from the C 11 CIOs.

We wanted to understand what is top of mind for them. And Ellen, this was music to my ears. Everything that Jfr put the investment in the past five years is in full alignment with what is top of mind for these CIOs.

And I'm talking about the platform play, right? Yeah. The end-to-end, um, uh, DevOps solution, the end-to-end software supply chain management solution.

I'm talking about the cloud and multi-cloud strategy of the CIOs. Mm-Hmm. I'm talking about security.

Yeah. Is a key, definitely a key for their success. And now these days, ai ai, right?

Definitely Everything is ai, you know, we are, and, and I haven't announced that here, but we're working on a big project, uh, Mitchell Ashley, who's right off camera heads our research, uh, a, a research study on what we're calling DevOps next. Yeah. And it's exactly what you are saying is 10 years ago when I started this, 12 years ago, 13 years ago, when I first got involved in DevOps, there were a lot of point solutions that were cobbled together, right.

To make it kind of work. But it, it didn't work seamlessly. So you had Artifactory and you might've been using Maven, and you might've been using Jenkins, and you know, all of these things.

Yes. What we're seeing now is the maturation, the maturing of the space where you need that end to end platform. Correct.

People don't want to cobble together like, um, uh, you know, uh, a mishmash. Yeah. And, um, this is the state of the art.

And now on top of that, you get this whole cloud native. Oh, yeah. But you know what's interesting, the interesting thing that is coming from this survey, and what we also hear from the field is that they know these CIOs.

They know that they are going to adapt even more tools, especially by the way, in the domain of infrastructure and security. Yeah. But here's the, the amazing thing.

95% of them are saying that they are going to adapt more, but search for ways to how to consolidate. Right? So if you are, and looking at the security space as an example, if you have a security platform that you are able to provide me the end-to-end solution on my DevSecOps journey.

Great. And part of our, again, support to this, uh, community, to this, uh, customers, is really to give them this end-to-end solution. Definitely.

So what they want is more functionality, more feature set, Less vendors, but Less providers. Yes. Right.

Providers we don't want Yes. It it, because it, it just introduces too much complexity into the whole, the whole system. Yes.

And look, again, this is part of the journey I talk about at RSA in, uh, two months, we're gonna be discussing this at the DevSecOps thing, which is DevOps is DevSecOps. Yes. DevSecOps is DevOps.

Yeah. Um, So you mentioned CIOs. A lot of people at home may, they, they think of CubeCon, they think of a lot of DevOps software, open source.

Some of it is free, it's downloadable. I get my hands on it and I use it. And that kinda lends itself to a bottom up right.

Kind of sale. Right. But the truth of the matter is, in talking to people like you, is these platform sales and stuff are much more top down.

Correct. But here's the thing, Ellen, and you, you know, last night when I attended the Frogs and France, right? Some folks ask me, well, you are Jfr, CRO, why are you here?

What's in it for you? The community, the developers? And my answer is, uh, very simple.

First of all, as we mentioned at the beginning, I was raised by open source people, right? At Ricks moon and you a Landman uhhuh. That's first and most of, but it's really eventually, even if I'm not looking at it as a, what is the right sales motion for me, going back to people, if I want to enable the right solution for those who has the pain, I need to be able to open the doors for them from the top down and bottoms up.

Right? And this is how I look at it. And therefore, you will see that Jfr is investing in the platform not to just be able to bring the, a good pitch for, oh, we have an end-to-end solution.

Each one of our teams, each one of the solutions, we are looking at them as best of breed, making sure that they are top in the industry. Yeah. And with this approach, you are able to bring, to answer the need and the concern of the sea level, but also provide solid products for the field.

It works together, in my View. Absolutely. So it's top down, bottom up, it meets in the middle, and it all goes hopefully.

Well, Yeah. Speaking of that, look, I, I am sure our audience knows Jfr was one of the first public DevOps companies. That's right.

Right, right. That went public. That's right.

How has that changed your job? Well, whew. Um, I mentioned before that this is a 14 years, uh, journey, but the last three, now almost four years since we went public, um, I would tell you that I think that number one, number one thing is the fact that, you know, that everything, that the responsibility you had so far, and I always took, don't get me wrong, I always took my responsibility very seriously.

Seriously. Oh, I know you did. But at this point where you have all these investors that are actually trusting you, and you are responsible on a quarterly reporting, on a quarterly commitment, this makes the level of, uh, my commitment to Jfr Rog to the success, to the success of the customers, even, even definitely higher.

Uh, but it also brings, part of what I find is that to our customers, it actually bring more trust. You know, Ellen, these days, everything is about trust. Yeah.

And the fact that you are not anymore a private company, and you don't know what's gonna happen tomorrow. They know that we are, we are there to support them in this journey for in Every Way to Right. To infinity.

So this is the privilege that I have when I know to say to, to tell again, our users, our customers. Hey, JRO has a really good, uh, back definitely a great experience, actually. Yeah.

I wanna ask you another area. Yeah, sure. So you mentioned you weren't always a salesperson.

Now with salespeople, there's, there's different schools of thought. Yeah. One is, salespeople are born not trained.

Another is, salespeople are trained. Right. Anybody could be a sales person.

Can you guess me, My type? No, You're not gonna Guess. You're trying to break the mold.

I, I think you're born, my personal opinion, you're born. Alright. But you did it, you were an HR person as you mentioned.

Yes. Switched over to sales. And it's one thing to be a salesperson, just because you're a good salesperson as a born salesperson, sales doesn't mean you're a good sales manager.

Yes. That's also a different set of skills. How has that been for you making that switch to sales and also managing a global sales team for a public company?

Right. Right. So I'll tell you a little secret.

When Shlomi offered me my CEO to John Jfr at the beginning, I told him, you are five people. Why do you need an HR manager? Right.

What do you need me for? And then I joined him to really support him, uh, with some leads he got from Java one, back in the days, um, when finally, you know, he made the formal offer of, uh, me staying in this, in the in sales. I told him, no, I'm not.

And the reason was because for me, it was a no go to, uh, be in a space where, um, you know what they, you know what many people think when you say sales men or saleswoman. Right? And this is not so much aligned with my, my values.

And then when I told him that, he said, why do you think I want you for this role? That's exactly the reason why, you know, Ellen, in our codex, we have our codex is the set of values of J Rog. Mm-Hmm.

One of the values is, uh, community and customer happiness. Yeah. And when we, when we chose this value, it took us really time, okay?

And it was, by the way, raised by the, uh, the team, by the frogs as we call them. I remember. And when we were, uh, looking for what is it that we feel, it was not about customer success or customer satisfaction, it was about customer and community happiness.

And for me, this is the only reason that I managed to take this move and move from HR to sales, was because I knew that this company, these people are treating first of all the people and only then the business. Right? Now you are right.

It's one thing to be a standalone, right. Uh, An individual contributor, Right, right. In this role, it's a different thing when you need to manage the entire organization.

And especially as the level of complexity is definitely right, uh, rising with, uh, becoming a public company. But, um, if I wouldn't have, that's the truth. The support and the ecosystem of all frogs around us, and the connection from the understanding of the commitment from the product side all the way to the financial side.

I, I, I will be very honest to tell you, it's not a one men show. No, not at all. It takes, it takes a, it takes a whole bunch of frogs.

Oh Yeah. A whole bunch Of frogs. A village of frogs.

Right. But when I'm sitting with our product team, and I know that when they plan the roadmap, it's not about, oh, we know best. It's about collecting the feedback, right?

Getting the, uh, the, the, the information and the need from the field from our customers that I know that they feel the pain that my customers feel. I know I'm in good hands. I know I can manage it.

I know I can go through this challenge. Well, if you're a J Rog customer, you're in good hands here with Tali Tali, I want to thank you so much for coming on. I know you're busy.

It's a crazy, crazy event here. It's a great one. But you Know what, as they say in Jfr, may the Frog be with you.

May the frog be with us all. Thank you, Ellen. Thank you.

Thank you for that. Tali Nachman, uh, CRO Jfr Live Act Cube con. We'll be back in a minute with a another.

Guest. com is the leading resource for news analysis and education on challenges facing the cybersecurity industry. com covers all aspects of cybersecurity, including data security, DevSecOps, cloud security, application security, network security, security threats, and more.

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Home of Security Bloggers Network. Next up, we have an episode of the digital CX O podcast. In this episode, uh, our own Amanda Ani and Mike, uh, bazar speak with Meredith Graham, chief people Officer of insano, about the findings of their recent Speak Up 2024 work survey of 1500 women in the tech sector.

There's some surprises there, but a lot of it you can probably guess. Next up we're gonna go back to CubeCon and our own Mitchell. Ashley speaks with Cheryl Hung senior director infrastructure ecosystem at Arm.

And we're gonna discuss how ARM is powering the future of cloud native AI in the cloud native community. Hello and welcome to the Digital six oh podcast. I'm excited to be here today because we have our first guest, Meredith Graham of Inno.

She is the Chief People Officer. How are you doing? I'm doing great today.

Thank you. Wonderful. And also with us is of course, Mike Vard.

How are you? I'm doing well. I'm excited to talk about this topic, but, uh, maybe a little scared at the same time.

So to begin, Meredith, can you share a little bit about in Sono first, and, uh, then we'll talk about a recent survey that y'all did? Yeah. Great.

So, um, in Sono, so we have about, um, over 3,400 global associates. Uh, and we are a managed service provider and we provide services to Fortune 500 companies. Um, and we provide ser those services mainframe to public cloud, um, and digital applications.

So I'm really excited to be here to talk today about, um, AI and how it's impacting women in tech. Awesome. So with that being said, y'all recently released the Speak Up 2024 survey, and this was a survey at 1500 women, um, across, uh, three different countries that are working in the tech sector.

So you mentioned ai, so we can start there. There was a lot of information, but we know that's the big topic of the day. So what are some of the key findings in the AI section?

Yeah, I think, um, which was very interesting is, is that of the women surveyed, a large proportion of the women, um, were really taking the lead in AI and what AI means, um, within the tech sector and for their careers, which I thought was very interesting. Um, and then there was also a lot of women mentorship as well. Um, so, so that, um, in and of itself, you know, leads me to believe that, you know, women are truly interested in this next step and this next phase of, of, of AI and how it's really gonna impact their world.

And then the future world. It seems like to me there's a bit of a difference though between the building of the AI models and the using of the AI models. And I bring that up because we've talked in the past with num, numerous folks about the STEM education issues that women encounter, and they kind of disappear from math departments in seventh grade.

And, um, there's a lot of men building the AI models, and I don't think they're deliberately injecting bias, but there's cultural bias that gets injected into the model itself that then manifests, uh, way out to the use cases. So, you know, what's your sense of, have we thought this completely through, or are we just kind of jumping in and then we're surprised when there are some, uh, suboptimal outcomes, as they say? Uh, no, I think, um, I think we're jumping in, as you said.

I don't think, um, you know, I mean, I think, uh, technology always outpaces kind of, um, you know, legislation and regulations and just kind of the thought around like, what is the true impact to, to the human. Um, and I think that that is why really women do have an interest in it, because I think that they are looking at it, I think that we are looking at it that there is gonna be a huge impact, and there's a potential for a lot of good if you can ensure that these models don't have those biases. But then there's a lot of risk if they do have biases.

So, um, you know, just around, you know, talent development and, and those types of things. And just ensuring equality. If if there's any sort of bias that gets into there, how do you stop it?

And I think that those are the questions, um, that, that people are asking. And I think that's why Ribbon really are interested in, in what it means and, and how they can participate in the development of AI as it is very new. So I remember in the report there was some information about remote work versus in-person work.

And for women, it seems like they're struggling a little bit if they're working remotely. Why is that? And can you share a little bit, uh, deeper information about that?

Uh, I mean, I think, you know, just stepping back, I don't think this has anything to do with, with ai. I think there's just struggles around, um, remote, remote work. Um, and it's particularly, you know, in, in businesses where there are kind of hybrid in person and then remote, um, I think that women in general enjoy remote work immensely.

Um, actually, actually I think a lot of people, um, do because it provides a lot of flexibility. Um, but there are, you know, inherent risks. So if you have people that are working inside an office and, um, are seeing, you know, the executives or the leaders day, day in and day out, there's always just generally that tendency to kinda lean towards that person that is sitting next to you and having that conversation and not necessarily sharing all of the information that you have with the remote workforce.

And I think it's just something that, um, you know, we've been working on since Covid and the world shifted around. How do you ensure that there's a level set across, um, the organization when you do have in-person hybrid and remote? Um, and I think it's a struggle, but I think it's more of a leadership development that, that we've been working on definitely, um, throughout the time.

And I think that there's just continued, um, leadership development that needs to occur to make sure that it's level and, and equal. Now, this part of the survey left me scratching my head a little bit because, you know, I worked in an office and had been doing so for the last year and a half, but prior to that, I spent the better part of a decade working remotely. Um, and as I went through your list, you know, I was like, yep, experience that.

Yep, experience that, check, check, check. I mean, it seemed to apply to both male and female. It's, it's just the nature of the beast.

Uh, agree. No, I don't, I don't think that there's, um, female versus male. I do think that there's a tendency for more women if it's hybrid, remote, or in person to accept more of a hybrid remote model just because of, you know, caretaking duties and things like that.

I think the flexibility around, um, child, you know, taking care of your children or, you know, even, you know, parental responsibilities, I think that flexibility just leans more towards that than men. Um, but that's, um, just more around those caretaking. But I agree wholeheartedly that, you know, if you're remote as a male and a remote as a female, it's probably gonna imply equally.

I would say the only difference is, the only difference that I do experience working in the office is that my wife is working from home now. Um, you know, she just randomly throws stuff on my calendar and expects me to do it. So I'm like, okay, there we go.

Well, that's relationship, so we don't wanna go there. Well, that does bring us to, to the next part of the survey, which was about that work life balance. So, um, what were some key findings there and what are some of the struggles?

Um, and what advice do you have as far as balancing, um, you know, the family care and the work? Yeah, I, I mean, it's, it's always difficult. I mean, again, so women generally have the caretaking duties fall, generally fall, fall on women.

Um, that's not always the case. Um, you know, and so, you know, those, the, there are struggles around the, that work-life balance, but the remote hybrid I think really enables, um, you know, individuals and women to have that flexibility, um, and to be able to do the work that they need to do at different hours and not always be, um, you know, an eight to five type of thing. So, um, I mean, I wish, you know, you know, people with children, and I would say that eight to five, you know, is, is doable for most, but it isn't, um, you know, in reality.

So, so I think, you know, that flexibility in how, and that hybrid really is very important. And I think, um, you know, some of the mandates that employers are giving around, um, being in the office on Tuesdays and Thursdays, I think that is adversely impacting women because, you know, I think they wanna be able to have the opportunity to come into the office, um, when they can be in the office and what works for them within their lives. So, so, um, I think those mandates are, are, um, adversely impacting, um, a lot of women.

And, and hopefully people will look at it as, um, encouraging more women to come into the office, but not the, those mandates. Mm-Hmm. We were just having this debate on the Textron gang, which is another show that we do.

Um, and I think Amanda was involved in this, and we were trying to work out the nuances of, well, you're working remote, but you're kinda have different hours and different times, and it's hard to sync up and kind of get everybody on the same page at the same time when we're all talking about the same thing. On the other hand, um, if we force everybody to live in San Francisco and New York, we limit the scope of the jobs that are available, and it's probably bad for the economy, and it's bad for everybody in general because we're not, uh, being as inclusive as we might. Um, so how do you kind of strike a balance between those things where we're, we're trying to stay engaged with each other, but at the same time we don't want to, you know, require everybody to work within a, you know, two hour commute to the office?

Yeah, I mean, so, you know, I mentioned at the beginning we're a global workforce. Um, you know, half of our workforce is in India, a portion is in Europe, um, portion in us. Um, I think, you know, that said, most companies are going towards those global, and, you know, you find those times where you connect and where it's important and you prioritize, you prioritize those meetings, you prioritize these, um, you know, teams meetings, these zoom meetings to make sure that everybody's on the same page to, to mandate.

People near offices are also excluding, if you look at the global workforce, you know, huge groups of people. So you know that India workforce, they tend to work late so that we in the US can get up early and Europe can be, you know, in their afternoon. So we can all collaborate equally.

So I think it's just setting those expectations around what's important and, and the timeline of, you know, those hours that you have to be available and ensuring that, that you work around that. I think most people, um, would agree that, um, you know, they can make that work within their schedules as long as they know in advance. Mm-Hmm.

What did women have to say as far as the overall work culture? I know there was some information in there about the culture. Yeah, I think, um, you know, overall, um, there was good news around how I think that women are feeling, um, more connected in the, in the workplace.

And, um, things are really improving. Um, you know, I think, you know, that said, um, you know, some of the feedback was that there's still some areas of improvement that need to, to occur. I think there was some still some feeling around, um, microaggressions and, and some sort of, and some, um, limited discrimination.

And, and I think, you know, again, it's around developing our leadership. It's developing the, the, the culture in the organizations to, to, you know, um, break down those barriers. And, but, um, you know, overall, um, based on where we were, um, you know, a few years back where when we did the first survey to where we are today, I think that most, um, women kind of expressed that, that they saw some po a lot of positive improvements, which was great.

Mm-Hmm. Is it your sense that, you know, that's a marginal improvement or is it your sense that we, you still got a long way to go? Or where are we on this journey before, before the guys run around and declare victory?

Yeah. No, no, no. I would never declare victory.

So, you know, and it was only 1500 women, so, uh, it was a sampling. But, um, I mean, I think it's great though that we're starting to see improvement overall. Um, so, you know, where we were, you know, even 10 years ago, even before we started doing the survey, I think women would, would, um, say that we've made significant improvement.

But I still think that there's a long way to go to, to true equality within the organization. Um, you know, and a lot of that too comes from, you know, if you look at the tech sector, there's not a lot of women still in leadership roles. And, you know, true equality comes from having, you know, um, equal parity, um, throughout an organization.

And so there is a lot of work that still needs to be done. Um, but, but that said, you know, there's been improvement. And so I'm really happy with what I've seen and heard.

Wonderful. And I do believe the survey did show that there are more women entering into the, the tech sector. Is that correct?

Yes, yes. So there's definitely, um, more women entering the tech sector. So we definitely, um, and even in an inno, um, we've seen a lot of, um, you know, women joining where we're getting into an equal parity situation at those, those, those junior levels.

Um, but again, still a lot of work to, to be had, um, particularly in the senior levels. Do these issues also apply to other forms of inclusion that we're trying to achieve, whether it's LBGQ or, um, it's just simply people of different economic backgrounds. I mean, um, are we making progress across the board or is it just that we've been focused on this one area, but we still got a long way to go in other areas?

Uh, no, I, I think we've made progress in a lot of areas, um, just because there's such a strong awareness around, um, bringing inclusion into the workplace, bringing equality, you know, um, every study that you read, um, you know, the more diverse you are in an organization, the stronger you are. Um, you know, the, the, the greater revenues, the stronger organizations, um, because you just need those different voices. Um, you know, bringing in people that are all from the, you know, same, uh, background and, you know, same age, you know, kind of, you know, grew up the same.

Uh, you know, that doesn't lead to a lot of diversity of thought. And I think we need that diversity of thought. Um, so, so, so there's been progress.

It's just there needs to be a lot more and, you know, our survey focused on women. Um, but, but I agree with you, you know, there needs to be a lot more just around the ethnic side, um, you know, and then also, you know, lgbtq plus. So, And I think the, the last, uh, segment of the survey shared a little bit about what women value as far as benefits, uh, from their work.

And so what were the most valued benefits from the workplace? Yeah, I think there was, um, you know, there was a lot around, um, you know, um, skills enhancement and development. Um, so, so making sure that there's those career paths, um, were very important.

I think the flexibility was noted. Um, and I think there was a lot of, um, you know, just information just around also being part of decision making, um, and being able to, to, to, you know, decide where the organization is going. And, you know, you know, going back to, um, the beginning of the topic around AI and women in tech, you know, I think a lot of women are interested in that because, um, they wanna be part of that decision making process.

Like, what is en o going to be doing? What is my company going be to be doing with ai? How can I assist?

How can I enable, how can I be part of that? Um, and so, you know, enabling women to, to, you know, lead the organization and help, you know, move the organization, um, and directionally is very important. Um, is, is what we read in that survey.

What's your sense of the political climate these days? 'cause of course, it is an election year, and we have a lot of different things happening in different states. We're in Florida, and of course, you know, as some of this is a regular conversation here, um, but it's everywhere, right?

The Republic of Ireland just had a referendum that changed some of the wording in their constitution about what defines a homemaker, and apparently not enough people showed up, so it went down in flames and, uh, much to the surprise of the political leadership. But it seems like it's a global conversation, and I wonder, does it matter what the government thinks, or should each company just do what they think is right? Uh, from what perspective?

I guess let's, with a little bit of clarity, that's a, that's an open, open question. Very different paths. Where, where are you going?

It's just a sense of, um, you know, should companies be worried or be paying attention to these various legislative initiatives that are floating around? Or should they just kind of focus on doing the right thing by what's good for their organization? And, um, maybe there's too much noise in the system.

Uh, no. I mean, I think, um, you know, we, we are heavily in focused on legislation that impacts, um, our associates and how it impacts our associates, because that impacts how they do their work and what they're focused on. And so, um, you know, monitoring what's going on, um, within, you know, where we do business is very important, um, so that we can address concerns that come up from it.

There's, there's a lot of divisiveness, um, you know, in, in the world today. Um, and that does impact people. And so, um, you know, we need to do the right thing, but we also need to support, um, all of the people that work for us in these, you know, kind of volatile times in.

So, um, you know, if necessary, you know, we, um, as in Sono, I'm sure would, would get involved in an initiative if we thought it was gonna have a huge impact on our associates, um, and, and the way we do business. Um, you know, at this point we haven't got to that point. Um, but I think it's something that, you know, you know, organizations and leaders just need to be concerned about because it does have a huge impact on, on how people, um, live their day-to-day lives.

Wonderful. So I think we have covered most all the sections of the survey. So if there was one key takeaway that you could leave our business leaders with today, what would that be?

I, I think you just really need to focus on, um, not only women in the organization, um, but just the, the, the minorities, um, in stem, you know, in technology, and ensure that you're bringing them along. I, you know, I said it before, diversity of thought, um, is, is very important. And that diversity of thought comes from people with diverse backgrounds.

Um, and, you know, and gender and ethnicity, um, you know, is very important and you should focus on it, um, you know, and, and continue to develop. Because as I said, you know, we've made great strides. Um, I, I, when I started long ago, I won't tell you how long ago, uh, you know, it was a very different world.

You know, I'm, I can say I'm very proud of where we are today, but, but we still need to do more. So, so continue, keep the focus, continue to support, listen, um, and, you know, just do the right thing and, and help enable, um, you know, those, those diverse, um, associate pools to, you know, come on board into your organization and, and really make a difference. All right, folks, well, we hope you're listening to this podcast 'cause that's part of listening, right?

So there you go. Absolutely. Well, thank you again for coming on our show, and thank you to our audience for tuning in, and we'll be here next week.

Great. Thank you. This is Textron tv.

Hey everybody. Mitch Ashley here at Kku Con in Paris 2024. Taking over a little bit for Alan.

He's been doing some interviews and I'm jumping in and meeting with some fantastic people, one of which is Cheryl Hung, who's with ARM Work. Welcome, Cheryl. Thank you so much.

It's fantastic to be here. Good to have you. Tell us about what you do at arm.

So, at Arm I'm the senior director of ecosystem within the infrastructure line of business. Mm. So that means arm in data centers, cloud, telco, networking, That, and that's really grown.

I mean, I, I see more and more ARM in the data center, arm in the cloud. It's, it's the fastest growing part of arm. ARM is very dominant in mobile phones and mobile devices already, but Cloud and of course, ai, the whole topic of this, this conference, it feels, um, are huge drivers for arm.

Very good. So I'm really happy to be here. I'm curious, uh, so you, you have some connections back though into CNCF before you were with arm, right?

Indeed, indeed. I actually used to work for CNCF, uh, a few years ago. Um, I used to lead the end user community as the VP of Ecosystem in CNTF.

So I care a lot about, you know, not just good technology, but the community, making sure that it's very easily accessible and very usable. Um, and supporting people from whatever big or small organizations or individuals to come and be part of the community. It's nice to have that continuity when you move to ARM and bring in all those relationships and that knowledge, that experience with you.

It is definitely, like, one of the things I love about this community is we're all friends here. You know, it doesn't matter where you work, doesn't matter what you are doing today, but the, the relationships that people form here have lasted, you know, beyond what their company's doing. One company and, you know, they really maintain that every coupon.

It's great. At least one person says that to me. Yeah.

You know, so I think it is really true. It is a community. I mean, the first coupon that I came to was Berlin in 2017.

I'm gonna say it was 1500 people. I was here on a diversity scholarship, so I knew nobody, you know, much smaller conference, obviously a lot of energy and buzz, but at the time you're still like, oh, Kubernetes and maybe a handful of other things. Yeah.

Now you look at it, look at this. Yeah. It's Kubernetes conference to really being cloud native Ommunity.

Yeah. And now Kubernetes is so established and, you know, solid. And that at the time it was still like, oh, is Kubernetes even gonna be a real thing?

So I'm very, very ratified, you know, to see how this community's grown. Well, so three things, three things I'd love to talk with you about. Love to hear more about how ARM is doing in the cloud.

'cause I, more and more I hear those kind of conversations happen on the infrastructure side. Um, open source project adoption of using arm, and then also of course ai. You know, we, you can't go five minutes into a conversation without talking about ai, but very legitimately, I mean, with ARM and AI projects.

So choose the order. You can talk about whatever you want. Let's, um, let's go from, let's go from the top.

Okay. So, um, last coupon Oracle announced that it was offering millions of dollars in cloud cloud credits for armed servers. So offering these two CNCF projects.

Um, and the goal here is really to enable c ncf F projects to experiment with arm servers to test, to deploy. And the goal is really for all of the projects, eventually to be multi architecture. So independent of what your end users are using, you know, today you should be neutral towards what architecture and you should try and support a broader range of end users as you can.

Mm-Hmm. So this announcement that we made with Oracle was really a way for us to show that this is something that we want our community to support and to take advantage of. So part of my goal of being here today is to talk to people and tell them like, oh, what are you doing with these cloud credits?

Can we help you? Can we do anything for you to make it easier for you to access those? Um, so yeah, that's, that's one of my main, so, So how's it gone since the announcement?

How, how's that adoption happening? What do you see happening with things? It's picking up.

It's picking up, yeah. Yeah. The, the challenge of course is um, is obviously, uh, smaller player.

Mm-Hmm. So far in the cloud native space, it's this Thing called momentum too, right. You know, You momentum Exactly, yeah.

Gathering, you're gathering, uh, more momentum as You do this. Exactly. And that actually takes me to the second topic you mentioned, the open source adoption.

We are very, very proud to say that more than 80% of the graduated projects support arm, that is huge. That is not where we were three years ago, and we would love to get that to a hundred percent. So the maintainers, the TOC, you know, we are really talking to members of the community and how we can get that number to a hundred percent.

Um, part of that, you know, supporting through things like cloud credits, part of it's supporting through supporting the maintainers and the community directly. Um, but it's a significant achievement. Never in mind.

ARM is not a software company, right? Right. Yeah.

A few years ago, ARM is like purely in chips and computer hardware. So for ARM to actually make the stance and say, you know what? The software matters, and when it comes to software, open source software really matters.

That is a big change for arm, a big cultural mind shift. Um, so I'm very, very proud of the company for that. Let me ask you then, so is it, is it just, is it, I'm gonna be simple, simple answer.

You tell me if it's the simple, I'm, I'm guessing it's not. Is it just getting projects to distribute their binaries in ARM and Intel and whatever, you know, chip formats? Or are there more things about how you incorporate design into your software to take advantage of different use cases for arm?

So, both. Both, I would say, um, on one hand, moving Recompiling to Arm is actually very simple. Um, arms put a lot of effort into making the compilers, you know, you basically flip a switch and in theory it should all work.

Of course, that, you know, last little bit is where all the effort is to make it all just work. There's something, anything like that. Sure.

Um, so there is a lot of work still left in terms of testing and making sure that software works together in the way you would expect it to. Mm-Hmm. Um, so, you know, I'm not saying that it's an easy task.

There's definitely still work to do there. And the second part is on particular workloads. So of course the, the hot topic of the day is ai, and AI is driving a lot of the demand for arm, um, and arm in the cloud.

In fact, I gave a keynote a different conference, um, last week where I said, if you're not on the AI hype train, you need to get on it now. You know, I was a little bit of a cynic myself, but, you know, seeing the momentum here, seeing how, seeing how AI is really being used to solve these problems that would've been too difficult or too expensive before. Um, it just goes to show, you know, that's where the, that's where the hype train is.

And obviously, um, you know, in collaboration with some other players like Nvidia is a big part of that. So we are doing our best to keep pushing, keep supporting that, um, community, and to democratize the access to GPUs and make it easier for people to use them and run them in production. Great.

Are there, are there certain things, do you need to work with different LLM providers to help them work in an ARM environment? Is that important at all, or that's kind of a little, a little higher up in the, in the stack. That's just not as big issue.

We, a surprising amount of it just works. You know, a lot of it is really, really, um, you know, just works. And that's really a tribute to all of the closed and open source, you know, engineers who've been working on this, um, over the last few years.

There are obviously some things, not so much in, you know, the data, but in the training and the influence that, you know, map need, uh, need some work and need some polish. But honestly, honestly, the, the biggest challenge right now is all of the layers above it. So things like Kubernetes, we saw from the keynotes today that actually there's still a lot of challenges in how you manage those GPUs and share those resources effectively and fairly and, you know, cost effectively from a business side, um, and keep your users happy because obviously they want, you know, the fastest access possible.

And so there's an absolute ton of those. And I say the other big bit that we are missing right now is actually in the ecosystem. You know, we don't have necessarily, the talents policies are changing really fast.

Mm-Hmm. Governments are figuring out what to do about this whole AI thing. Um, and the hardware is still difficult to access, very Expensive early on parts of it, it seems like.

Exactly. I mean, ai, ml's been around for a while, but especially journey of AI is all do most of us. You know, it's, it's, it's been around, but it's been around for the very big tech companies.

Right. You have the deep pockets. If we're gonna get that out to the widest range of people and companies possible, then we need all these things.

We need the money, we need the talent, we need policies to support it, and we need the hardware to be accessible as well. So we definitely have a long way to go, but it's really great to be at the beginning of the journey and, you know, see where we land in five years. Yeah.

You do kind of have to be there at the start, you know, to really be part of the whole ecosystem. How, how about, I'm curious, you know, there's a lot of discussion about, uh, demos, domain specific LLMs, um, geographically based LLMs that are at the edge. Now we're talking about just gen AI at the moment.

It seems like those are, you know, really particularly great use cases for ARM as well as, you know, more efficient user resources, sustainability Yeah. As a big issue around ai and it's, it's, it's kind of elevating sustainability even more as a topic than it already is. Yeah, 100%.

Um, ARM has always had this focus on, um, being energy efficient right from the beginning. That was, you know, the goal was these very low energy chips and that served it very well in mobile devices where, you know, you wanna have your battery life for your phone last all day, but turns out that's also wonderful if you have massive data centers and you are using an enormous amount of, of energy Mm-Hmm. Then using that energy effectively is super important.

So many businesses, you know, almost every business I'm talking to has sustainability as one of its long-term goals. And one of the best ways to do that is to make sure that you're using your energy and, you know, it's also good for the company, right? Because you energy costs money, you're spending a lot on your cloud resources, so you wanna get the most out them.

There's good, good for the company, good for the planet, and good for people. Excellent. Yeah.

Well, uh, so wrap up with kind of more of a, a you question as a veteran, coming back to Kub Con, having gone multiple years, multiple events, is there any particular meetup or group or event or part of like, is the top of the list of, I always show up at the Wednesday, whatever, you know, is there something like that for you that you really enjoy participating in the most? Um, I would say on a personal level, um, I've run the cloud Native London Meetup. I've run it since 2017, so gosh, seven years now.

I've been running this meetup. We have over 8,000 members, and I always love to meet the meetup organizers from different countries. Mm-Hmm.

So, you know, the one in Paris, the one in the Nordics, you know, the one all over. And I, I love meeting up with these folks because they're always really passionate about getting people together, you know, learning new tech, sharing what they know. So for me, I think getting in touch with the amba other ambassadors is key.

And then, yeah, just hanging out with people, you know. That's great. I think it's always good to meet people you don't know as well.

It's, you meet people you don't know and, and you know, it's, we're covid somewhat in the rear view mirror for, for us, but I'm still meeting people. I've only met online and like, yeah, just, just someone walked up today and like, Hey, I finally get to meet you in person. We're still making that personal connection.

Just two minutes ago I saw someone I knew and I was like, oh, after this is done, I might have to catch up. I gotta go Run to catch them. Exactly.

Well, it's been fantastic talking with you, Cheryl, and, uh, we use you the best and I'm excited what ar what ARM is doing in the industry and the adoption level. You know, I have one more question if I could ask You. Yeah, go for it.

I have this theory, I don't know how big of an impact it has, you know, being developer in my background and, and long, long term Apple users, when Apple moved over to an arm style chip, at least that instruction set, it seemed like that kind of brought a whole new developer community into the arm world needing ARM software, all the things you're talking about of course. Which helps when you move it into the cloud and into the data center. So it seems like moves like that where developers are working and the software they're working on is seems, seems like one of the things that will help accelerate that.

Is that true? It is very true. It's, it's actually a little bit embarrassing how often I hear, you know, oh, I moved my company to Arm because I wanted to use my Mac with my fancy new M two.

Hey, that's what gets them there, right? I'm Like, sure, yes. You know, that's, that's great.

Um, I actually used to work for Apple before Arms, so I've also seen that from both sides. Um, but you know, it just goes to show that, you know, arms growth is everywhere and it's wonderful and it also really goes to show how developers are leading the charge. Mm-Hmm.

You know, a lot of things, you know, like, yeah, okay. Sustainability might not be something the engineers care about day to day, but they care about their battery life. So we're all heading in the same direction.

Excellent. Well, Cheryl's been a lot of fun. Cheryl Hung, who's with Arm, you wanna know something about what's happening in the cloud or CNCF or Apple or Arm.

Not to, not to overload too many expectations on you. It's been great talking with you. It's Been wonder appreciate coming by.

Been wonderful talking to you. Thank you so much, Mitch. You too.

Have A great show. Thank you everybody. We will be right back with our next fantastic guest, just like Cheryl and I hope you stay tuned.

We're just getting started here at Kon in Paris. See you in a minute. I'm Bonnie Schneider, sustainability contributor to the Techstrong Group.

I'm excited to introduce you to a groundbreaking new initiative from Techstrong Research, the sustainability pulse meter. The pulse meter offers valuable insights into how environmental responsibility factors into tech purchasing decisions for key players in the industry. Position your company as a leader in the industry and differentiate from your competitors with a sustainability pulse meter offered exclusively from Techstrong Research.

How about some views with Bazaar? We got two of them for you today. And this first one, Mike speaks, uh, with Aerospike, CEO Suebu IER about an additional $109 million in funding Aerospike picked up and why a larger shift to real-time applications is driving the need for multimodal databases.

This is Techron tv. Hey guys, thanks for the thrower. We're here with Sabu Wire, who is CEO for Aerospike, and they're fresh off of a investment of $109 million.

That's on top of what they previously have managed to recruit from folks, but we're gonna talk about what's driving that, because they're a pretty big player now in this whole space of graph databases and vector databases, and they're used for slightly different things. And how all this is coming together. Sue.

Boom, welcome to the show. Thank you, Mike. A pleasure.

What is your assessment of what's driving all this? I mean, on the one hand, it seems like vector databases are closely tied now to the boom and AI graph databases may be tied more towards, uh, knowledge graphs and visualizations, but it feels like both are being lifted. Are the two related, or are they different use cases, or how does this all come together in your mind?

So, Mike, before I answer that question, just a little bit of a background. So, you know, what we see is, you know, real time access to data and real-time decisioning is actually now prevalent within every industry. You know, Aerospike was founded on the premise of really making, you know, real-time data accessible at high performance at, with any scale of data, right?

We started our journey with Ad Tech, but now we have customers in every industry, financial services, telco, e-commerce, retail, gaming, and entertainment, healthcare. So really what we have seen is the, you know, the adoption and the desire to harness data at any scale in real time. And, you know, we've been deployed in, in a lot of customers gro globally already for several years within their AI and ML pipelines.

So, you know, as you mentioned, obviously Vector has become a topic of conversation ever since, you know, chat. GPT became available last year. Uh, but we've been in deployed in customers across their AI and ML use cases, whether they're fraud analysis, recommendation engines, uh, profile stores, so on and so forth, uh, for the last several years.

So what we see out there is really, you know, a desire to actually act on a lot of data. So if you think about ai, I think AI begets more data because what we hear repeatedly from our customers is that more data actually drives better context and better accuracy to their AI systems and their AI results. We were built from day one to handle data at scale while delivering real-time performance on that data.

So we are, we believe AI is a perfectly suited application for us, if I can call it that. And, you know, know we were, we were ready for the AI age. It's just that, you know, over the last year, as you talked about, there's a lot, lot more, you know, desire to actually implement AI within systems.

Mm-hmm. As far as graph is concerned. Uh, the second part of your question, we entered the graph market last year, and, you know, we are, as you correctly pointed out, a knowledge graph that supports both use cases like identity management and fraud.

And, you know, we saw a, you know, opportunity in the market, again, based on customer feedback, who wanted to actually deploy graph at scale. So the existing solutions that were available for graph, uh, really didn't scale much. Uh, so we came out with a solution which can, you know, support billions of vertices and trillions of edges.

Um, so this is the, you know, class of kind of graphs that we are supporting for our customers. And we've got a go a lot of traction. It's not just the scale, but that size of a graph we can traverse on a multi hop query in, you know, single milliseconds.

That's amazing. You know, when we talk to our customers and we, when we see the market out there with ai, you know, and the desire to actually use more vectors to generate those embeddings, and whether it's within generative AI or predictive AI use cases, actually leverage vectors. We are seeing that happen, uh, as far as the adoption of vector and vector search is concerned.

But we also believe there's a congruence, if you will, where graph and vector can exist independently to power some of these use cases, and they can also come along. So if we talk about a simple example, if you're trying to look for a specific document, let's just say using a vector search, a a vector kind of search with vector embeddings is probably the right way to go. But if you're looking for documents which are similar in nature, for example, which is, you know, for lack of a a word, let's just say it's a corpus of similar documents, then you want to use a vector search, which is then augmented with a graph so that, you know, the graph can then actually build relationships and build our similar documents that are available.

So that's how we see this entire field of vector and graph coming together and evolving over the next several years. There's much to unpack there. So let's jump in.

Let's start with real time. So we are clearly seeing that there is more data being processed and analyzed at the point where it is created and consumed, but then it needs to be fed back to something else in near real time. Is this changing the way we think about building our applications?

Because historically everything was very much batch oriented. So, you know, do we have the developer skills to do that? Where are we on this journey?

Absolutely. I mean, you know, some of our, uh, leading edge customers are already doing that. As I mentioned, uh, before, you know, we've been deployed in AI ML pipelines for several years across, you know, uh, our global set of customers.

Um, what we hear repeatedly from prospects and customers we talk to Mike, is the desire to actually use more data in their AI or other kind of applications. And the limiting factor for them is one of two things. One is really scale, which is, you know, they look at systems and solutions, which actually hit a scale wall.

And the second is really the cost. So it becomes very prohibitively expensive for them to actually keep all that data online, if I will. So we actually, once they talk to us and they discover, you know, the art of the possible with Aerospike in that aerospike and support this infinite scale, whether that's hundreds of gigabytes or even petabytes of data without compromising performance, and at A TCO that is the lowest in the industry, right?

We run on hardware footprint, which is 80% less than anything else that is out there now, that actually opens up a lot more possibilities for them. And as we kind of get into more and more AI applications being built for more context, better accuracy, as I mentioned, they're gonna want, you know, all that data to be available as quickly as possible. You know, we, we are talking to customers right now who are telling us, Hey, I'm only keeping 20% of my data online because, you know, these same things.

Because it, you know, my platform doesn't scale or it's pretty expensive, but if I have a solution, I would love to keep, you know, if not a hundred percent, at least 80% of my data online. And then it's not static dataset, right? It's also coming in where your ingesting data, let's just say in the case of vectors, there's vectors which are coming in at a rapid rate.

So can the platform actually support, you know, high throughput ingestion and then make it available, build the index of the vector as this new data comes in and have it available to feed the AI application or the decisioning systems? These are the kind of, you know, key kind of considerations that our customers have. So, to your question, absolutely, if they could find systems like Aerospike, which can actually help them process data online in real time, they would do a lot more online.

And, you know, several of our customers have done exactly that. They've gone from nightly kind of decisioning to first going kind of, you know, several hours a day. And then now they're, you know, some of our customers are doing it every few minutes.

That's just changed the trajectory on their business. Do we have the skills to manage those types of databases? 'cause historically we've had relational databases and then document databases.

Um, a lot of folks I talked to, you know, vector databases have been around for a while, but until AI came along, they had no idea what a vector database was. Do we have the folks that required to manage that? Is that the, a new type of dba?

Is it a data engineering team? Where are the skills? Um, so, you know, skills, uh, we see, you know, two, two kind of, um, kind of areas of, of investment here.

One is, you know, larger organizations do seem to have the talent and the skills, and then other organizations rely on partners or look to us for the expertise so that they can actually onboard and train their workforce, uh, around these skill sets. Along with that, you know, it's our job as vendors and we are investing a lot into this, which is making it easier to manage these databases and also help the developers build easily. So let me give you two examples.

We are investing a lot in observability and management. So as you know, more data comes online and is managed under Aerospike. We wanna make it easy to really manage that database, you know, whether you're scaling up, scaling out, you know, whether you are trying to understand if something actually pops up as an issue, we should be able to be proactive, um, in, in terms of alerting the, uh, user, uh, so that they can take the appropriate action.

So that's one area that we can make life simpler for the operators, if I can kind of call it that. The second part is really the builders and the developers. So we are investing a lot in terms of, you know, making it easier for developers to find resources.

So we built out a developer hub last year. com, there's tutorials, there's sandbox sample applications. We have a community forum, which is actively actually responded to by our team.

And then we also are making it easier from an API perspective, so more simplified APIs, you know, using frameworks like Spring data if you're a Java developer, uh, using Link Pad if you're a T net developer. So that, you know, our graph solution supports gremlin as a query language. So we are trying to get it closer to the language of the developer and have simpler APIs.

So for both, for the builder and for the operator, our mission is to make life easier. And, you know, as simple as possible, Might we not use generative AI to make it simpler to manage the databases that we're using to drive ai? Absolutely.

So, you know, we are investing in efforts, uh, exactly to your point so that, um, you know, we don't, you know, we want to give the operator the control, whether they want, you know, decisions to be made automatically on their behalf or based on certain policies, or we just want human intervention so that we alert the human. But you're absolutely right. I think, you know, part of kind of what we are gonna invest and build out is exactly what you talked about, so that there is, you know, uh, self remediation in certain cases and or alerting, which is propped by ai, uh, which is, you know, built into the product.

How do you see the way IT teams are organized kind of evolving? And I'm asking the question because in this day and age, we'll see data scientists hanging out with developers and DevOps, and then I've gotta talk to A DBA and there's a data engineering folk, and there's probably a couple of cybersecurity people throwing into the mix as well. Um, is there a different way of thinking about managing all those things more cohesively?

'cause right now we just had a lot of fiefdoms over the years. Um, we see two models evolving there. You know, some of our customers actually have what they call center of excellence and a shared services team that stands up and manages kind of the database and infrastructure services and ha makes them available, uh, internally within the lines of business and through the developers.

Um, other organizations, maybe, you know, medium sized to smaller sized organizations have, you know, all of that actually built together into a singular app team, if you'll, so the person who owns the app owns not just the build face of it, but also the deploy and the operate and manage phases of it. Maybe different people and different roles, but it's all part of the same team. So we see two models kind of, um, emerging, um, where, you know, when, when you have multiple lines of businesses, you know, larger organizations, they tend to go for a center of excellence or a shared services model.

On the infrastructure side, You guys have garnered this latest investment round. Are there any priorities for that kind of allegation here? I mean, what's top of mind for you?

Yeah, so I mean, I think we've been, um, you know, incredibly lucky Mike to actually attract, um, you know, attention and, you know, requests from a bunch of, uh, investors wanting to actually join us in the next part of our journey here. Uh, we decided to go with sumero. Uh, we love the team.

They got exactly what we are trying to do and our vision for the future. Um, so, you know, really thrilled to work with the Sumero team. George Kfa, uh, who's, who's been, you know, in the industry for a long time, has joined our board from the Sumer team.

He is a co-founder and managing director. So, you know, personally, I'm thrilled, uh, to be working with, uh, George. Um, the core areas of investment are r and d, primarily around ai.

So we talked about Vector, we talked about graph. We wanna make it where the Vector database and the vector search from Aerospike is natively integrated to the workflow automation and the, you know, models that are available on the different cloud vendors, whether it's A-W-S-G-C-P or uh, uh, Azure, uh, so that, you know, our customers don't have to stitch these things together on their own. And, you know, it's seamlessly integrated.

So there is a lot more investment around ai. Uh, the second area is cloud. We released, uh, our database as a service product last year in Q4, and we continue to build out our entire cloud portfolio.

So, you know, we are investing in that, um, the, and the outside of r and d, um, you know, it's go to market. So, you know, we are trying to expand our go-to market efforts, both in term of direct sales, um, and also channel, uh, so that we have more reach and coverage across the globe. So, you know, r and d and go to market are the two primary, um, areas of investment.

Seems like there's this ongoing debate about whether or not I need a dedicated database for a particular use case, or if I can extend, say, my existing, uh, relational database to other data types. And we've seen that going on over the years and, and it's now playing out again, especially in Vector. What is your sense of, you know, when do I need standalone?

When can I use something that I'm extending or, or are there legitimate use cases for both? Or is there one or the other? So I truly believe that, you know, the world is gonna go towards a multi-model database.

That's what, you know, we have built out and we already always strive to actually build out. We started with key value, we added support for documents, then we added support for graph, and now we added support for Vectors. Um, the reason is, you know, one of the things is what you mentioned, which is skillset.

How many different database technologies, you know, can a particular developer or your team learn at some point in time? You know, you wanna make sure that you're able to leverage the skills across a broad set of use cases. Whether that use case demands that you use a vector or a graph or a document data type under the covers, I think that's gonna be an important consideration.

Uh, you know, the entire database and data field has been pretty dynamic and, you know, obviously, uh, folks have chosen, uh, the best, uh, or what they consider the best database for the particular use case. And that's why you have such a dynamic ecosystem of vendors, uh, that have evolved. Um, but I think if you can deliver the core value propositions that I talked about, which we believe we do, uh, with real time performance, you know, low latency availability, and you know, lowest TCO that's actually applicable to a bunch of different use cases.

And, you know, data model happens to be just something that supports a particular use case. Uh, so, you know, we think the world is gonna get to at, at a point in time where they will have a fewer set of databases and they'll build a bunch of different use cases on a multi-model database. Whether, you know, SQL and No SQL come together, you know, I don't know, they seem to be two different world right now in terms of what the sql, uh, databases are doing.

And, and kind of the no SQL databases is where the energy and the growth is for the most part. So what's your best advice to folks? What's that one thing you see them doing these days that just makes you shake your head a little bit and go, folks, we need to be a little bit smarter than that.

I think, you know, um, what I see out there, Mike, is, you know, we tend to resort to a path of lease resistance. So I would encourage people to do a little more research because before you choose a platform or a particular solution, you know, look at the alternatives, look at what's available and what's gonna stand with you for the next 18 months, for the next 24 months of your journey. 'cause replatforming is gonna be very, very expensive for most folks.

So if you start your journey with a solution, which may look okay for you right now, as you grow, and you start kind of, you know, leveraging more and more aspects of that particular solution, you'll see that you'll start hitting a wall either on a data wall, a scale wall, rather, I would say, or a performance kind of, uh, issue or a cost issue, which is it starts getting prohibitively expensive. So do your research, uh, before you actually start that journey so that you look at a particular solution or a platform or an infrastructure can, that can stand its time for the next several years. Uh, you don't wanna be replatforming in 18 months or, you know, 24 months.

So that's kind of the, you know, big advice that I would actually put in front of, uh, folks. All right, folks, you heard it here. There's an old saying that says, you know, you date your hardware vendor, you marry your software vendor, and it's still true.

Hey, bu thanks for being on the show. Great. Uh, okay, once again, great catching up again, Mike.

Thank you so much. All right. And back to you guys in the studio In our second few with vard today.

Mike speaks with Lee Foss global field CTO for GitLab, and they're gonna delve into the complexities of finops, finops, shedding light on the challenges of managing cloud cost effectively. This is techron tv. Hey guys, thanks for throw.

We're here with Lee faus, who's global field CTO for GitLab, and we're talking about finops. It's all the rage these days, but it's not clear everybody knows how to do it. Hey, Lee, welcome to show.

Thank you so much, Mike. Appreciate being here. What do you think is going on when finops, because I remember back in the day we had capacity planning and there used to be finance teams, and IT folks would walk around and figure out how much they were gonna spend almost out of the penny, and it was a fairly exact science, and then the cloud came along and did we just forget or, or we kind of relearning some skills that we lost sign?

I think it's a combination of both. Um, one of the items that we see from our customers is there's two different buying models that you have for products and services. So when they're on the public cloud, they're learning how to adjust to things like commits and consumption based pricing for some things.

So if I've got a database and I've got a bunch of storage that needs to be to back that, that storage is something that they get charged on a month by month basis, where you then have other products that charge by user. So the procurement teams are trying to figure out, how do I blend these two models to make sure that I'm meeting the financial targets that they have internally while still capturing things like return on investment and total cost of ownership. Do you think folks are being surprised as they do that?

Are they spending more money than they initially thought? I mean, I'm kind of liking it to my television set where I have all these different apps that I'm subscribing to now, and I'm probably spending more money on television than ever. Yeah, I agree with that.

I, I run into that same situation with, uh, uh, two girls in the house, and very surprised at the end of the month when you get the bill and you start to total things up. And, um, I think that is something that's running with, uh, our customers today is, um, they have targets that they want to meet for their budgets, for the applications that they're building and deploying. And those applications, when they become wildly successful, um, they're not targeting for what are the additional services that they need to, uh, generate budget for.

So items that I, a lot of customers are surprised on are things like egress costs and being able to move stuff between a data center and into the public cloud. These are things that you normally wouldn't have to budget for if you were just targeting your own data center. So there are some surprise costs that do pop up, but at the same time, um, as we look at what the overall lifecycle looks like of an application, they only target things like, what does the production infrastructure look like?

And now that we have these ephemeral environments that we can spin up and tear down, and we're not tied to the old legacy hardware that we used to have in our data center, even with virtualization technologies, we never felt really comfortable just tearing things down, spinning up new environments. And as they're doing that, they have a lot more flexibility in how they do things in dev test, a lot more performance testing. They're able to run through their regression tests.

Uh, there's just so much more that they're able to do that they just weren't able to do before. And that's where some of these surprise costs are coming in. There's a lot of different models in the cloud, um, and you hear a lot about spot pricing, but I, I also talk to folks and a lot of 'em don't use that simply because it's a lot of work to figure out exactly what the optimization is for a particular use case at a given time of day.

Do we need another way of thinking about kind of capacity on demand versus an application that runs more consistently? I mean, is the model kind of broken? Yeah.

I remember back when, um, I first started building applications and running my, uh, engineering teams. You know, one of the things that we did is we had profiles for applications that we were building. Are they memory intensive?

Are they CPU intensive? Are they IO intensive? Is there a blend or a mix?

How do we go ahead and target the correct infrastructure for the correct applications? And now a lot of people look at things inside the public cloud is just general compute type resources. So I think as the cloud is evolving, we're starting to see more and more customers treat it as a platform rather than just infrastructure services.

So they're able to get an economy of scale in the cloud by leveraging services that they would normally have to build themselves. So they're sort of hiding what some of the infrastructure costs look like underneath, because they're getting the value that actually sits on top of those resources internally. Can I go after this holistically, or am I gonna kind of two step it where I need to, uh, optimize the cloud spend by category?

So it might be, in your case, a DevOps platform, but then I've got storage, and to your point earlier, there's a lot of different models. And then I gotta aggregate all that into some sort of more comprehensive analysis that lets me get to my total cost. Is that kind of the, the, the flow and the skills required here?

Or how does this play out? Yeah, it's, um, interesting because one of the things that we're asked for from our customers are wanting to know things like when they're running CI jobs. So when they're running those, they wanna know how many minutes a project is consuming of those resources to try to do some sort of chargeback showback type model inside across, um, application owners.

So things that we see in the future is when we start to look at those things holistically, we've gotta move away from just talking about minutes. And we need to start thinking about the value that these services provide. And that's where a lot of the customers, when we talk about value, it needs to be, unfortunately for them, there is some vendor tie in, but what are the specific services that you can consume that makes it easier for your users to monitor their applications, to be able to do auto scaling, to be able to, uh, uh, do things like DNS and auto failover and being able to do hr, uh, ha dr um, inside their environments for their applications.

And as they start to abstract those things away, they're now no longer needing to worry about, well, how do I provision a virtual machine? Or how do I provision elastic storage? So those things are going to be interesting about how we value those services above the individual minutes that we're seeing a lot of people measuring today in their finops environments.

Do you think that AI might come along and help us with all of this? Because it seems like there's a lot of data flowing around and I need to kind of maybe rationalize and summarize it. So that sounds like generative AI to me, and then I can apply some algorithms and some controls, and is that where we're headed?

I I do think that there are definitely opportunities for generative AI to step in and provide some forecasting models. Um, so when I've worked with other customers inside of the public cloud, there's trends that they have. So there's spikes in workloads, let's say at the end of a quarter, or if you're, um, selling things online, you see spikes in between the holidays.

So how do we build that forecasting model to automatically build future compute like models that we can scale proactively rather than reactively and optimize that spend even further based on time of year, time of day? Um, we have people that will be building applications and those applications, they may not have developers in other parts of the world, or they may just be America's based. Well, what do I do with the compute resources?

Do I have to run them at their high watermark or at seven o'clock in the evening, can I run a minimum amount of workload and then coming in at 6:00 AM East coast time, do I spin those resources back up? It really comes down to how far do you need to optimize before you start running into situations of, um, the complexity of being able to set up those environments. How many people really know how to do that?

So those are things that I think, uh, generative AI can really help with is being able to find those trends and analysis and then automatically building the scale up, scale down type models for those applications. I don't wanna accuse our developer friends of being quote unquote drunken sailors here, but um, it seems like a lot of times they create a development environment and they don't wanna take it down or they forget about it, or they have a bunch of BMS running and they forget about that too because, you know, they think they're gonna use it again any day now, and then it just sits there and generate some costs. And then on the back end, it seems like, you know, everybody over provisions.

'cause nobody wants that call at two o'clock in the morning and we're not really optimizing the compute infrastructure because, well, if it comes down to cost versus, you know, my wife being mad at me because I got roused out of bed at 3:00 AM I'm going with the compute cost. So, um, how much of this is kind of our human nature that we need to kind of work around a little bit? I I do think that there's, uh, some human nature elements to this.

Uh, there's a lot of people, even with how long the cloud's been around, there's a lot of people still somewhat hesitant to moving workloads into the public cloud. Um, when a funny story, uh, a company that I was working for, um, it wasn't until they generated actual showback of the resources that were being consumed. Normally central, it manages the budgets for all applications.

And the development teams are, their budgets are really just about the people on the project. So they'll go to external staffing, they'll go to internal hr, they'll grab their people that they need and they can sort of break it down into what the cost looks like for that application based on the time that resources are allocated to it. They never take into account the actual infrastructure that requires that application to run all the way from issue all the way into production.

So they don't think about things like, Hey, what does it look like for CI to be running 24 hours a day for GPUs that are building new models for generative ai? Those things usually come outta the central IT budget because the developers usually don't have to worry about it. In this company, there was a group that they had a budget that was really low and they were like, yeah, you know what, these are our people.

And um, then what they did is when they started getting showback, they were surprised by the infrastructure that was being generated by these 36 developers on this team for exactly the reason you just described. They were leaving environments up and running. They, um, would spin up another environment.

So they're trying to create an almost in an environment per, uh, task or per ticket. And when they started learning how they could optimize and share resources, they were able to draw that budget way down just by being able to show them what the costs actually looked like. This is something that's very difficult to do if you're running in your own data center because it's usually a, a CapEx type expenditure.

So it's really hard if I'm running in a four U server that's been racked for five years, do I still measure it off of the five-year cost from when we bought it or based off of the price of what that particular hardware would cost me today? This is something that's great about the public cloud, is we usually have very recent infrastructure that we get to work off of. And because we're being billed on a per hour basis, it's much easier for us to be able to generate that chargeback showback to those, uh, application teams so they know where the spends actually coming from.

Do you think we're gonna see as more organizations wrap their arms around the cost, um, more movement of workloads? Because some of the things that are running in the cloud, let's be honest, you know, it was covid times, it was the only place we could put them. So maybe we need to kinda look at the total cost of running something in the cloud.

Likewise, to your earlier point, some of the financial quote unquote engineering that goes on to justify on premises may not stand up to the light of day either as we apply finops not just to the cloud, but also to on-premise. So are we gonna see a lot of workload movement just because as people get a better handle on these cost issues? Yeah, that's a great question.

Um, what I'm seeing with our customers is there has been a little bit of a slowdown for workload migrations. A lot of that comes down to staffing. Um, the, there's been, uh, reductions in force inside these IT organizations, the people who had a majority of the knowledge about the applications themselves and how they ran.

They're a little hesitant to move them to a new environment because it may or may not be running okay today, but they would rather fix it in place rather than building a migration path. But then other companies that we're seeing when they break that application apart into the platform components and really leverage the cloud that it was in the way it was intended to not as individual infrastructure services, they're able to create building blocks. And so they have reusable templates that they can create and they're using GitLab to drive that automation for those workload migrations.

So they can see, oh, if I need a MongoDB database, here's what that MongoDB database, MongoDB database looks like inside of AWS for us as an example. They can reuse that template over and over again and get the economy at scale for the movement of that application in the public cloud. And then they get a lot of the other, uh, flexible benefits that we were just describing.

They can now see what that particular resource costs them on a per hour basis. So what have you seen people doing well to get down this path? Because I feel like a lot of folks look at this stuff and it's immediately overwhelming, right?

Because to our earlier point, there's all these things that are priced somewhat differently and now no apples and orange and all this other stuff. So how do I get started? Where do I begin?

Because, you know, ultimately I, you know, the first time you look at this, you just shake your head and go, never gonna get there. Yeah. So, um, I have a number of conversations with CIOs and CTOs, um, CTOs think about this problem a little bit differently.

They're really interested in the architecture side of it and a lot of the CIOs are interested in the financial side of it. Um, the first thing that we see a lot of our customers wanting to do is not only measure what it costs for a production runtime sitting in the public cloud, but holistically, what does it cost to be able to take an individual feature or bug from development all the way through to production? What does that cost look like?

So we're seeing teams being spun up out of the finops concept that are driving this thing called value stream management. So how do I organize my people and how do I organize my projects in such a way that I can maximize the value of change to a particular customer or for a particular event that allows us to then aggregate all of the different steps that it takes for that reorganization and for that change to occur for us to be able to funnel that up into our Excel spreadsheets, into other things by measuring all the different parts as well as the runtime environments, aggregating that cost and being able to show that to A CFO or to A CTO so they can figure out where they need to do optimization. We do this inside of GitLab ourselves.

So we have somebody who is, uh, director of finance and, um, about once a month what they will do is sit our CEO, our director of finance and our CTO, they get together and they look at, there's a concept in GitLab called a merge request. And what they'll do is looking at that merge request. Anything that is open for more than three days, we have to justify the expenditure of why that has not been merged and released into production.

So we wanna keep that pipeline of change moving quickly because the faster we can get those things into production, we're able to do things on the backend, like we can reduce support burden because we may have a number of customers who are running into an issue. This is the part where finops has an opportunity to expand into is usually when I'm talking to customers, they're only interested in that optimization on the cloud side, but when we start to say, Hey, by the way, I now can allocate a support person to be able to work on different tickets 'cause they're not answering the same question 30 times in a day because we have a consistent bug inside of the system that allows 'em to be able to get new features, new capabilities out, rather than trying to walk them through a workaround or something else. So we're able to optimize that process all the way into the support side.

So that's where having that total cost of ownership of what change means is where I see this, uh, finops concept going down the road. You mentioned value stream management and we've been banging on that drum for some time in the land of DevOps. Um, do you think finops will pull value stream management or at least create more awareness of that as a motion for managing or assessing the value proposition of software initiatives?

I definitely believe it's pulling it forward. Um, so the value stream consortium is seeing this as, uh, sort of top of the funnel for being able to drive awareness of the importance of measuring change and change is both measured in good and bad. So, um, when you're able to evaluate those trends across the organization, you'll be able to measure, okay, well where are certain teams excelling and how do I get other teams to act like them?

What are they doing right? And how do I retrain other teams to be like them? How do I go ahead and build patterns, uh, for reuse?

Um, I'm, I used to be an enterprise architect and that's near and dear to my heart is how much of this can we templatize create blueprints out of build components that are reusable so that way we're not recreating a wheel every time we go into a new project. And as we're able to do that, that ends up being realized in value stream management. And then I'll, um, at the end of that, when we start looking at finops, that funnels up to the CFO and they can see the cost of building new applications goes down while customer acquisition costs are going down, while revenues are increasing.

And I have yet to meet A CFO that doesn't wanna meet their board of directors and show them those charts and not be jumping up and down and saying, look at how awesome I am at being able to optimize for this. So do you think ultimately that finops should be just kinda another metric we're tracking alongside all the Dora metrics and it's just in our console and we should be able to see in real time what things actually cost? Or is this gonna be like, you know, a separate platform somewhere with a separate console that, you know, a finance team is using and then sending off nasty messages to the IT team?

Uh, so I definitely think this is gonna be embedded in your, you know, DevSecOps platform. You know, the near term things that we're already showing to customers is for, uh, how many CI minutes are being consumed by project so we can show them the minutes, but because we don't necessarily know what sort of infrastructure lies underneath that, it's hard for us to be able to put a cost to it. But they can easily do that mapping by being able to know where their, what we call runner fleets, which is where CI jobs execute for GitLab.

They can do that mapping by being able to pull reports from AWS or GCP or Azure and then mapping that to the minutes for that project. So that's a short term win. But I do believe over the long term is we're gonna be able to know more about how these applications are being deployed and managed and, you know, um, what happens when I've got a bug in production, we've all heard the numbers.

Oh, finding a bug in production is x cost. Finding it in depth test is another cost. Finding it in the developer's ID is another cost and it's usually a 10 x type of increase every time you go across.

Well, let's actually show that. Let's, let's have that visible to a development team that when they find a bug in production, why is it important that that's getting fixed as soon as possible? Because we've gotta draw that cost down because it's driving costs and support.

It could be driving loss of customers having that kind of visibility we just don't have today. And the only place you're gonna get that is in a holistic platform like GitLab. All right, folks, I know it feels like the cloud infrastructure is free, but trust me, there's a bill that shows up every month and somebody starts screaming about it somewhere.

So we need to get better at all this stuff. Hey Lee, thanks for being on the show. Thank you so much for having me.

All right, and back to you guys in. Hey everyone, happy Monday and it's a futuristic Monday. Today we're gonna be talking about superhuman ai, invasion of the drone body snatchers, uh, as well as carbon counting.

It's 2024. What else can we do? You are watching Textron Gang.

Hi everyone. Happy Monday to you. Welcome here to the Techstrong Gang.

As I mentioned in the opening, we've got some really kind of futuristic, you know, topics we're gonna be hitting on today, but if of course the future is now, and so they're very timely. Let me introduce you though to our gang for today. First of all, joining us from the road up in New York, uh, the one and only John Willis.

John, welcome, welcome back to Textron Gang. Hey gang, what's up? Very well, good here.

Um, pleasure to have you here, John, joining John and I is our, uh, echo Insights editor and, uh, professional, you know, when it comes to carbon and, and, uh, climate change and so forth. Bonnie Schneider, welcome Bonnie. How are you?

I'm Well, thanks for having me. Fantastic. And then last but not least, again, hailing from the Bronx.

Bats left Roses, right? Batting 3 48. Yep.

Batting three 40 early in the season. We'll see. Talk to me after the Allstar break, our chief content officer, Mike Ard.

It's clearly Yankee Celebration Day, right? Yes it is. Yes, it is.

Thenk the yas are back, the Yanks are back. Um, but you know, we could do another show on baseball. We're gonna have to focus on, on what we've got on our tech strong agenda today.

Mike, I wanted to kick it over to you on this superhuman AI story. Yeah, yeah. It turns out that the State Department is issuing some sort of framework document that suggests that, um, the AI may spin out of beyond our control.

And there's all kinds of calamities that may ensue and it's theoretical, but they're calling for the government to start preparing for that possibility. And secondarily, you're seeing the UN is, uh, calling for a ban on any AI system that violates human rights. But the definition of that will be, uh, hard to say.

But John, I know you've been following this space closely. What is your sense of, you know, how much are we concerned about maybe these systems spinning out of our human control? Because theoretically, the most important job in the world could be the guy standing next to the plug, right?

Yeah, yeah, yeah. Well, you know, they, so I, I was wondering if the, um, if the guys can do the wavy Scooby-Doo thing to say that I'm really not that scared. Um, yeah, I read the report, you know, it's a couple interesting things.

Uh, I think they conflate, um, the, you know, this, this is like, depending on whose definition, there's sort of three definitions of ai, right? There's just ai, what they call narrow ai, which, you know, sort of the Siri, all the sort of expert systems. There's, um, what they call a GI, right?

Which is theoretically where they, they call that sort of like, um, a strong ai, and that's sort of where the computers of the AI are matching human capability, but very sort of focused plan. And then the questions come up with, you know, are can the machines learn? Can the machines, um, you'll use examples, can they plan?

Um, the experts would still argue that we're not at a GI yet, right? I, you know, I think that's an interesting debate. We're somewhere between AI and a GI, you know, depending on what you're thinking about.

But this article and then the, you know, so the, the, the report conflates a GI with the third category, which is super intelligence, and that, I mean, there, there's still about a 50, i I, I'll just use my numbers. There's a half debate of the experts that say that we're not a GII, in fact, Elon Musk lawsuit against, um, open AI is based on that. He claims that once we made a GI, they were supposed to turn it all back to open source, right?

And that's a whole nother, so, but the article really just says a GI, but it, it throws in super intelligence periodically. Um, I, you know, I I, I don't think anybody believes super intelligence is an issue right now. I mean, it's, it's phenomenal what these machines are doing.

Um, but you know, when they use things like, you know, power seeking and you know, like the, how, you know, like the, the sort of the, the 2001 Space Odyssey, like, it won't allow us to turn it off. Yeah. It's run by electricity, you know?

Um, and you know, and I was thinking about this too. Like, I worked with a large financial institution, right? When they, they, they've been doing Al Gore trading forever, you know, a Night capital story, right?

Is a company that literally, uh, and an Al Gore in an exchange literally went wild. And, and, you know, they, they went outta business in 24 hours. So it's like $400 million, like 45 minutes.

I mean, how banks deal with that. They have border routers. So open AI has border routers.

You, you basically turn 'em off, you know, and guess what? The, the, it can't take over the world if, if there's like a, you know, if they've got, you know, a number of border routers and you, and, and any responsible organization and, and most of their infrastructure from what I hear is run by Microsoft, they're gonna have kill switches on routers. And, you know, and again, you turn electricity off.

One other point, mark, is that I went and read the report, and that report is written by the, um, it was all written by the one company, uh, you know, the, the, the, what is it, the Glacier or what the Glad Gladi Gladstone a ai. So all the, the three authors of that paper were Gladstone ai. Well, if you look at Gladstone's website, they sell ai, you know, so I like and know the, the fact that, you know, all the sort of UN and US government was sort of, you know, pointing to this, this paper is that, you know, I was expecting to see a consortium of like, you know, industry experts, maybe like a Josh Corman on it or, or something like that.

And, you know, and then last but not least, you know, like I think their outline, you know, like, you know, like one of them is, um, basically, um, outlaw open source of advanced data. That's exactly the opposite of what you wanna, it's open if there's any savior, if there is an a SI coming in the future, or if a GI gets so crazy that we can't manage it. I mean, the, the anybody who's playing with this stuff right now will tell you the answer is to have the models open so we can see how they're weighed.

We can see how we could at least, we'll never understand how they're working at scale, but at least we have a cha a fighting chance. But if it's just, if it's, you know, open AI and it's, it's Amazon Running Claw or Atropic code, you know, like then we, you know, now we, we can't even tell what's going on. So, yeah, so I, I think there's a little bit of hype.

I think we've been, we've been sort of mentally trained of this whole like, sci-fi universe with this stuff. Um, but I'll, I'll just end with there. You know, there are phenomenal things going on in these like neural networks and the people who have built them and maintained 'em will tell you they don't really understand how they're doing the things they do.

So I don't know what the right answer is, but I'm, I'm really not that worried. Look, I, anybody who sits who thinks that they're gonna, you know, lay down on the railroad tracks and and stop the AI train Yeah, that's true. They're dead.

The train's not stopping. So, you know, you could write That's right. You could write till the cows come home.

I have two questions. First of all, you said right now, okay, I'm with you on that. I tend to agree, but if not, now, when, when do you think any of this might actually come to a head?

John? I, it, it, I mean that this is like a really, like terrible answer, but it depends, right? But I mean, humans are pretty resilient, right?

I mean, if you think about like, like I said, Al Gore training, right? We've had like flight, automated flight system. I know it's in, it's like multiple orders of magnitude more complex.

But we've been living with automation now, again, think go back 50 years and think about somebody explaining to you that we're gonna let planes fly themselves, right? You'd be like, oh, you kidding? That's insane.

What if the plane, you know, I, I remember when I, I sold Chef, um, I, I'd go to these old Linux, you know, sort of like stodgy cis admins, and they'd be like, what if Chef just starts creating servers? What if it just starts deleting servers? I'm like, yes, probably not gonna do that.

And again, I'm not comparing what chef does to what, like, you know, sort of a GPT-4 does, and completely different universe. My point is that we've been living with algorithmic compute power forever, and humans are very resilient. Um, you know, I I think we'll just sort of maintain, we'll figure it out.

We'll absorb and we'll sort of adapt. But I think there's also gonna be a lot of pressure, um, politically, uh, you know, how do we regulate what, who's, who's deciding what's ethical, what isn't, and going back and forth with that. Because as we know, you know, there's always a human behind the AI originally that's putting all the data in.

So going back to the, the idea of regulating ethics, that that's gonna be also as this, as Alan said, the train is going, I think that's gonna evolve pretty much at the same time or maybe behind it. We can't regulate human ethics. So it makes you think you're gonna regulate AI's ethics.

Yes. That's, That's we the, here in the US we have no political will to get things done. The EU might, but certainly Russia and India and China and Iran and the axis of evil and all these other things, you think they give that crap what any of these people say they're gonna use AI to their own best benefits.

And if it comes up and swallows them one day as a result, Oh, well, second question, John. Yeah. You were talking about putting the controls in and the routers around the edges to prevent anything from spinning outta control.

Humans are gonna do that. So do we need some way to make sure that those things are actually operating? 'cause last time I checked humans make more errors than machines, so Yeah, yeah.

And, and, and we're, we're both equally gonna make mistakes, right? That, you know, um, but you know, I think it goes back to our resiliency, right? Like, how did, uh, uh, something like Barclays learn to build?

In fact, it, it, so at some of these large financial institutions, they actually have like a two factor human to turn a border switch off, right? They didn't, they didn't do that at day one, right? They had some real bad issues with Al Gores going nuts.

And, and, and, you know, and, and over the years they've built these incredibly complex systems, you know, like literally, um, heartbeats within the exchange, heartbeats without the exchange exchange. Like, and so, um, I think our, you we're going to adapt to these things that are going to be mistakes, and there's gonna be a ton of mistakes. There's gonna, you know, my, one of my biggest fears right now is, you know, as we're accidentally putting this is the more scarier thing is as we're sort of interacting with these things, we don't know what our organization's putting in these models.

Now they say they don't train on your data, but they don't know for sure. So I don't know that we're not gonna find out sometime later this year or early next year that somebody at JPMorgan Chase is gonna be typing in something and they're gonna get output from Goldman Sachs. Or even worse, an adversary will go say, I am, I'm in funds at JP Morgan Trace, I work in 33 Broadway, and, and I would like to know how to configure the servers for fun.

And it answer the question. I think that's more likely to happen than the computer basically turning off all our lights and, and making us pay a ransom to, uh, you know, some, some Election. And that may happen once or twice and, and they'll figure it out and fix it, you know, they'll put in some safeguards or whatever.

Um, but let, let's not, you know, invoke images of guardians of the Galaxy coming in here to, to, yeah. How transparent should we be with folks about how that process is evolving? Because let's be honest, we're doing a lot of trial and error here, and a lot of it is, you know, not an experiment in a lab.

It's live. And so, I don't know if we're being candid enough with the average person, Hey, you know, I'm not a fan of Elon Musk, and I wrote an article recently in defense of his lawsuit. If you read the lawsuit, it's pretty interesting.

Now, again, like I can go down a list of all the reasons I don't like Elon Musk, and you know, Alan, you know this, right? Mm-Hmm. But, but I think his, you know, and, and whether he's doing it for his own gain, he owns that X he's an investor in X ai, does he want open AI to just expose the model so that he can learn things?

But, but at the core of what he's saying is, um, and he had this argument with the founders of Google. You know, there's a great article that in some ways I think he believes what I think a lot of people believe is the transparency gonna be in the openness. And unfortunately, right now, the, the, the frontier models, you know, the, the GPT fours, the, uh, Gini, the, you know, the, the, the Anthropics, um, clo, those are not open.

And, and, you know, and you could argue somebody who's like a data scientist might be ar listening going a, that 80, it doesn't know. It's really hard to train. I know that, you know, like, it's not simple to take Claudes, but least we have a fighting chance, you know?

Um, and so I, I think the transparency really has to be driven by, um, like opening up these models and if, if, right? And so again, the opposite thing of what the, um, the Gladstone report said, it, it shouldn't be that we need to, um, you know, uh, you know, outlaw open source, advanced models. It's, I think it's the opposite, right?

So, John, let me set you at ease. Elon Musk does nothing. That's not for his own benefit.

I know. And this law suit is, is strictly for his own benefit. You know, he wouldn't mind if it wasn't open as long as he had access to it.

But the world doesn't have to be open. That, that, you know, again, you think Russia's gonna open up what they're doing on it. You think China is gonna open China?

I mean, they're doing on it Or Any of these folks, but at least come on least we have a, why should we have to play with one hand tied behind our back So we co see the leadership to these other, you know, I geopolitical rivals. Do, Do we wanna get real matters? 'cause that's what we do.

We're better at, we're better at, at being humans than some of these other s They We're all humans. They're all humans. Humans color.

I know we, in all shapes and sizes and colors, I know We're going left you, but I, I think it's our responsibility to do it the right way. So, so what about, we've seen in the past with open source, right? Eventually it's supersedes in terms of innovation, the thing that came before.

So That's the only reason to do it, right? Well, then it may come down to the point where everybody collectively contributing to an LLM may accelerate the pace at which a proprietary LLM can advance Perhaps. And if it will, it will.

But to think we're going to do it for some super sense of ethics, we're not communist, right? We're not a, a society of atheistic saints. We, at the end of the day, market forces will control this.

And if there's a lot more profit to be made, well, it'll be made. Government Forces will control it too. Right?

There's market forces. The government has the will to control it. Um, but I mean, that's my point is there might be, You don't think, think the market forces have a say on what the government's forces do through lobbying and everything else?

Come on. Who's being naive now, Kate? No, I mean, but, but I, what I'm not being naive about is the idea that the collaborative nature of people trying to understand what these things do is going to be our best defense Absolutely.

Of understanding. Absolutely. And at least having, if there is, if the a AI is inevitable, and I don't know, I'm not smart enough to tell you if it is or it isn't.

But, um, I do think least having fighting chance would be having models, at least the collective of millions of people could at least try to understand in the aggregate. All right. Our AI tells us, we gotta take a break here.

Hold one second here. In the immortal words of Arnold Schwarzenegger. What?

We'll be back. Okay. I didn't know where you were going with that.

All right. As promised, we are back, and we're gonna be talking about the invasion of the drones. Turns out that, uh, Walmart Chick-fil-A and um, seven 11, and all kinds of folks these days are experimenting with drone delivery, and they're gonna have all these drones that are gonna be flying around our neighborhoods, dropping off your lunch and all kinds of fun things that will go with that.

But I guess my, my first question is, and I'll throw it to you, but, uh, you know, where are all these drones gonna fly around and how are they not gonna fly into each other and Or into other things, right? Or do we need congestion pricing for drones now? Shades, Shades of George Jetson.

You know, when I was, when I used to watch the Jet Jetsons, that was always something I thought about. Like, there's no traffic lights or lane markers, right? How did they not collide?

You know? Now theoretically, the drones using some superhuman AI might be able to avoid, you know, by talking to each other or whatever. But, um, I, I really feel like we have to give Amazon credit here, right?

Because Amazon came out with drone delivery, or started talking about drone delivery, I guess it had to be seven, eight more years ago. And everyone said, nah, never happened. Never hap Well, it's happening, it's here.

I mean, if we could deliver bombs with drones, I, we should be able to deliver groceries. Um, but there's going to have to be some autonomous driving ability built into these drone deliveries so that we don't, you know, you're gonna have congestion, but I, I would imagine, you know, nothing works perfectly. You're going to have collisions.

Well, that's what I was gonna say of the, one of the other X factors here is the weather. Yes. How are they gonna navigate through, uh, hurricane storms?

I mean, I guess that's mostly when people like to, when they're stuck home when during a storm they like to order food. Um, yeah. But, but when you have strong winds, this is a big issue with, with planes.

Well, But I, I could see that. But you could write software for that really easy, right? You have a wind detector.

If it goes over 20 knots doing my nautical thing, um, boom, go to the ground, right? And don't, and don't start back up until you get an okay or whatever. Right?

I, you, you could build those safeguards in. I'm more worried about Mike's drone. He got a cheap drone drone from some third, you know, rate country and, and it stopped working good.

And all of a sudden it goes berserk and takes out my beautiful Walmart drone. This is your boating issue, isn't it? Yeah.

This boat. Well, but it's the same thing with autonomous driving and trucks and everything. Yeah.

It is similar issue. You know, you guys remember a couple episodes ago I was telling you about a presentation I saw from the DOD about military drones, and, and their biggest problem right now is, uh, the commercial create, there's, the problem is there's no collective or collaborative intelligence. So like, when we see these spectacular things with drones where they light up the sky and, and create, you know, images of Penn State University or whatever, right?

Um, they're all individually programmed. And this is a similar problem with autonomous vehicles. I'm not an expert there, but like, one of the hardest problems of, of collective autonomous vehicles is how are they all aware of each other?

And so the, the thing I would sort of question is, if DDOD is trying to build, you know, weapon around drones, and they're struggling because they can't get pro, everything's proprietary. They even, they're trying to build open architectures to try to get people who create the technology for drones to start creating open, you know, collaborative, um, capabilities and frameworks for these things. I'm not sure what, you know, you know, what the, the vendors, you know, the e-commerce vendors are gonna do in that regard.

Are they gonna struggle with the same thing? Are they creating collaborative nature for, 'cause if, if Chick-fil-A just sends out their drones, Amazon sends their drones and they're all working on different networks and they're not communicating, and you know, regardless of the weather, they're gonna be smashing into each other. Yeah.

Target's gonna deliberately smash into the Amazon Jones, right? Yeah. And you also have, They don't have an army.

They'll take the military route. They'll take out, uh, yeah, there you go. That'd be good Sci-fi like target taking out Amazon delivery services.

So, Well, you also have the, uh, the human factor into, and let's say you're getting your food delivery wrong order. Your neighbor sees the drone, says, gets the drone to come to their, their house. I mean, there's a lot of things that could also happen with the human factor hacking trucks.

Yeah, yeah. Hacking the drone. Well, Hack, I'm hack drones is one thing, or, you know, up in the Appalachian Hills, some drones flying by my property and, and on the Hatfields, and he's headed to the McCoys.

I'm gonna shoot that sucker out of the sky. I might just do that for fun. Or just People, right?

Or you're hungry gun from a Christmas story and let's go drone shooting. You know? So I'll tell you what I am really dubious about, though.

So we can't even figure out an algorithm that consistently manages traffic flow in a city as it is. This is with cars and lanes and things that are reasonably structured. So I don't see how we're gonna manage drones who have, can go any which way they want in any kind of collective fashion, Except they are computer controlled.

So you could say, look, all Walmart drones have to fly at 2,500 to 2,600 feet. Uh, Chick-fil-A phone, uh, drones get 1500 feet, Amazon drones fly it. So you, you have that three dimensional kind of aspect to it speeds, you know, pathways.

So you can, you know, using a grid like that, I think really kind of segregate your traffic a lot more. You can than on a city block. Yeah.

But how did they drop this stuff off? Well, that, that's a whole nother story, controllers. I, I think you gotta start making drone ports on top of your house or your porch Or something.

And that's the collective, right? I think that's the problem that DOD is, um, dealing with, like the, you know, how can you manage these things at scale? Mm-Hmm.

And, and you're going to, they're, they're going have to be a network of intelligence Superhuman ai. Well, according to DOD, it doesn't exist yet for, um, for drones, at least according to presentation I saw, Is kite flying gonna become illegal then? Because I gotta like, only have a certain amount of space.

You know What? Kite flying is illegal in certain airspace. Mm-Hmm.

I, you're talking to someone who grew up near Kennedy Airport. I know this. So it has been illegal.

And, and I guess it will be, unless the, the kites have AI and, and kind of intelligence built in them to avoid drone avoidance. Um, it's a crazy world. It is.

It's a crazy world where you live in it. And, and my biggest thing is, given all of that, are they, you know, we we're gonna put all of these Amazon drivers and all these, you know, uh, DoorDash people and this whole thing outta business by drones. Is it really that much more efficient?

I don't know. Well, it, it, another thing that might be driving it is not only that the AI and the technology, but the, the movement to be more sustainable, less traffic, less transportation, less emissions. Um, one of the problems in cities is you have all these delivery trucks double parked.

It's more congestion. So they wanna have more bike lanes. I just lived in Manhattan, so I know this was an issue there.

So I think that that's part of what's behind it. How do we get more cars off the road? Okay, we'll get them in the air.

Yeah. Well, well, why stop at drones then? Let's, let's go full on Jetsons, right?

And have flying cars. That would be cool. Sounds good.

I think that was coming At least it was promised like 25 years ago. 25 we're older than that, Mike. It's closer to 50 years ago.

Um, but anyway, yes, it was. And you know, I want my flying car guard, darn it. Anyway, we gotta take a break here.

We're going to, uh, be right back on Textron Gang, and we're coming back with some Echo Insight. Green news. Stay tuned.

com is the number one online destination for DevOps education and community building. com covers all aspects of DevOps, including DevOps, best practices and tools, DevOps culture, DevSecOps, business impact, continuous testing, continuous delivery, and more. com has the largest collection of original DevOps content featuring breaking news, blog posts, podcasts, and more.

com to learn more. com where the world meets DevOps. Hi everyone.

Welcome back to the Techstrong Gang. Well, now we're talking about sustainability. And as organizations strive to be more sustainable, a lot of folks are wondering, where do we begin?

How do we know how much carbon we're emitting at this point? And then once we do know that and we can measure it, then what, and Dynatrace has offered some great solutions about how to do this with their new carbon impact app. And I recently spoke to Klaus Ensen Hoffer, who's the product lead of business analytics, to talk more about the challenges and the capabilities of implementing this app with Dynatrace, You have to balance between customer experience, availability of IT systems, business needs, SLAs, and all of that.

And with that, you are adding carbon to the, to the mix as a, as a part of, of, of, of the game. And you have to balance out between these dimensions. And the beauty is you have to have everything in context of IT systems into, and give every stakeholder a different perspective.

Because when you think about it, you have green engineering, green coding, which is aiming for optimization done by developers, by engineers, and you have initiative. These initiatives do go into IT operations as well, where you try to optimize, uh, the data centers that you're running, the machinery that you have there. All that needs to work together in order to really, uh, produce a reduction.

But everybody needs to have the same KPI and focus. Everybody needs to have the same view on the state of the corporation. And, uh, that way really step by step optimizing, uh, the carbon footprint.

You know, Dynatrace also says that, uh, there's a lot of, uh, global mandates that are happening, and that's one of the reasons that they've moved towards this app. And, um, it's really interesting because it's bringing more attention to IT departments, which a lot of people are focused on, and how we can go ahead and take the insights that the app provides and then make these changes and also report back to shareholders regarding ESG, which is a big commitment as well. So, sure.

Um, do you guys think that we're seeing a lot more of this? I think we are, but I wonder if it's gonna be like going to the restaurant now and everything has a calorie listing on it, right? And every little thing.

And so maybe every little thing is gonna have some sort of carbon counting mechanism on it, where we're all gonna know exactly, you know, how much quote unquote harm we're doing to the environment. And maybe we're all gonna owe carbon credits or something because, you know, somebody has to offset that and becomes part of your digital wallet. But it, you know, you can imagine how far it can go.

I'm not saying it's bad, I'm just saying I've seen that with shopping at el, Your Yeah, I think we're already starting to see it. I, I'll tell you, you know, we're just back from Europe. We were around Paris for Q Con, and then I did some other traveling through Europe.

And, and I'll tell you, they're again, they're much further ahead on, they're much further ahead than we are on this. And they're already thinking like that. What is the impact?

You know, I, I went for a dinner boat cruises, uh, in Paris, and a big selling point is the boat that I went on was all electric and the amount of carbon it saved versus a, uh, you know, non-electric boat. And they, that was their major. Well, that it had really good food.

But, but that was a major selling point. And, and there were plans within Paris to make all those boats electric. And they had, i, that one boat saved something like 540,000 tons of, of carbon a year, um, by doing that.

And if they did, and you know, the government, the local government had it, if they, all of the boats were electric, the amount of savings would was substantial. So I think that's the world we're living in. Yeah.

We're moving towards if we, if we're gonna be successful, we, we need to do This. You know what, then you got Sam Waltman wanting to do the, the $7 trillion project, which is basically gonna be a massive, you know, we're talking about GPUs that, I mean, I think what's interesting, I think what Dynatrace did, which was fabulous, right? Like I, I've been waiting for somebody to do this.

I have a friend of mine who talks about this all the time. Like, could we actually calculate the carbon footprint from like, what, I mean, you can get those APIs from Google and Amazon and, and, um, but the, the areas that I, I think we're just getting such a tip of the iceberg is like, what about all the mainframe use in in a corporation? com, and then now add, how do we even come close to calculating the carbon footprint of copilots?

I don't, I don't think there is an API for any of that. There isn't a calculation. I think the real answer is some of the research I've been doing on, uh, how data center, uh, you know, like taking advantage of, you know, um, composable data centers and, and, and rethinking how we're sort of building the infrastructure.

Um, that's where we're gonna basically get, you know, 'cause like to what Alan said earlier, you know, it says all the time and earlier is that, you know, we're not gonna stop this train. I mean, right now the Nvidia and G Nvidia GPUs are scorching the earth. I mean that there's no, until somebody figures out a better way to do ai, um, you know, we are just, you know, just consuming power at, at a, an alarming rate.

In fact, some of my research is that, you know, that it used to be Pete was a concern. Now it's a major concern in data centers. Yeah, right.

Um, you know, so, and, but there's some interesting stuff going on. Um, you know, that, I'm just gonna write some articles for you about this stuff, but there's really, really interesting stuff going on that, so I, I think calculating and, and, and sort of ESG and having, um, you know, having metrics for your carbon footprint is, it's like anything else. Like it, it's good, right?

The more good is good, the more, you know, I, I think, you know, it's interesting. Dynatrace also put out some statistics that, uh, I thought were interesting about moving your workload to the cloud can reduce the carbon pri footprint by up to 96%. And when we also, um, talk about carbon emissions and what, what's the biggest problem, transportation always comes at the top of the list, especially air the airline industry, for example, um, they found that cloud computing has a greater far carbon print footprint than the airline industry.

And average cloud server idle time exceeds 70%. So, um, there, there's a lot more pointing towards this is something that, that needs to be done. I think generally, as a general population, we look, we definitely have our eyes on transportation on, and our last segment, we were talking about drones.

Um, but, uh, the amount of energy that, um, that's going into the, our IT carbon footprint, um, the focus is on measuring it. And then how do we, um, what are the plans that we can make to reduce it? So some of it, um, involves recycling, um, and rethinking how we do with, uh, do way with waste.

That's a big issue as well. And The reason that, yeah, and the reason that footprint is reduced is that these, uh, organizations, you know, Google and Amazon, they really focus on composable infrastructure that they don't waste. I mean, the problem that most large organizations have have is their, you know, their CPUs are idle, but they're still paying power for it, right?

Because they don't have really good sort of InfiniBand and, and, and infrastructure in place that, that doesn't starve the CPU. And that's the biggest problem with GPUs right now, is there's this sort of, this GPU starvation is that now these things are like power hung, like compute hungry, and, and you know, like you're paying that kind of power, uh, tax on A GPU versus a CP, and it's basically idle 60% of the time because you can't get the storage and the memory and the shared memory. So anyway, again, that, that, I think that's for people who are still gonna run their own data centers.

There's their sort of advantage is, um, you know, trying to figure out how to create composable infrastructure so that you can sort of make use of, you know, sort of the storage to CPU, all those things. Uh, and again, that's really fascinating stuff going on right now. I think networking has a big part of this conversation.

'cause we saw early on people were building data centers in places like Iceland or Upstate New York near water sources. But then it became an issue about how quickly could I access that compute over a network. It was too slow.

So you saw kind of a shift back to more metropolitan areas where I was closer to where people were consuming the data. If we wanna build more efficient data centers, whether they're in Canada or wherever else, we gotta have better networks. 'cause otherwise we're stuck.

Agreed. Well, that's the, that, that's the thing about the sort of composable rate, which is the bottleneck is the storage to compute, or the memory to compute, right? Or, and, and so being able to create sort of multi-channel environments where you can really have high speed, high bandwidth, high, you know, um, that, that's gonna sort of reduce in, in sense, reduce the power consumption because you know, you're getting more value if you, if you're, you're running, you know, a, a million GPUs and they're only based and they're being starved, 60 or 70%.

I don't know the exact numbers. You know, what would happen if you didn't star if you could get their sort of utilizations up to 70, 80, 90%? You know?

And again, that's I think what, you know, what Google and Amazon have perfected over the years is the ability to build network and infrastructure like that. You know, that's typical of sort of first generation, early on stuff. Like, first you want to get just the functionality, then you start optimizing.

So I, you know, again, John, to your point about ingenuity of human minds, I think that'll be the, you know, the gen two, gen three, gen four will continue optimizing and making them more energy efficient, letting them throw off less heat, you know, because someone will build a better mouse trap otherwise, and that, again, market, market forces that work there. All right, so buy land in Northern Canada today. So you can sell it to somebody who's gonna build a really big ass data center tomorrow.

Or it could be beachfront in 50 years. You know, I don't know. The climate change might, that would be good for beachfront.

If you, any of people listening there, kids are going into engineering. Um, you know, this friend of mine, Pacific Crest, who, who gave me a whole bunch of analytics on, on this whole subject, he said that right now one of the hottest jobs is a, is a job that people weren't even taking, is getting trained in heat entrance engineering. It's like, you know, something that nobody really cared about for the last 10 years, and now all of a sudden it's probably one of the hottest positions you can get, you know, um, you know, crazy.

So crazy. We, well, you know, that begs the question of the jobs of tomorrow. All of these things that we spoke about today, superhuman ai, drone delivery, and, and regulating and controlling and know at scale carbon footprint and measuring all of that, they're gonna create jobs that don't exist today.

I agree. Mm-Hmm. But the John's point, the guy driving the HVAC van around town, you ever see those guys?

Yep. He's laughing all the way home. Yeah, totally.

You know what, you're still gonna need those guys too. Absolutely. Anyway, if we don't have anything else, I think that's gonna call a wrap on today's Textron Gang.

Stay tuned. We have a full, uh, Textron TV schedule for you for the next couple hours. Um, and then we'll be back tomorrow with another fresh episode of the Textron Gang.

Until then, John, thank you very much. Bonnie. Michael, pleasure.

Thank you very much. This is Alan Hummel. We're out.

All right. I hope you enjoyed today's text Drunk Gang. We had some real futuristic stuff there with superhuman ai, drone delivery, carbon counting and, and more.

But now it's time for the rest of our Text Drunk TV show. And let me tell you what we have in store. Uh, first of all, an interview I did with Comrade Comrade's, a large company based over in Europe and all over.

I had a chance to talk to their COO of Vlad, and it's a tough last name for me, Vlad and Tan Civic. And we spoke about, you know, today's disaster recovery versus, you know, the old days where we had big tapes. Good conversation with Vlad.

He's a really bright guy, and, uh, I recommend this. This is Textron tv. Hi everyone.

Welcome back here to Techron tv. I wanna introduce you to Vladin, and I'm gonna do my best on this. Vladin, Vladin, Vladin Vic and I Anvik.

Maybe we're gonna call him Vlad. Vlad, welcome to Text Drunk tv. Just for the record, say your name correctly, so your family hears it.

They, they don't think, oh, that guy butchered it. Thank you. So my last name is Vic.

Sorry, a little bit long. My name is Lada, but you can call me What? So, you know, there was one very good famous basketball player in NBA.

He played the Raiders and Sacramento Kids. V they To pronounce. Yeah, no, that Vlad has become, especially for basketball fans.

Uh, a very popular name. Right? So good, good for you.

And Vlad, thank you. And I appreciate you, you coming on and, and talking to us today. So you are the, uh, COO Chief Operations Officer at a company called Comrade Group.

And before we get into Compr Group, let's talk a little bit about your background. How did you come to become a COO over here? Well, we need to go back to I'm more than 30 years in business originally.

I, I'm a software engineer, so I was educated from the military engineering. So my background was military engineering. And actually I started my career in gun factory.

So I ended up producing this software for, for the, you know, greater the Gulf. The gun P is very, very old, established 1853 in progress. My hometown, this is where I'm original.

So basically, I think it's pretty good basic, you know, if you are starting to become the, to, to produce the software, you need to think about a lot of things. Not only about innovation, where the value, but you also need to think about availability. So I start career, that disaster group, and then, you know, to different, different stories.

10 years ago, I started with a stock engineer. Nine years ago I was on chief preparation officer for the whole, so you maybe remember the Youo s So that company was producing the stead cut. So, you know, after nine years, I started as a programer, but then I was on still level, uh, in the board of directors possible for the whole, it, there was about 46 companies in that room.

But then I do the contract delete, and then I start my own company stock, the outsourcing code. So that was the huge stuff. Sure.

You know, like typically to go out to your conference zone. So I step out, I created the software outsourcing company, but we were entertained at that time, the Microsoft boutique, very, very profitable Microsoft technology. And we, our clients was through the premise of that company.

Now we were creating a software from the hp, but also from the Microsoft. And then five years later they acquired my company. Then with a few steps, I was the GM of few companies.

Then I stepped out, rejoin. I did a lot of things from the corporate perspective, but also as entrepreneur. I don't know that I been short share my story, but actually thank you.

It's a fascinating story. I haven't heard people talk about the Yugo in a long time. I'll be honest with you.

It's been a long time. But in many ways then Harold did a lot of the, you know, the small smart card, the minis, all those, I mean those that, that's taking us back. So Vlad Comrade, some of our audience is probably familiar, but I'm gonna bet a lot of our audiences.

How would you describe comrade to them? Com is company born in 1990s, and we have, I would say the two major, major footprint. One is in distribution, and the second one other one is the stock develop.

So, you know, we start with one branch as a distributor of the well world known brand. They're speaking about at the Arctic region. This is where the original, the company was born.

And then there was two companies, one focused on distribution of the China. They, uh, compact church partner, Like channel, Like channel. And then the company who was doing the stock to develop more than 1000 engine and then joined 2008 company, acquired the other companies and became the biggest company in this area, providing the services global from early beginning in 1990s, we start working with, and therefore HP created one of their products for the disaster tower called Data Protect.

So this is the original, how we start being really deep, deep in this disaster recovery backup technology. Then can the other customers that we were providing develop for that, such as automotive industry in industry, you know, then we created several companies back in these 30 years, three zero years, we spin off several companies. For instance, in 2020, we spin off company called Company Digital Service to end up and company listed stock exchange.

So spin of that company then, uh, 2021, this spin of the other company called ku. KU is very well known in disaster recovery world. And actually capital KU from us.

So, you know, you see that 30 years ago we started doing software development business from the real tech company. There is still of several companies, but still some of the knowledge is here. Why?

Because the, the one part of the company services remain two country. And then we named the company for, for, so quantity 360 was what we actually speaking about all the knowledge from those 30 years when we start building some specific solution, the product we offer is still here. But in short, I don't know that I give you any idea about the content or not.

If we think about the pillars, content revenue is around 500 million euro thousand, million euro per year. And we have around 3000 employees in different Wow. That's s sizeable.

That's a big company. It's a big company. So, and, and well, what's funny is even though you've spun off companies that got acquired by Bain and stuff like that, at your heart though, there's still the, uh, Dr.

Disaster recovery kind of expertise within the company. Um, and, and I wanted to talk to you a little bit about that today, right? Look, I'm like you, I've, I've been in technology a couple, couple minutes myself, right?

A long time. 30 plus years. And over that time, you know, there was a time where disaster recovery to me meant you had big tape machines, right?

And a tape library. And then that tape library was very automated. There'd be like a robotic arm that would pull that tape out and get, but, you know, and, and thank God we never had to really restore very often.

'cause what we found out is it could take weeks to restore something if it, if it went bad, right? The so, but DR has always been i, 23 years, I'm down here in Florida now, right? And our fear is always that some big hurricane is gonna come and wipe out the data center.

And how are we gonna, well, you know, cut off all the power. We need generators, we need backup, we need offsite backup, we need everything, right? The cloud came along.

The cloud made a huge difference in terms of DR because we were no longer tied to a data center or even a company's data centers. We, we could be in this region and that region and mirrored here and mirrored there. And it kind of changed the DR game.

Then we started, started seeing things like ransomware and all of a sudden having a DR that was off prem, not connected. Wow. What a lifesaver that was for a lot of companies.

So we've seen a, an evolution of, it's not, it's not your father's disaster recovery anymore. Talk to us, you know what, but I'm not in it. You are in it vlada, You know, What's the latest and greatest.

Yeah. Funny thing, when I start my professional career, I start in IBM Computer center. So I remember the tape, not You remember those, huh?

Me too. By my own sense, you know, Uhhuh, and yes, as you said, you know, we, in the, for instance, in one of the companies, we have two computer center and there was one other, you know, disaster Recovery Center because we replicated the data mostly over the weekend, you know, Sunday, Saturday, Sunday, like, yeah. But, and the technology dramatically changed, as you said, the, the internet, then the cloud, you know, all those dramatically changed, but what didn't change for all those years didn't change.

The reason why you need to have disaster economy. So first of all, you need to have the business continue. So the business continuity is the best, but it was 30 years ago, 2000 today, what's changed?

The speed of technology change and the impact of technology on whatever business you are doing is now much big. What does it mean? It means that you really need to invest in advance to the disaster recovery.

What is a typical mistake making 30 years ago, but now it should be invest in disaster recovery before some disaster happen, or it's always, always smarter and cheaper to invest in front of, you know, to be, to, to act in the front of the energy disaster. So this is what was happen, but now based on the cloud technology, it brings a lot of, let's say, decrease of the cost. You know, it's much easier now not to have all the premises.

You remember how it was, you know, given the cooler as, as a house. Now you don't need to think about it. But now it's really the matter of the privacy, because when you order your own, you know, the disaster recover as a service, it's very nice.

It's easy to, you know, just click and tab it. But on the other hand, you know, it's about two, I would say very, very challenging things. One is about the privacy of the data, and the second who is responsible for the data.

Meaning it's somebody who is your cloud provider. They, they're not responsible to keep your data back up from the data is not only all the data, it's also the application. You have all your business application, the cloud, and you know, they said, okay, it might lose.

We don't guarantee you that it'll be there. So what does it, you want to prevent disaster. We need to think, okay, how can I really have the fully operated disaster account?

And then now we are to this either hybrid month, however specific service product. So this is where we as the 360, or let's say the company experience, okay, let's do the analysis. What is data, what you need, what kind of the data application we need to recover, how fast you need to do it.

And then to different product figure. I don't know, did they? No, No.

I, I think you're dead on Vlad. These are, these are the things that we, we need to do here. So you, you know what I see, you didn't mention AI though.

Everything we hear about today is ai, but I've heard people say AI is gonna make DR much easier. It'll be totally automated, easier to back up, easier to restore. What are you seeing on that?

I have to come back to my history because in some particular, I was running system integration in the 12 countries around the Southeast Europe. And, uh, we have major clients in banking division. Most of them are sensitive of being different type of sensitivity.

Why? Because in the banks, you know, if, if the bank is not running all the time, they're losing the line. Then there is a reputation of the, they lost the data.

It's totally disaster. Now why, why I'm explaining those two things. It's about the business continuity and it's about the data.

Now for the banks, they wanted to have disaster recovery because the, the sector banks and the regulators forced for us as the system was easy money because we offered sale. But then problems came. The problem came at the moment when they really need to prove if something happened, they need to run the, they're speaking about the needs now in the government, different, they is okay for us to, they start offering the service to citizens in one day or three days.

It's not critical as that. But we need to recover and to secure all the data. You don't, you must secure them.

There is no Databricks. So don't things actually force us about this. All those things, business continu help to ensure that the service will be in the banks.

The rest of the real time industry will continuously, and then in the government to keep the data monitor. Then you can force to, let's say, postpone the, let's say the recovery costs as the answer of those questions. What you need to kick mark, have the solutions that we provide, how together, this is actually what we do believe that it's most critical model.

Now, we said about the ai, AI I don't think can replicate the people, the human in all in the aspects to do this replication of the database of the just do the snapshot of some application. It's fine. Then it, when it comes to run the service around it, I'm not sure that it'll be run.

Just like, so we are also using a lot of machine learning AI stuff to make it very efficient and productive to build this application. But when it comes to run the service, then somebody needs to monitor on them and to work. This is our student.

Understood. You know, Vlad, I I feel like I, I, we missed, I, not that we missed, but I didn't explain it good to the people out there. When we talk about comrade, it's Comrade group is sort of the parent organization, right?

Comrade group, and you're the COO of comrade group. But the whole DR thing that we're talking about is part of Comrade 360. Is, is that correct?

One part is because we are, we, uh, we product development for some different vendors, such as such, well, such hp, such as cyber forecast. This is what we are in the other companies. We, we have system integrators and then we are offering those service.

So we are using some of the products from the vendor. We are the power and they implementing and giving business. So there are different things that we are off the first we approach the customers if they want to go some consulting business, what, what type of disaster recovery solution they would like to set up.

It's one thing, if there is a value, we would like to build some product because we did it past several times. Then system come in and say, then make all the requirements and the whole product develop side cloud. We do believe the, the answer because the cloud, yes, it's time to help, but at the end of the day, you know, some of the data we like to keep from the premises because of different things.

Maybe the data we are, if you have ations, very, very, very cost expensive for to put all more data in the maybe the label, You know, you think about it, right? The cloud was supposed to take the expense side of that. But what we found out is it's still cheaper to keep the stuff in your own data center than than the cloud.

You need to really understand what, what came to your business point of view, the cross point of yourself, and then to, to do the best of your business. Absolutely. Vlad, we're almost out of time, but I wanna make sure for people out here watching who's you?

Look, Dr is every single company watching this needs a DR plan, right? What's the best, like what's the path? What website should they go to to engage with compr?

What's the website they go? Yeah, website is pump three com. Our quarter of that company is in Boston, Massachusetts, Toyota.

And we have the GM store that we running operation in the us. So, you know, they can find, it is easy to find, easy to understand how to approach us, what to see, what would be the, the, the right question. They can read the case studies that best way to get about And what, what's the web address?

I didn't hear it colleague. com? Compre 360 what?

Comp 360. Do com. Dot com.

Perfect. Alright, glad. That's the one thing.

If I, if I don't get anything else out, I want them to know where to go get that. com. Vlad, I want to thank you for coming on today and giving us what a fascinating story.

You know, like, like a lot of work in it. A lot of it isn't sexy. A lot of it is just the nuts and bolts of how, how we do business.

Disaster recovery is one of those things, right? We kind of take it for granted. Yeah, of course we gotta do it, but people sometimes need to be reminded.

Unfortunately, it's when disaster strikes that they get reminded. But it, it's good to know when you don't need it, that it's there and, and how important it is and what goes and how people like you and the folks at Compr, you know, work on it so that when we do need it, it works the way we want it to. Thank you.

Keep up the great work. Okay. And we'll have you again soon.

Thank you. Next up we're gonna start running some of our CubeCon Paris, hold on, hold on. Videos.

And, uh, the first one out of the, out of the shoot is one I did with my good friend, Tali Notman. Not Norman. Tali of course is the CRO at Jfr.

She was employee number six there. Fascinating story. Her background.

You know, salespeople are born, not trained or trained, not born. She was the HR person, but she took on sales and man, she, she, you know, commands a sales force that's responsible for hundreds of billions of dollars of revenue right now. I love talking to Tally.

She's great, great person. She's really the power behind the throne there. Here's my interview with Tally Notman.

This is Textron tv. Hi everyone. We're back here, live in Paris on the busy buzzing show floor of Q Con.

I am really happy to introduce our next guest. com. March is 10 years.

10 years already. Oh yeah. And if you've watched us, we always have on Shlomi Baha, my friend who's the CEO at Jfr.

We've had Y Laman many times. My good friend Fred Prince, the three, those are the three co-founders. We've had Steven Chin, we've had countless people from Jfr, but this is the first time we've had this woman right here.

And I will tell you dirty little secret, she's really the power behind J ffr. She's their CRO. Her name is Tali Noman.

And we are, I'm thrilled to have Tali here with us. Hey, Tali, welcome. Hey, thank you so much.

I can get used to it. All this compliments. Yeah, No, don't let it go to your head.

We'll keep you now. So Tali, let, let's start with kind of your Jfr journey, right? Mm-Hmm.

You've been with Jfr, what about 11 years? 12 years? Well, you won't believe it.

It's been 14 years now. 14 years. Yes.

Yes. Almost From the beginning, Right? Since the very beginning of the commercial, uh, the go to market journey.

This is where I joined. Absolutely. Yes.

And, and your journey from Jay Frog has seen you kind of take you and your family from Israel move to the Bay Area though. When are you ever home with running around, but Right, right. I mean you more as much as any of the other people I mentioned symbolize really the success.

Thank you. Thank you for that. Well, of Jfr, I'll tell you the first thing that it symbolized is that, uh, I'm a follower.

I'm a follower of my co-founders, my CEO and for the good reasons, uh, you know, the success comes second. The first thing that comes is, uh, really the people Yeah. And their values and what they bring.

And I follow them from the previous company to Jfr. And not only that, as you mentioned, right? I follow them between, you know, moving from one country to another, um, and 14 years later who would believe that this is where we are at an amazing journey.

It's Been an amazing journey. An amazing journey. Definitely.

Definitely. But I've, I've always told Shlomi this, you know, I have a good seat. Not today, this is not as comfortable, but I have a good seat of the DevOps world.

Yes. And I get to talk to a lot of all the companies in DevOps for my money, JFR G's culture that they've built. And, and it starts with Shlomi and, and comes down the, the culture at Jfr is really when, when they talk about the frogs right?

They talk about that feeling of we're on a mission together. Yes. It's very strong there.

And, you know, uh, my personal story is a little more unique than you can find in the market because I actually moved from HR to sales first time with J Rog. Really? That's really the story.

Yes. And, um, I think that one of the special thing about it is that, uh, it's all about people. So I'm here now at Q Con following a few weeks of events, as you mentioned, right.

Traveling quite a lot. But the last weeks I was, uh, hosting our top customers events in Europe and in the us uh, speaking with our top customers, hearing what is it that they have to share with us? And it's all about relationship building.

Yes. But from there to last night, frogs and friends hosting the, uh, community leaders Yeah. Together with you.

Definitely. That was an amazing event. Thank You.

And it's really, it's all about here getting the opportunity to share the highlights of our roadmap here. The feedback from the audience and now here in this, uh, exhibition hall to hear the, uh, the, the pains and the challenges of the users is something that drive our growth. This is what really fuels our growth.

So it starts with this value of people to people Right. And the relationship and being a good listener to what are your pains? What are your needs?

And then from here, put the investment in the right areas to meet your commitment to your audience. Absolutely. But that's, that's kind of the secret sauce.

Jfa, you're not supposed to tell anyone. Oh, yeah. But, um, but it, it's true because it, it is a very community led, customer focused kind of organization.

As part of that, you guys recently did a study Yes. Of kind of what were the priorities? What were the, the, the, the aims, the goals, the technologies that customers want to do, you know, have more success with, more involvement with Yes.

Tell us about it a little. Yeah. So there are, you know, I mentioned before, being a good listener and collecting the feedback and understanding the needs.

Uh, one of the, the additional things that we've done is that we met with CIOs, of course, our customers, but we then we actually reached out to our, uh, partners on the, uh, banking side, the, uh, research side of the banking. And, uh, we asked them to share with us the survey of 2024 of what is, uh, CIO's top of mind, right? Mm-Hmm.

And the amazing thing, and we sent, again, my team analyzed the, uh, the, the results of the survey, um, by six or seven different, uh, banks. And part of what I was looking is to figure, you know, eventually the audience that we have here at CubeCon will get priorities from the top as well, right? This will come from the C 11 CIOs.

We wanted to understand what is top of mind for them. And Ellen, this was music to my ears. Everything that Jfr put the investment in the past five years is in full alignment with what is top of mind for these CIOs.

And I'm talking about the platform play, right? Yeah. The end-to-end, um, uh, DevOps solution, the end-to-end software supply chain management solution.

I'm talking about the multi-cloud strategy of the CIOs. Mm-Hmm. I'm talking about security is a key, definitely a key for their success.

And now these days, ai, right? Definitely Everything is ai. You know, we are, I haven't announced that here, but we're working on a big project, uh, Mitchell Ashley, who's right off camera heads our research, uh, a, a research study on what we're calling DevOps next.

And it's exactly what you are saying is 10 years ago when I started this, 12 years ago, 13 years ago, when I first got involved in DevOps, there were a lot of point solutions that were cobbled together, right. To make it kind of work. But it, it didn't work seamlessly.

So you had Artifactory and you might've been using Maven and you might've been using Jenkins and you know, all of these things. Yes. What we're seeing now is the maturation, the maturing of the space where you need that end to end platform.

Correct. People don't want to cobble together like, um, uh, you know, uh, a mishmash. Yeah.

And, um, this, this is the state of the art. And now on top of that, you get this whole cloud native. Oh yeah.

But you know what's interesting, the interesting thing that is coming from this survey, and what we also hear from the field is that they know these CIOs. They know they are going to adapt even more tools, especially by the way, in the domain of infrastructure and security. Yeah.

But here's the, the amazing thing. 95% of them are saying that they are going to adapt more, but search for ways to how to consolidate, right? So if you are, and looking at the security space as an example, if you have a security platform that you are able to provide me the end-to-end solution on my DevSecOps journey.

Great. And part of our, again, support to this, uh, community, to this, uh, customers, is really to give them this end-to-end solution. Definitely.

So what they want is more functionality, more feature set, less Vendors, but Less providers. Yes. Right.

Providers we don't want Yes. It it because it, it just introduces too much complexity into the whole, the whole system. Yes.

And look, again, this is part of the journey. I talked at RSA in, uh, two months, we're gonna be discussing this at the DevSecOps thing, which is DevOps is DevSecOps. Yes.

DevSecOps is DevOps. Yeah. Um, So you mentioned CIOs.

A lot of people at home may, they, they think of CubeCon, they think of a lot of DevOps software, open source, some of it is free, it's downloadable. I get my hands on it and I use it. And that kinda lends itself to a bottom up Right.

Kind of sale. Right. But the truth of the matter is, in talking to people like you, is these platform sales and stuff are much more top down.

Correct. But here is the thing, Ellen, and you, you know, last night when I attended the Frogs and France, right? Some folks ask me, well, you are Jfr, CRO, why are you here?

What's in it for you? The community, the developers? And my answer is, uh, very simple.

First of all, as we mentioned at the beginning, I was raised by open source people, right? At Rick, and you have Landman, Uhhuh, that's first and most of, but it's really eventually, even if I'm not looking at it as, uh, what is the right sales motion for me, going back to people, if I want to enable the right solution for those who has the pain, I need to be able to open the doors for them from the top down and bottoms up. Right?

And this is how I look at it. And therefore, you will see that J Rog is investing in the platform not to just be able to bring the, a good pitch for Oh, we have an end-to-end solution. Each one of our teams, each one of the solutions, we are looking at them as best of breed, making sure that they are top in the industry.

Yeah. And with this approach, you are able to bring, to answer the need and the concern of the C level, but also provide solid products for the field. It works together in my Field.

Absolutely. So it's top down, bottom up, it meets in the middle and it all goes hopefully well, right. Hopefully.

Well, yep. Yeah. Speaking of that, look, I, I am sure our audience knows Jfr was one of the first public DevOps companies.

That's right. Right? That went public.

That's right. How has that changed your job? Well, whew.

Um, I mentioned before that this is a 14 years, uh, journey, but the last three, now almost four years since we went public, um, I would tell you that I think that number one, number one thing is the fact that, you know, that everything, that the responsibility you had so far, and I always took, don't get me wrong, I always took my responsibility very seriously. Oh, I know you did. But at this point where you have all these investors that are actually trusting you, and you are responsible on a quarterly reporting, on a quarterly commitment, this makes the level of, uh, my commitment to jfr, to the success, to the success of the customers, even, even definitely higher.

Uh, but it also brings, part of what I find is that to our customers, it actually bring more trust. You know, Ellen, these days, everything is about trust. Yeah.

And the fact that you are not anymore a private company, and you don't know what's gonna happen tomorrow, they know that we are, we are there to support them in this journey for in Every the way To ride to infinity. So this is the privilege that I have when I know to say to, to tell again, our users, our customers. Hey, Jeff Rock has a really good, uh, back definitely a great experience actually.

Yeah. I wanna ask you another area. Yeah, sure.

So you mentioned you weren't always a salesperson. Now with salespeople, there's, there's different schools of thought. Yeah.

One is salespeople are born not trained. Another is salespeople are trained. Right.

Anybody could be a sales person. Can you guess me, My type? No, You're not gonna Guess.

You try to break the mold. I, I think you're born my personal opinion. You're born.

All right. But you did it, you were an HR person as you mentioned. Yes.

Switched over to sales. And it's one thing to be a salesperson, just because you're a good salesperson as a born salesperson, sales doesn't mean you're a good sales manager. Yes.

That's also a different set of skills. Right. How has that been for you making that switch to sales and also managing a global sales team for a public company?

Right. Right. So I'll tell you a little secret.

When Shlomi offered me my CEO to join Jfr at the beginning, I told him, you are five people. Why do you need an HR manager? Right.

What do you need me for? And then I joined him to really support him, uh, with some leads he got from Java one back in the days, um, when finally, you know, he made the formal offer of, uh, me staying in this in the in sales. I told him, no, I'm not.

And the reason was because for me, it was a no go to, uh, be in a space where, um, you know what they, you know what many people think when you say sales men or saleswoman, right? And this is not so much aligned with my, my values. And then when I told him that, he said, why do you think I want you for this role?

That's exactly the reason why, you know, Ellen, in our coex, we have our coex is the set of values of J Rog Mm-Hmm. One of the values is, uh, community and customer happiness. Yeah.

And when we, when we chose this value, it took us really time, okay? And it was, by the way, raised by the, uh, the team, by the frogs, as we call them. I remember.

And when we were, uh, looking for what is it that we feel, it was not about customer success or customer satisfaction. It was about customer and community happiness. And for me, this is the only reason that I managed to take this move and move from HR to sales, was because I knew that this company, these people are treating first of all the people and only then the business.

Right? Now you are right. It's one thing to be a standalone, right?

Uh, An individual contributor, Right? Right. In this role, it's a different thing when you need to manage the entire organization.

And especially as the level of complexity is definitely right, uh, rising with, uh, becoming a public company. But, um, if I wouldn't have, that's the truth. The support and the ecosystem of all frogs around us, and the connection from the understanding of the commitment from the product side all the way to the financial side.

I, I, I will be very honest to tell you, it's not a one man show. No, not at all. It takes, it takes a, it takes a whole bunch of frogs.

Oh Yeah. A whole bunch Of frauds. A village of frogs, Right?

But when I'm sitting with our product team, and I know that when they plan the roadmap, it's not about, oh, we know best. It's about collecting the feedback, right? Getting the, uh, the, the, the information and the need from the field, from our customers.

I know that they feel the pain that my customers feel. I know I'm in good hands. I know I can manage it.

I know I can go through this challenge. Well, if you're a J Rog customer, you're in good hands here with Tali Tali, I want to thank you so much for coming on. I know you're busy.

It's a crazy, crazy event here. It's a great one. But you Know what?

As they say in Jfr, may the frog be with you. May the frog be with us. No, thank you, Ellen.

Thank, thank you for that. Tali Nachman, uh, CRO Jfr Live Act con. We'll be back in a minute with a another.

Guest. com is the leading resource for news analysis and education on challenges facing industry. com covers all aspects of cybersecurity, including data security, DevSecOps, cloud security, application security, network security, security threats, and more.

com has the largest selection of security content featuring breaking news, blog posts, podcasts, and more. com to learn more. com, home of Security Bloggers Network.

Next up, we have an episode of the digital CXO podcast. In this episode, uh, our own Amanda Ani and Mike, uh, bazar, speak with Meredith Graham, chief people Officer of insano, about the findings of their recent Speak Up 2024 work survey of 1500 women in the tech sector. There's some surprises there, but a lot of it you can probably guess.

Next up we're gonna go back to CubeCon and our own Mitchell. Ashley speaks with Cheryl Hung senior director infrastructure ecosystem at Arm. And we're gonna discuss how ARM is powering the future of cloud native AI and the cloud native community.

Hello and welcome to the digital CXO podcast. I'm excited to be here today because we have our first guest, Meredith Graham of Inno. She is the Chief People Officer.

How are you doing? I'm doing great today, thank you. Wonderful.

And also with us is of course, Mike Baard. How are you? I'm doing well.

I'm excited to talk about this topic, but, uh, maybe a little scared at the same time. So to begin, Meredith, can you share a little bit about in Sono first, and, uh, then we'll talk about a recent survey that y'all did? Yeah.

Great. So, um, in Sono, so we have about, um, over 3,400 global associates, uh, and we are a managed service provider, and we provide services to Fortune 500 companies. Um, and we provide ser those services mainframe to public cloud, um, and digital applications.

So I'm really excited to be here to talk today about, um, AI and how it's impacting women in tech. Awesome. So with that being said, y'all recently released the Speak Up 2024 survey, and this was a survey of 1500 women, um, across, uh, three different countries that are working in the tech sector.

So you mentioned ai, so we can start there. There was a lot of information, but we know that's the big topic of the day. So what are some of the key findings in the AI section?

I think, um, which was very interesting is, is that of the women surveyed, a large proportion of the women, um, were really taking the lead in AI and what AI means, um, within the tech sector and for their careers, which I thought was very interesting. Um, and then there was also a lot of women, women mentorship as well. Um, so, so that, um, in and of itself, you know, leads me to believe that, you know, women are truly interested in this next step and this next phase of, of, of AI and how it's really gonna impact their world.

And then the future world. It seems like to me there's a bit of a difference though between the building of the AI models and the using of the AI models. And I bring that up because we've talked in the past with num, numerous folks about the STEM education issues that women encounter, and they kind of disappear from math departments in seventh grade.

And, um, there's a lot of men building the AI models, and I don't think they're deliberately injecting bias, but there's cultural bias that gets injected into the model itself that then manifests itself way out to the use cases. So, you know, what's your sense of, have we thought this completely through, or, or we just kind of jumping in and then we're surprised when there are some, uh, suboptimal outcomes, as they say? Uh, no, I think, um, I think we're jumping in, as you said.

I don't think, um, you know, I mean, I think, uh, technology always outpaces kind of, um, you know, legislation and regulations and just kind of the thought around like, what is the true impact to, to the human. Um, and I think that that is why really women do have an interest in it, because I think that they are looking at it, I think that we are looking at it that there is gonna be a huge impact, and there's a potential for a lot of good if you can ensure that these models don't have those biases. But then there's a lot of risk if they do have biases.

So, um, you know, just around, you know, talent development and, and those types of things. And just ensuring equality. If if there's any sort of bias that gets into there, how do you stop it?

And I think that those are the questions, um, that, that people are asking. And I think that's why Ribbon really are interested in, in what it means and, and how they can participate in the development of AI as it is very new. So I remember in the report there was some information about remote work versus in-person work.

And for women, it seems like they're struggling a little bit if they're working remotely. Why is that? And can you share a little bit, uh, deeper information about that?

Uh, I mean, I think, you know, just stepping back, I don't think this has anything to do with, with ai. I think there's just struggles around, um, remote, remote work. Um, and it's particularly, you know, in, in businesses where there are kind of hybrid in person and then remote, um, I think that women in general enjoy remote work immensely.

Um, actually, actually, I think a lot of people, um, do because it provides a lot of flexibility. Um, but there are, you know, inherent risks. So if you have people that are working inside an office and, um, are seeing, you know, the executives or the leaders day, day in and day out, there's always just generally that tendency to kinda lean towards that person that is sitting next to you and having that conversation and not necessarily sharing all of the information that you have with the remote workforce.

And I think it's just something that, um, you, you know, we've been working on since Covid and the world shifted around, how do you ensure that there's a level set across, um, the organization when you do have in-person hybrid and remote? Um, and I think it's a struggle, but I think it's more of a leadership development that, that we've been working on definitely, um, throughout the time. And I think that there's just continued, um, leadership development that needs to occur to make sure that it's level and, and equal.

Now, this part of the survey left me scratching my head a little bit because, you know, I worked in an office and had been doing so for the last year and a half, but prior to that, I spent the better part of a decade working remotely. Um, and as I went through your list, you know, I was like, yep, experience that. Yep, experience that, check, check, check.

I mean, it seemed to apply to both male and female. It's, it's just the nature of the beast. Agreed.

No, I don't, I don't think that there's, um, female versus male. I do think that there's a tendency for more women if it's hybrid, remote, or in person to accept more of a hybrid remote model just because of, you know, caretaking duties and things like that. I think the flexibility around, um, child, you know, taking care of your children or, you know, even, you know, parental responsibilities, I think that flexibility just leans more towards that than men.

Um, but that's, um, just more around those caretaking. But I agree wholeheartedly that, you know, if you're remote as a male and remote as a female, it's probably gonna apply equally. I would say the only difference is, the only difference that I do experience working in the office is then my wife is working from home now.

Um, you know, she just randomly throws stuff on my calendar and expects me to do it. So I'm like, okay, there we go. Well, that's relationships, so we don't wanna go there.

Well, that does bring us to, to the next part of the survey, which was about that work life balance. So, um, what were some key findings there and what are some of the struggles? Um, and what advice do you have as far as balancing, um, you know, the family care and the work?

Yeah, I, I mean, it's, it's always difficult. I mean, again, so women generally have the caretaking duties fall, generally fall, fall on women. Um, that's not always the case.

Um, you know, and so, you know, those, the, there are struggles around the, that work-life balance, but the remote hybrid I think really enables, um, you know, individuals and women to have that flexibility, um, and to be able to do the work that they need to do at different hours and not always be, um, you know, an eight to five type of thing. So, um, I mean, I wish, you know, you know, people with children and would say that eight to five, you know, is, is doable for most, but know in reality. So, so I think, you know, that flexibility in how, and that hybrid really is very important.

And I think, um, you know, some of the mandates that employers are giving around, um, being in the office on Tuesdays and Thursdays, I think that is adversely impacting women because, you know, I think they wanna be able to have the opportunity to come into the office, um, when they can be in the office and what works for them within their lives. So, so, um, I think those mandates are, are, um, adversely impacting, um, a lot of women. And, and hopefully people will look at it as, um, encouraging more women to come into the office, but not the, those mandates.

Mm-Hmm. We were just having this debate on the Textron gang, which is another show that we do. Um, and I think Amanda was involved in this, and we were trying to work out the nuances of, well, you're working remote, but you're kinda have different hours and different times, and it's hard to sync up and kind of get everybody on the same page at the same time when we're all talking about the same thing.

On the other hand, um, if we force everybody to live in San Francisco and New York, we limit the scope of the jobs that are available, and it's probably bad for the economy, and it's bad for everybody in general because we're not, uh, being as inclusive as we might. Um, so how do you kinda strike a balance between those things where we're, we're trying to stay engaged with each other, but at the same time we don't want to, you know, require everybody to work within a, you know, two hour to the office? Yeah, I mean, so, you know, I mentioned at the beginning we're a global workforce.

Um, you know, half of our workforce is in India, a portion is in Europe, um, portion in us. Um, I think, you know, that said, most companies are going towards those global, and, you know, you find those times where you connect and where it's important and you prioritize, you prioritize those meetings, you prioritize these, um, you know, teams meetings, these zoom meetings to make sure that everybody's on the same page to, to mandate. People near offices are also excluding, if you look at the global workforce, you know, huge groups of people.

So you know that India workforce, they tend to work late so that we, in the US can get up early in Europe, can be, you know, in their afternoon. So we can all collaborate equally. So I think it's just setting those expectations around what's important and, and the timeline of, you know, those hours that you have to be available and ensuring that, that you work around that.

I think most people, um, would agree that, um, you know, they can make that work within their schedules as long as they know in advance. Mm-Hmm. What did women have to say as far as the overall work culture?

I know there was some information in there about the culture. Yeah, I think, um, you know, overall, um, there was good news around how I think that women are feeling, um, more connected in the, in the workplace. And, um, things are really improving.

Um, you know, I think, you know, that said, um, you know, some of the feedback was that there's still some areas of improvement that need to, to occur. I think there was some, still some feeling around, um, Mike feeling around, um, microaggressions and, and some sort of, and some, um, limited discrimination. And, and I think, you know, again, it's around developing our leadership.

It's developing the, the, the culture in the organizations to, to, you know, um, break down those barriers. And, but, um, you know, overall, um, based on where we were, um, you know, a few years back when, when we did the first survey to where we are today, I think that most, um, women kind of expressed that, that they saw some po a lot of positive improvements, which was great. Mm-Hmm.

Is it your sense to, you know, that's a marginal improvement or is it your sense that we, you still got a long way to go? Or where are we on this journey before, before the guys run around and declare victory? Yeah.

No, no, no. I would never declare victory. So, you know, and it was only 1500 women, so, uh, it was a sampling.

But, um, I mean, I think it's great though that we're starting to see improvement overall. Um, so, you know, where we were, you know, even 10 years ago, even before we started doing the survey, I think women would, would, um, say that we've made significant improvement, but I still think that there's a long way to go to, to true equality within the organization. Um, you know, and a lot of that too comes from, you know, if you look at the tech sector, there's not a lot of women still in leadership roles.

And, you know, true equality comes from having, you know, um, equal parity, um, throughout an organization. And so there is a lot of work that still needs to be done. Um, but, but that said, you know, there's been improvement.

And so I'm really happy with what I've seen and heard. Wonderful. And I do believe the survey did show that there are more women entering into the, the tech sector.

Is that correct? Yes. Yeah, so there's definitely, um, more women entering the tech sector.

So we definitely, um, and even in Inno, um, we've seen a lot of, um, you know, women joining where we're getting into an equal parity situation at those, those, those junior levels. Um, but again, still a lot of work to, to be had, um, particularly in the senior levels. Do these issues also apply to other forms of inclusion that we're trying to achieve, whether it's LBGQ or, um, it's just simply people of different economic backgrounds.

I mean, um, are we making progress across the board or is it just that we've been focused on this one area, but we still got a long way to go in other areas? Uh, no, I, I think we've made progress in a lot of areas, um, just because there's, uh, such a strong awareness around, um, bringing inclusion into the workplace, bringing equality, you know, um, every study that you read, um, you know, the more diverse you are in an organization, the stronger you are. Um, you know, the, the, the greater revenues, the stronger organizations, um, because you just need those different voices.

Um, you know, bringing in people that are all from the, you know, same, uh, background and, you know, same age, you know, kind of, you know, grew up the same. I, you know, that doesn't lead to a lot of diversity of thought, and I think we need that diversity of thought. Um, so, so, so there's been progress.

It's just there needs to be a lot more and, you know, our survey focused on women. Um, but, but I agree with you. You know, there needs to be a lot more just around the ethnic side, um, you know, and then also, you know, lgbtq plus.

So, And I think the, the last, uh, segment of the survey shared a little bit about what women value as far as benefits, uh, from their work. And so what were the most valued benefits from the workplace? Yeah, I think there was, um, you know, there was a lot around, um, you know, um, skills enhancement and development.

Um, so, so making sure that there's those career paths, um, were very important. I think the flexibility was noted. Um, and I think there was a lot of, um, you know, just information just around also being part of decision making, um, and being able to, to, to, you know, to decide where the organization is going.

And, you know, you know, going back to, um, the beginning of the topic around AI and women in tech, you know, I think a lot of women are interested in that because, um, they wanna be part of that decision making process. Like, what is in Zno going to be doing? What is my company going to be doing with ai?

How can I assist? How can I enable, how can I be part of that? Um, and so, you know, enabling women to, to, you know, lead the organization and help, you know, move the organization, um, and directionally is very important.

Um, is, is what we read in that survey. What's your sense of the political climate these days? 'cause of course, it is an election year, and we have a lot of different things happening in different states.

We are in Florida, and of course, you know, as some of this is a regular conversation here, um, but it's everywhere, right? The Republic of Ireland just had a referendum that changed some of the wording in their constitution about what the fines, the homemaker, and apparently not enough people showed up. So it went down in flames and, uh, much to the surprise of the political leadership.

But it seems like it's a global conversation. And I wonder, does it matter what the government thinks, or should each company just do what they think is right? Uh, from what perspective?

I guess let's, with a little bit of clarity, that's a, that's an open, open question. Very different paths. Where, where are you going?

It's just a sense of, um, you know, should companies be worried or be paying attention to these various legislative initiatives that are floating around? Or should they just kind focus on doing the right thing by what's good for their organization? And, um, maybe there's too much noise in the system.

Uh, no. I mean, I think, um, you know, we, we are heavily in focused on legislation that impacts, um, our associates and how it impacts our associates, because that impacts how they do their work and what they're focused on. And so, um, you know, monitoring what's going on, um, within, you know, where we do business is very important.

Um, so that, that come up from it. There's, there's a lot of divisiveness, um, you know, in, in the world today. Um, and that does impact people.

And so, um, you know, we need to do the right thing, but we also need to support, um, all of the people that work for us in these, you know, kind of volatile times. And so, um, you know, if necessary, you know, we, um, as in Sono, I'm sure would, would get involved in an initiative if we thought it was gonna have a huge impact on our associates, um, and, and the way we do business. Um, you know, at this point we haven't got to that point.

Um, but I think it's something that, you know, you know, organizations and leaders just need to be concerned about because it does have a huge impact on, on how people, um, live their day-to-day lives. Wonderful. So I think we have covered most all the sections of the survey.

So if there was one key takeaway that you could leave our business leaders with today, what would that be? I, I think you just really need to focus on, um, not only women in the organization, um, but just the, the, the minorities, um, in stem, you know, in technology, and ensure that you're bringing them along. I, you know, I said it before, diversity of thought, um, is, is very important.

And that diversity of thought comes from people with diverse backgrounds. Um, and, you know, and gender and ethnicity, um, you know, is very important and you should focus on it, um, you know, and, and continue to develop. Because as I said, you know, we've made great strides.

Um, I, I, when I started long ago, I won't tell you how long ago, uh, you know, it was a very different world. You know, I'm, I can say I'm very proud of where we are today, but, but we still need to do more. So, so continue, keep the focus, continue to support, listen, um, and, you know, just do the right thing and, and help enable, um, you know, those, those diverse, um, associate pools to, you know, come on board into your organization and, and really make a difference.

All right, folks. Well, we hope you're listening to this podcast 'cause that's part of listening, right? So there you go.

Absolutely. Well, thank you again for coming on our show, and thank you to our audience for tuning in, and we'll be here next week. Great.

Thank you. This is Textron tv. Hey everybody.

Mitch Ashley here at Kku Con in Paris 2024. Taking over a little bit for Alan. He's been doing some interviews and I'm jumping in and meeting with some fantastic people, one of which is Cheryl Hung, who's with ARM Work.

Welcome, Cheryl. Thank you so much. It's fantastic to be here.

Good to have you. Tell us about what you do at arm. So, Adam, I'm the senior Director of Ecosystem within the infrastructure line of business.

So that means arm in data centers, cloud, tell, telco, networking That, and that's really growing. I mean, I, I see more and more ARM in the data center, arm in the cloud. It's, it's the fastest growing part of arm.

ARM is very dominant in mobile phones and mobile devices already, but Cloud and of course, ai, the whole topic of this, this conference, it feels, um, are huge drivers for arm, very big. So I'm really happy to be here. I'm curious, uh, so you, you have some connections back though into CNCF before you were with arm, right?

Indeed, indeed. I actually used to work for C-N-C-F-A few years ago. Um, I used to lead the end user community as the VP of Ecosystem in CNTF.

So I care a lot about, you know, not just good technology, but the community, making sure that it's very easily accessible and very usable. Um, and supporting people from whatever big or small organizations or individuals to come and be part of the community. It's nice to have that continuity when you move to ARM and bringing all those relationships and that knowledge, that experience with you.

Uh, it is definitely, like, one of the things I love about this community is we're all friends here. You know, it doesn't matter where you work, it doesn't matter what you are doing today, but the, the relationships that people form here have lasted, you know, beyond what their company's doing, one company, and, you know, they really maintain that. So every A great coupon, at least one person says that to me.

Yeah. You know, so I think it is really true. It is a community.

I Mean, the first coupon that I came to was Berlin in 2017. I'm gonna say it was 1500 people. I was here on a diversity scholarship, so I knew nobody, you know, much smaller confidence, obviously a lot of energy and buzz, but at the time it was still like, oh, Kubernetes and maybe a handful of other things.

And now you look, look at it, look at this Yeah's Kubernetes conference to really being full cloud, cloud native Ommunity. Yeah. And now Kubernetes is so established and, you know, solid.

And that at the time it was still like, oh, is Kubernetes even gonna be a real thing? So I'm very, very ratified, you know, to see how this community's grown. What, so three things, three things I'd love to talk with you about.

I'd love to hear more about how ARM is doing in the cloud. 'cause I, more and more I hear those kind of conversations happen on the infrastructure side. Um, open source project, adoption of arm, and then also of course, ai.

You know, we, you can't go five minutes into a conversation without talking about ai, but very legitimately, I mean, with ARM and AI projects. So choose the order. Yeah.

You can talk about whatever you want. Let's, um, let's go from, let's go from the top. Okay.

So, um, last coupon Oracle announced that it was offering millions of dollars in cloud cloud credits for ARM servers. So offering these two CNCF projects. Um, and the goal here is really to enable CNCF projects to experiment with ARM servers, to test, to deploy.

And the goal is really for all of the projects, eventually to be multi architecture. So independent of what your end users are using, you know, today, you should be neutral towards what architecture, and you should try and support a broader range of end users as you can. Mm-Hmm.

So this announcement that we made with Oracle was really a way for us to show that this is something that we want our community to support and to take advantage of. So part of my goal of being here today is to talk to people and tell them like, oh, what are you doing with these cloud credits? Can we help you?

Can we do anything for you to make it easier for you to access those? Um, so yeah, that's, that's one of my main things. So, so how's it gone since the announcement?

How, how's that adoption happening? What do you see happening with things? It's picking up.

It's picking up, yeah. Yeah. The, the challenge of course is, um, is obviously, uh, smaller player Mm-Hmm.

So far in the cloud native space, it's this Thing called momentum too, right. You Know, you're momentum Exactly, yeah. Gathering, you're gathering, uh, more momentum as you Do this.

Exactly. And that actually takes me to the second topic you mentioned, the open source adoption. We are very, very proud to say that more than 80% of the graduated projects support arm, that is huge.

That is not where we were three years ago, and we would love to get that to a hundred percent. So the maintainers, the TOC, you know, we are really talking to members of the community and how we can get that number to a hundred percent. Um, part of that, you know, supporting through things like cloud credits, part of it's supporting through supporting the maintainers and the community directly.

Um, but it's a significant achievement. Never in mind. ARM is not a software company, right?

Right. Yeah. A few years ago, ARM is like purely in chips and computer hardware.

So for ARM to actually make the stance and say, you know what? The software matters, and when it comes to software, open source software really matters. That is a big change for arm, a big cultural mind shift.

Um, so I'm very, very proud of the company for that. Let me ask you then, so is it, is it just, is it, I'm gonna be simple, simple answer. You tell me if it's this simple, I'm, I'm guessing it's not.

Is it just getting projects to distribute their binaries in ARM and Intel and whatever, you know, chip formats? Or are there more things about how you incorporate design into your software to take advantage of different use cases for arm? So, both.

Both, I would say, um, on one hand, moving recompiling to ARM is actually very simple. Um, put a lot of effort into making the compilers, you know, you basically flip a switch, and in theory it should all work. Of course, that, you know, last little bit is where all the effort is, make it all just work.

There's always anything like that. Sure. Um, so there is a lot of work still left in terms of testing and making sure that software works together in the way you would expect it to.

Mm-Hmm. Um, so, you know, I'm not saying that it's an easy task. There's definitely still work to do there.

And the second part is on particular workloads. So of course the, the hot topic of the day is ai, and AI is driving a lot of the demand for arm, um, and arm in the cloud. In fact, I gave a keynote a different conference, um, last week where I said, if you're not on the AI hype train, you need to get on it now.

You know, I was a little bit of a cynic myself, but, you know, seeing the momentum here, seeing how, seeing how AI is really being used to solve these problems that would've been too difficult or too expensive before. Um, it just goes to show, you know, that's where the, that's where the hype trainers and obviously, um, you know, in collaboration with some other players like Nvidia is a big part of that. So we are doing our best to keep pushing, keep supporting that, um, community, and to democratize the access to GPUs and make it easier for people to use them and run them in production.

Great. Are there, are there certain things, do you need to work with different LLM providers to help them work in an armed environment? Is that important at all, or, that's kind of a little, a little higher up in the, in the stack.

That's just not as big issue If we, a surprising amount of it just works. You know, a lot of it is really, really, um, you know, just works. And that's really a tribute to all of the closed and open source, you know, engineers who've been working on this, um, over the last few years.

There are obviously some things, not so much in, you know, the data, but in the training and the influence that maps need, uh, need some work and need some polish. But honestly, honestly, the, the biggest challenge right now is all of the layers above it. So things like Kubernetes, we saw from the keynotes today that actually there's still a lot of challenges in how you manage those GPUs and share those resources effectively and fairly and, you know, cost effectively from a business side, um, and keep your users happy because obviously they want, you know, the fastest access possible.

And so there's an absolute ton of those. And I say the other big bit that we're missing right now is actually in, in the ecosystem. You know, we don't have necessarily, the talents policies are changing really fast.

Mm-Hmm. Governments are figuring out what to do about this whole AI thing. Um, and the hardware is still difficult to access very, they're very Expensive.

We're currently on all parts of it, it seems like. Exactly. I mean, ai, mls been around for a while, but especially Journey AI is all they do.

Most of us, You know, it's, it's, it's been around, but it's been around for the very big tech companies. Right. You have the deep pockets.

If we're gonna get that out to the widest range of people and companies possible, then we need all these things. We need the money, we need the talent, we need policies to support it, and we need the hardware to be accessible as well. So we definitely have a long way to go, but it's really great to be at the beginning of the journey and, you know, see where we land in five years.

Yeah. You do kind of have to be there at the start, you know, to really be part of the whole ecosystem. Definitely.

How, how about, I'm curious, you know, there's a lot of discussion about, uh, demos, domain specific LLMs, um, geographically based LLMs that are at the edge. Now. We're talking about just gen AI at the moment.

It seems like those are, you know, really particularly great use cases for ARM as well as, you know, more efficient user resources, sustainability Yeah. As a big issue around ai and it's, it's, it's kind of elevating sustainability even more as a topic than it already is. Yeah, 100%.

Um, ARM has always had this focus on, um, being energy efficient, right from the beginning. That was, you know, the goal was these very low energy chips, and that served it very well in mobile devices where, you know, you want to have your battery live for your phone last all day, but turns out that's also wonderful if you have massive data centers and you are using an enormous amount of, of energy Mm-Hmm. Then using that energy effectively is super important.

So many businesses, you know, almost every business I'm talking to has sustainability as one of its long-term goals. And one of the best ways to do that is to make sure that you're using your energy and, you know, it's also good for the company, right? Because you energy costs money, you're spending a lot on your cloud resources, so you wanna get the most out of them.

There's good, good for the company, good for the planet, and good for people. Excellent. Yeah.

Well, uh, so wrap up with kind of more of a, a you question as a veteran, coming back to Kub Con, having gone multiple years, multiple events, is there any particular meetup or group or event or part of Kub Con, like is the top of the list of, I always show up at the Wednesday, whatever, you know, or is there something like that for you that you really enjoy participating in the most? Um, I would say on a personal level, um, I run the cloud Native London Meetup. I've run it since 2017, so, gosh, seven years now.

I've been running this meetup. We have over 8,000 members, and I always love to meet the meetup organizers from different countries. Mm-Hmm.

So, you know, the one in Paris, the one in the Nordics, you know, the one all over. And I, I love meeting up with these folks because they're always really passionate about getting people together, you know, learning new tech, sharing what they know. So for me, I think getting in touch with the Amba other ambassadors is key.

And then, yeah, just hanging out with people, you know. That's great. I think it's always good to meet people you don't know as well.

It's, you meet people you don't know. And, and, you know, it's, we're Covid iss somewhat in the rear view mirror for, for us, but I'm still meeting people. I've only met online and like, yeah, just, just someone walked up today and like, Hey, I finally get to meet you in person.

We're still making that personal connection. Just two minutes ago I saw someone I knew and I was like, oh, after this is done, I might have to catch up. I gotta Go try to catch them.

Exactly. Well, it's been fantastic talking with you, Cheryl, and, uh, we usually the best, and I'm excited what our, what arm is doing in the industry and the adoption level. You know, I have one more question, if I could Ask you.

Yeah, go for it. I have this theory, I don't know how big of an impact it has, you know, being developer in my background and, and long, long term Apple users, when Apple moved over to an arm style chip, at least that instruction set, it seemed like that kind of brought a whole new developer community into the arm world needing ARM software, all the things you're talking about, of course. Which helps when you move it into the cloud and into the data center.

So it seems like moves like that where developers are working and the software they're working on is seems, seems like one of the things that will help accelerate that. Is that true? It is very true.

It's, it's actually a little bit embarrassing how often I hear, you know, oh, I moved my company to Arm because I wanted to use my Mac with my fancy new M two. Hey, that's what gets them there, right? I'm Like, sure, yes.

You know, that's, that's great. Um, I actually used to work for Apple before Arms, so I've also seen that from both sides. Um, but you know, it just goes to show that, you know, arms growth is everywhere and it's wonderful.

And it, it also really goes to show how developers are leading the charge. Mm-Hmm. You know, a lot of things, you know, like, yeah, okay.

Sustainability might not be something the engineers care about day to day, but they care about their battery life. So we're all heading in the same direction. Excellent.

Well, Cheryl, it's been a lot of fun. Cheryl Hung, who's with Arm, you wanna know something about what's happening in the cloud or CNCF or Apple or Arm, not to, not to overload too many expectations on you. It's been great talking with you.

It's been Wonderful. Appreciate you coming by, been wonderful talking to you. Thank you so much, Mitch.

You Too. Have a great show. Thank you.

Be right back with our next fantastic guest, just like Cheryl. And I hope you stay tuned. We're just getting started here at Kku Con in Paris.

See you in a minute. I'm Bonnie Schneider, sustainability contributor to the Techstrong Group. I'm excited to introduce you to a groundbreaking new initiative from Techstrong Research, the sustainability pulse meter.

The pulse meter offers valuable insights into how environmental responsibility factors into tech purchasing decisions for key players in the industry. Position your company as a leader in the industry and differentiate from your competitors with a sustainability pulse meter offered exclusively from Techstrong Research. How about some views with Bazaar?

We got two of them for you today. And this first one, Mike speaks, uh, with Aerospike, CEO Suebu IER about an additional $109 million in funding Aerospike picked up, and why a larger shift to real-time applications is driving the need for multimodal. This is Textron tv.

Hey guys, thanks for the throw. We're here with Sabu Aire, who is CEO for Aerospike, and they're fresh off of a investment of $109 million that's on top of what they've previously managed to recruit from folks. But we're gonna talk about what's driving that, because they're a pretty big player now in this whole space of graph databases and vector databases, and they're used for slightly different things and how all this is coming together.

Suebu, welcome to the show. Thank you, Mike. A pleasure.

What is your assessment of what's driving all this? I mean, on the one hand, it seems like vector databases are closely tied now to the boom and AI graph databases may be tied more towards, uh, knowledge graphs and visualizations, but it feels like both are being lifted. Are the two related, or are they different use cases, or how does this all come together in your mind?

So, Mike, before I answer that question, just a little bit of a background. So, you know, what we see is, you know, realtime access to data and realtime decisioning is actually now prevalent within every industry. You know, Aerospike was founded on the premise of really making, you know, realtime data accessible at high performance at, with any scale of data, right?

We started our journey with Ad Tech, but now we have customers in every industry, financial services, telco, e-commerce, retail, gaming, and entertainment, healthcare. So really what we have seen is the, you know, the adoption and the desire to harness data at any scale in real time. And, you know, we've been deployed in, in a lot of customers grow globally already for several years within their AI and ML pipelines.

So, you know, as you mentioned, obviously Vector has become a topic of conversation ever since, you know, chat. GPT became available last year. Uh, but we've been in deployed in customers across their AI and ML use cases, whether they're fraud analysis, recommendation engines, uh, profile stores, so on and so forth, uh, for the last several years.

So what we see out there is really, you know, a desire to actually act on a lot of data. So if you think about ai, I think AI begets more data because what we hear repeatedly from our customers is that more data actually drives better context and better accuracy to their AI systems and their AI results. We were built from day one to handle data at scale while delivering real-time performance on that data.

So we are, we believe AI is a perfectly suited application for us, if I can call it that. And, you know, we were, we were ready for the AI age. It is just that, you know, over the last year, as you talked about, there's a lot, lot more, you know, desire to actually implement AI within systems.

Mm-hmm. As far as graph is concerned. Uh, the second part of your question, we entered the graph market last year, and, you know, we are, as you correctly pointed out, a knowledge graph that supports both use cases like identity management and fraud.

And, you know, we saw a, you know, opportunity in the market, again, based on customer feedback, who wanted to actually deploy graph at scale. So the existing solutions that were available for graph, uh, really didn't scale much. Uh, so we came out with a solution which can, you know, support billions of vertices and trillions of edges.

Um, so this is the, you know, class of kind of graphs that we are supporting for our customers. And we've got a, got a lot of traction. It's not just the scale, but that size of a graph we can traverse on a multi hop query in, you know, single milliseconds.

That's amazing. You know, when we talk to our customers and we, when we see the market out there with ai, you know, and the desire to actually use more vectors to generate those embeddings, and whether it's within generative AI or predictive AI use cases, actually leverage vectors. We are seeing that happen, uh, as far as the adoption of vector and vector search is concerned.

But we also believe there's a congruence, if you will, where graph and vector can exist independently to power some of these use cases and they can also come along. So if you talk about a simple example, if you're trying to look for a specific document, let's just say using a vector search, a a vector kind of search with vector embeddings is probably the right way to go. But if you're looking for documents which are similar in nature, for example, which is, you know, for lack of a a word, let's just say it's a corpus of similar documents, then you wanna use a vector search, which is then augmented with a graph so that, you know, the graph can then actually build relationships and build our similar documents that are available.

So that's how we see this entire field of vector and graph coming together and evolving over the next several years. There's much to unpack there. So let's jump in.

Let's start with real time. So we are clearly seeing that there is more data being processed and analyzed at the point where it is created and consumed, but then it needs to be fed back to something else in near real time. Is this changing the way we think about building our applications?

Because historically everything was very much batch. So, you know, do we have the developer skills to do that? Where are we on this journey?

Absolutely. I mean, you know, some of our, uh, leading edge customers are already doing that. As I mentioned, uh, before, you know, we've been deployed in AI ML pipelines for several years across, you know, uh, our global set of customers.

Um, what we hear repeatedly from prospects and customers we talk to Mike, is the desire to actually use more data in their AI or other kind of applications. And the limiting factor for them is one of two things. One is really scale, which is, you know, they look at systems and solutions, which actually hit a scale wall.

And the second is really the cost. So it becomes very prohibitively expensive for them to actually keep all that data online, if I will. So we actually, once they talk to us and they discover, you know, the art of the possible with Aerospike in that Aerospike can support this infinite scale, whether that's hundreds of gigabytes or even petabytes of data without compromising performance.

And at A TCO that is the lowest in the industry, right? We run on hardware footprint, which is 80% less than anything else that is out there now, that actually opens up a lot more possibilities for them. And as we kind of get into more and more AI applications being built for more context, better accuracy, as I mentioned, they're gonna want, you know, all that data to be available as quickly as possible.

You know, we, we are talking to customers right now who are telling us, Hey, I'm only keeping 20% of my data online because, you know, these same things because it, you know, my platform doesn't scale or it's pretty expensive, but if I have a solution, I would love to keep, you know, if not a hundred percent, at least 80% of my data online. And then it's not static dataset, right? It's also coming in where you are ingesting data.

Let's just say in the case of vectors, there's vectors which are coming in at a rapid rate. So can the platform actually support, you know, high throughput ingestion and then make it available, build the index of the vector as this new data comes in and have it available? AI application or the decisioning systems.

These are the kind of, you know, key kind of considerations that our customers have. So to your question, absolutely, if they could find systems like Aerospike, which can actually help them process data online in real time, they would do a lot more online. And, you know, several of our customers have done exactly that.

They've gone from nightly kind of decisioning to first going kind of, you know, several hours a day. And then now they're, you know, some of our customers are doing it every few minutes. That's just changed the trajectory on their business.

Do we have the skills to manage those types of databases? 'cause historically we've had relational databases and then document databases. Um, a lot of folks I talked to, you know, vector databases have been around for a while, but until AI came along, they had no idea what a vector database was.

Do we have the folks that required to manage that? Is that, uh, a new type of DBA? Is it a data engineering team?

Where are the skills? Um, so, you know, skills, uh, we see, you know, two, two kind of, um, kind of areas of, of investment here. One is, you know, larger organizations do seem to have the talent and the skills, and then other organizations rely on partners or look to us for the expertise so that they can actually onboard and train their workforce, uh, around these skill sets.

Along with that, you know, it's our job as vendors and we are investing a lot into this, which is making it easier to manage these databases and also help the developers build easily. So let me give you two examples. We are investing a lot in observability and management.

So as you know, more data comes online and is managed under Aerospike. We wanna make it easy to really manage that database, you know, whether you're scaling up, scaling out, you know, whether you're trying to understand if something actually pops up as an issue, we should be able to be proactive, um, in, in terms of alerting the, uh, user, uh, so that they can take the appropriate action. So that's one area that we can make life simpler for the operators, if I can kind of call it that.

The second part is really the builders and the developers. So we are investing a lot in terms of, you know, making it easier for developers to find resources. So we built out a developer hub last year.

com, there's tutorials, there's sandbox sample applications. We have a community forum, which is actively actually responded to by our team. And then we also are making it easier from an API perspective, so more simplified APIs, you know, using frameworks like Spring Data if you're a Java developer, uh, using Link Pad if you're a dotnet developer.

So that, you know, you know, our graph solutions supports gremlin as a query language. So we are trying to get it closer to the language of the developer and have simpler APIs. So for both, for the builder and for the operator, our mission is to make, our mission is to make life easier.

And, you know, as simple as possible, Might we not use generative AI to make it simpler to manage the databases that we're using to drive ai? Absolutely. So, you know, we are investing in efforts, uh, exactly to your point so that, um, you know, we don't, you know, we want to give the operator the control, whether they want, you know, decisions to be made automagically on their behalf or based on certain policies, or we just want human intervention so that we alert the human.

But you're absolutely right. I think, you know, part of kind of what we are gonna invest and build out is exactly what you talked about so that there is, you know, a self remediation in certain cases and or alerting, which is propped by ai, uh, which is, you know, built into the product. How do you see the way IT teams are organized kind of evolving?

And I'm asking the question because in this day and age, we'll see data scientists hanging out with developers and DevOps, and then I've gotta talk to A DBA and there's a data engineering folk, and there's probably a couple of cybersecurity people thrown into the mix as well. Um, is there a different way of thinking about managing all those things more cohesively? 'cause right now we have just had a lot of fiefdoms over the years.

Um, we see two models evolving there. You know, some of our, our customers actually have what they call center of excellence and a shared services team that stands up and manages kind of the database and infrastructure services and have makes them available, uh, internally within the lines of business and through the developers. Um, other organizations, maybe, you know, medium sized to smaller sized organizations have, you know, all of that actually built together into a singular app team, if you'll, so the person who owns the app owns not just the build face of it, but also the deploy and the operate and manage phase of it.

Maybe different people and different roles, but it's all part of the same team. So we see two models kind of, um, emerging, um, where, you know, when, when you have multiple lines of businesses, you know, larger organizations, they tend to go for a center of excellence or a shared services model. On the infrastructure side, You guys have garnered this latest investment round.

Are there any priorities for that kind of allocation here? I mean, what's top of mind for you? Yeah, so I mean, I think we've been, um, you know, incredibly lucky Mike to actually attract, um, you know, attention and, you know, request from a bunch of, uh, investors wanting to actually join us in the next part of our journey here.

Uh, we decided to go with sumero. Uh, we love the team. They got exactly what we are trying to do and our vision for the future.

Um, so, you know, really thrilled to work with the Sumer team. George Kfa, uh, who's, who's been, you know, in the industry for a long time, has joined our board from the Sumer team. He is a co-founder and managing director.

So, you know, personally, I'm thrilled, uh, to be working with, uh, George. Um, the core areas of investment are r and d, primarily around ai. So we talked about Vector, we talked about graph.

We wanna make it where the Vector database and the vector search from Aerospike is natively integrated to the workflow automation and the, you know, models that are available on the Dent Cloud vendors, whether it's A-W-S-G-C-P or uh, uh, Azure, uh, so that, you know, our customers don't have to stitch these things together on their own. And, you know, it's seamlessly integrated. So there is a lot more investment around ai.

Uh, the second area is cloud. We released, uh, our database as a service product last year in Q4, and we continue to build out our entire cloud portfolio. So, you know, we are investing in that, um, the, and the outset of r and d, um, you know, it's go-to market.

So, you know, we are trying to expand our Go-to market efforts, both in term of direct sales, um, and also channel, uh, so that we have more reach and coverage across the globe. So, you know, r and d and go-to market are the two primary, um, areas of investment. Seems like there's this ongoing debate about whether or not I need a dedicated database for a particular use case, or if I can extend, say, my existing, uh, relational database to other data types.

And we've seen that going on over the years and, and it's now playing out again, especially in Vector. What is your sense of, you know, when do I need standalone? When can I use something that I'm extending or are, are there legitimate use cases for both or is it one or the other?

So I truly believe that, you know, the world is gonna go towards a multi-model database. That's what, you know, we have built out and we already always strive to actually build out. We started with key value, we added support for documents, then we added support for graph, and now we added support for vectors.

Um, the reason is, you know, one of the things is what you mentioned, which is skillsets, how many different database technologies, you know, can a particular developer or your team learn at some point in time? You know, you wanna make sure that you're able to leverage the skills across a broad set of use cases. Whether that use case demands that you use a vector or a graph or a document data type under the covers.

I think that's gonna be an important consideration. Uh, you know, the entire database and data field has been pretty dynamic and, you know, obviously, uh, folks have chosen, uh, the best, uh, or what they consider the best database for a particular use case. And that's why you have such a dynamic ecosystem of vendors, uh, that have evolved.

Um, but I think if you can deliver the core value propositions that I talked about, which we believe we do, uh, with real time performance, you know, low latency availability, and you know, lowest TCO, that's to a bunch of different use cases. And, you know, data model happens to be just something that supports a particular use case. Uh, so, you know, we think the world is gonna get to at, at a point in time where they will have a fewer set of databases and they'll build a bunch of different use cases on a multi-model database.

Whether, you know, SQL and NoSQL come together, you know, I don't know, they seem to be two different worlds right now in terms of what the sql, uh, databases are doing. And, and kind of the NoSQL databases is where the energy and the growth is for the most part. So what's your best advice to folks?

What's that one thing you see them doing these days that just makes you shake your head a little bit and go, folks, we need to be a little bit smarter than that. I think, you know, um, what I see out there, Mike, is, you know, we tend to resort to a path of lease resistance. So I would encourage people to do a little more research because before you choose a platform or a particular solution, you know, look at the alternatives, look at what's available and what's gonna stand with you for the next 18 months, for the next 24 months of your journey.

'cause re-platforming is gonna be very, very expensive for most folks. So if you start your journey with a solution, which may look okay for you right now, as you grow and you start kind of, you know, leveraging more and more aspects of that particular solution, you'll see that you'll start hitting a wall either on a data wall, a scale wall, rather I would say, or a performance kind of, uh, issue or a cost issue, which is it starts getting prohibitively expensive. So do your research, uh, before you actually start that journey so that you look at a particular solution or a platform or an infrastructure can that can stand its time for the next several years.

Uh, you don't wanna be replatforming in 18 months or, you know, 24 months. So that's kind of the, you know, big advice that I would actually put in front of, uh, folks. All right, folks, you heard it here.

There's an old saying that says, you know, you date your hardware vendor, you marry your software vendor and it's still true. Hey, suebu, thanks for being on the show. Great.

Uh, okay, once again, great catching up again, Mike. Thank you so much. Alright, and back to you guys in the studio In our second few with vard today.

Mike speaks with Lee Foss global field CTO for GitLab, and they're gonna delve into the complexities of finops finops, shedding light on the challenges of managing cloud cost effectively. This is Techron tv. Hey guys, thanks for throw.

We're here with Leaf House's global field CTO for GitLab, and we're talking about ops. It's all the rage these days, but it's not clear everybody knows how to do it. Hey Lee, welcome to the show.

Thank you so much, Mike. Appreciate being here. What do you, what do you think is going on with finops?

Because I remember back in the day we had capacity planning and there used to be finance teams and IT folks would walk around and figure out how much they were gonna spend almost out of the penny, and it was a fairly exact science, and then the cloud came along and did we just forget, or are we kind of relearning some skills that we lost sight of? I think it's a combination of both. Um, one of the items that we see from our customers is there's two different buying models that you have for products and services.

So when they're on the public cloud, they're learning how to adjust to things like commits and consumption based pricing for some things. So if I've got a database and I've got a bunch of storage that needs to be to back that, that storage is something that they get charged on a month by month basis, where you then have other products that charge by user. So the procurement teams are trying to figure out how do I blend these two models to make sure that I'm meeting the financial targets that they have internally while still capturing things like return on investment and total cost of ownership.

Do you think folks are being surprised as they do that? Are they spending more money than they initially thought? I mean, I kind of liken it to my television set where I have all these different apps that I'm subscribing to now and I'm probably spending more money on television than ever.

Uh, I agree with that. I I run into that same situation with, uh, uh, two girls in the house and very surprised at the end of the month when you get the bill and you start to total things up. And, um, I think that is something that's running with, uh, our customers today is, um, they have targets that they want to meet for their budgets, for the applications that they're building and deploying.

And those applications, when they become wildly successful, um, they're not targeting for what are the additional services that they need to, uh, generate budget for. So items that I, a lot of customers are surprised on are things like egress costs and being able to move stuff between a data center and into the public cloud. These are things that you normally wouldn't have to budget for if you were just targeting your own data center.

So there are some surprise costs that do pop up, but at the same time, um, as we look at what the overall lifecycle looks like of an application, they only target things like what does the production infrastructure look like? And now that we have these ephemeral environments that we can spin up and tear down, and we're not tied to the old legacy hardware that we used to have in our data center, even with virtualization technologies, we never felt really comfortable just tearing things down, spinning up new environments. And as they're doing that, they have a lot more flexibility in how they do things in dev test, a lot more performance testing.

They're able to run through their regression tests. Uh, there's just so much more that they're able to do that they just weren't able to do before. And that's where some of these surprise costs are coming in.

There's a lot of different models in the cloud. Um, and you hear a lot about spot pricing, but I, I also talk to folks and a lot of 'em don't use that simply because it's a lot of work to figure out exactly what the optimization is for particular use case at a given time of day. Do we need another way of thinking about kind of capacity on demand versus an application that runs more consistently?

I mean, is the model kind of broken? Yeah. I remember back when, um, I first started building applications and running my, uh, engineering teams.

You know, one of the things that we did is we had profiles for applications that we were building. Are they memory intensive? Are they CPU intensive?

Are they IO intensive? Is there a blend or a mix? How do we go ahead and target the correct infrastructure for the correct applications?

And now a lot of people look at things inside the public cloud as just general compute type resources. So I think as the cloud is evolving, we're starting to see more and more customers treat it as a platform rather than just infrastructure services. So they're able to get an economy of scale in the cloud by leveraging services that they would normally have to build themselves.

So they're sort of hiding what some of the infrastructure costs look like underneath because they're getting the value that actually sits on top of those resources internally. Can I go after this holistically or am I gonna kind of two step it where I need to, uh, optimize the cloud spend by category? So it might be in your case, uh, DevOps platform, but then I've got storage and to your point earlier, there's a lot of different models and then I gotta aggregate all that into some sort of more comprehensive analysis that lets me get to my total cost.

Is that kind of the, the, the flow and the skills required here or how does this play out? Yeah, it's um, interesting because one of the things that we're asked for from our customers are wanting to know things like when they're running CI jobs. So when they're running those, they wanna know how many minutes a project is consuming of those resources to try to do some sort of chargeback showback type model inside across, um, application owners.

So things that we see in the future is when we start to look at those things holistically, we've away from just talking about minutes and we need to start thinking about the value that these services provide. And that's where a lot of the customers, when we talk about value, it needs to be, unfortunately for them, there is some vendor tie in, but what are the specific services that you can consume that makes it easier for your users to monitor their applications, to be able to do auto scaling, to be able to uh, uh, do things like DNS and auto failover and being able to do hr, uh, ha dr um, inside their environments for their applications. And as they start to abstract those things away, they're now no longer needing to worry about, well, how do I provision a virtual machine?

Or how do I provision elastic storage? So those things are going to be interesting about how we value those services above the individual minutes that we're seeing a lot of people measuring today in their finops environments. Do you think that AI might come along and help us with all of this?

'cause it seems like there's a lot of data flowing around and I need to kind of maybe rationalize and summarize it. So that sounds like generative AI to me, and then I can apply some algorithms and some controls and is that where we're headed? I I do think that there are definitely opportunities for generative AI to step in and provide some forecasting models.

Um, so when I've worked with other customers inside of the public cloud, there's trends that they have. So there's spikes in workloads, let's say at the end of a quarter or if you're, um, selling things online, you see spikes in between the holidays. So how do we build that forecasting model to automatically build future compute spike models that we can scale proactively rather than reactively and optimize that spend even further based on time of year, time of day.

Um, we have people that will be building applications and those applications, they may not have developers and other parts of the world and they may just be America's based. Well, what do I do with the compute resources? Do I have to run them at their high watermark or at seven o'clock in the evening, can I run a minimum amount of workload and then coming in at 6:00 AM East coast time, do I spin those resources back up?

It really comes down to how far do you need to optimize before you start running into situations of, um, the complexity of being able to set up those environments. How many people really know how to do that? So those are things that I think, uh, generative AI can really help with is being able to find those trends and analysis and then automatically building the scale up, scale down type models for those applications.

I don't wanna accuse our developer friends, our developer friends of being quote unquote drunken sailors here, but um, it seems like a lot of times they create a development environment and they don't wanna take it down or they forget about it or they have a bunch of BMS running and they forget about that too because, you know, they think they're gonna use it again any day now and then it just sits there and generates some costs. And then on the back end it seems like, you know, everybody over provisions. 'cause nobody wants that call at two o'clock in the morning and we're not really optimizing the compute infrastructure because well, if it comes down to cost versus, you know, my wife being mad at me because I got roused out of bed at 3:00 AM I'm going with the compute cost.

So, um, how much of this is kind of our human nature that we need to kind of work around a little bit? I I do think that there's, uh, some human nature elements to this. Uh, there's a lot of people, even with how long the cloud's been around, there's a lot of people still somewhat hesitant to moving workloads into the public cloud.

Um, when a funny story, uh, a company that I was working for, um, it wasn't until they generated actual showback of the resources that were being consumed. Normally central it manages the budgets for all applications. And the development teams are, their budgets are really just about the people on the project.

So they'll go to external staffing, they'll go to internal hr, they'll grab their people that they need and they can sort of break it down into what the cost looks like for that application based on the time that resources are allocated to it. They never take into account the actual infrastructure that requires that application to run all the way from issue all the way into production. So they don't think about things like, Hey, what does it look like for CI to be running 24 hours a day for GPUs that are building new models for generative ai?

Those things usually come out of the central IT budget because the developers usually don't have to worry about it. In this company, there was a group that they had a budget that was really low and they were like, yeah, you know what, these are our people. And um, then what they did is when they started getting showback, they were surprised by the infrastructure that was being generated by these 36 developers on this team for exactly the reason you just described.

They were leaving environments up and running, they, um, would spin up another environment. So they were trying to create an almost in an environment per, uh, task or per ticket. And when they started learning how they could optimize and share resources, they were able to draw that budget way down just by being able to show them what the costs actually looked like.

This is something that's very difficult to do if you're, you're running in your own data center because it's usually a, a CapEx type expenditure. So it's really hard if I'm running in a four U server that's been racked for five years, do I still measure it off of the five year cost from when we bought it or based off of the price of what that particular hardware would cost me today? This is something that's great about the public cloud is we usually have very recent infrastructure that we get to work off of.

And because we're being billed on a per hour basis, it's much easier for us to be able to generate that chargeback showback to those, uh, application teams so they know where the spends actually coming from. Mm-Hmm. Do you think we're gonna see as more organizations wrap their arms around the cost?

Um, more movement of workloads because some of the things that are running in the cloud, let's be honest, you know, it was covid times, it was the only place we could put them. So maybe we need to kinda look at the total cost of running something in the cloud. Likewise, to your earlier point, some of the financial quote unquote engineering that goes on to justify on premises may not stand up to the light of day either as we apply finops not just to the cloud, but also to on premise.

So are we gonna see a lot of workload movement just because as people get a better handle on these cost issues? Yeah, that's a great question. Um, what I'm seeing with our customers is there has been a little bit of a slowdown for workload migrations.

A lot of that comes down to staffing. Um, the, there's been, uh, reductions in force inside these IT organizations, the people who had a majority of the knowledge about the applications themselves and how they ran. They're a little hesitant to move them to a new environment because it may or may not be running okay today, but they would rather fix it in place rather than building a migration path.

But then other companies that we're seeing when they break that application apart into the platform components and really leverage the cloud that it was in the way it was intended to not as individual infrastructure services, they're able to create building blocks. And so they have reusable templates that they can create and they're using GitLab to drive that automation for those workload migration. So they can see, oh, if I need a MongoDB database, here's what that Mongo database, MongoDB database looks like inside of AWS for us as an example.

They can reuse that template over and over again and get the economy at scale for the movement of that application in the public cloud. And then they get a lot of the other, uh, flexible benefits that we were just describing. They can now see what that particular resource cost them on a per hour basis.

So what have you seen people doing well to get down this path? Because I feel like a lot of folks look at this stuff and it's immediately overwhelming, right? Because point, there's all these things that are priced somewhat differently and now no apples and orange and all this other stuff.

So how do I get started? Where do I begin? Because, you know, ultimately I, you know, the first time you look at this you just shake your head and go, never gonna get there.

Yeah. So, um, I have a number of conversations with CIOs and CTOs, um, CTOs think about this problem a little bit differently. They're really interested in the architecture side of it and a lot of the CIOs are interested in the financial side of it.

Um, the first thing that we see a lot of our customers wanting to do is not only measure what it costs for a production runtime sitting in the public cloud, but holistically, what does it cost to be able to take an individual feature or bug from development all the way through to production? What does that cost look like? So we're seeing teams being spun up out of the finops concept that are driving this thing called value stream management.

So how do I organize my people and how do I organize my projects in such a way that I can maximize the value of change to a particular customer or for a particular event that allows us to then aggregate all of the different steps that it takes for that reorganization and for that change to occur for us to be able to funnel that up into our Excel spreadsheets and into other things by measuring all the different parts as well as the runtime environments, aggregating that cost and being able to show that to A CFO or to A CTO so they can figure out where they need to do optimization. We do this inside of GitLab ourselves. So we have somebody who is a director of finance and um, about once a month what they will do is sit our CEO, our director of finance and our CTO, they get together and they look at, there's a concept in GitLab called a merge request.

And what they'll do is looking at that merge request. Anything that is open for more than three days, we have to justify the expenditure of why that has not been merged and released into production. So we wanna keep that pipeline of change moving quickly because the faster we can get those things into production, we're able to do things on the backend.

Like we can reduce support burden because we may have a number of customers who are running into an issue. This is the part where finops has an opportunity to expand into is usually when I'm talking to customers, they're only interested in that optimization on the cloud side, but when we start to say, Hey, by the way, I now can allocate a support person to be able to work on different tickets 'cause they're not answering the same question 30 times in a day because we have a consistent, we have a consistent bug inside of the system that allows 'em to be able to get new features, new capabilities out rather than trying to walk them through a workaround or something else. So we're able to optimize that process all the way into the support side.

So that's where having that total cost of ownership of what change means is where I see this, uh, finops concept going down the road. You mentioned value stream management and we've been banging on that drum for some time in the land of DevOps. Um, do you think finops will pull value stream management or at least create more awareness of that as a motion for managing or assessing the value proposition of software initiatives?

I definitely believe it's pulling it forward. Um, so the value stream consortium is seeing this as, uh, sort of top of the funnel for being able to drive awareness of the importance of measuring change and change is both measured in good and bad. So, um, when you're able to evaluate those trends across the organization, you'll be able to measure, okay, well where are certain teams excelling and how do I get other teams to act like them?

What are they doing right? And how do I retrain other teams to be like them? How do I go ahead and build patterns, uh, for reuse?

Um, I'm, I used to be an enterprise architect and that's near and dear to my heart is how much of this can we templatize create blueprints out of build components that are reusable so that way we're not recreating a wheel every time we go into a new project. And as we're able to do that, that ends up being realized in value stream management. And then I'll, um, at the end of that, when we start looking at finops, that funnels up to the CFO and they can see the cost of building new applications goes down while customer acquisition costs are going down, while revenues are increasing.

And I have yet to meet A CFO that doesn't wanna meet their board of directors and show them those charts and not be jumping up and down and saying, look at how awesome I am at being able to optimize for this. So do you think ultimately that finops should be just kinda another metric we're tracking alongside all the Dora metrics and it's just in our console and we should be able to see in real time what things actually cost? Or is this gonna be like, you know, a separate platform somewhere with a separate console that, you know, a finance team is using and then sending off nasty messages to the IT team?

Uh, so I definitely think this is gonna be embedded in your, you know, DevSecOps platform. You know, the near term things that we're already showing to customers is for, uh, how many CI minutes are being consumed by project so we can show them the minutes, but because we don't necessarily know what sort of infrastructure lies underneath that, it's hard for us to be able to put a cost to it. But they can easily do that mapping by being able to know where their, what we call runner fleets, which is where CI jobs execute for GitLab.

They can do that mapping by being able to pull reports from AWS or GCP or Azure and then mapping that to the minutes for that project. So that's a short term win. But I do believe over the long term is we're gonna be able to know more about how these applications are being deployed and managed and you know, um, what happens when I've got a bug in production, we've all heard the numbers.

Oh, finding a bug in production is x cost. Finding it in depth test is another cost. Finding it in the developer's ID is another cost and it's usually a 10 x type of increase every time you go across.

Well, let's actually show that. Let's, let's have that visible to a development team that when they find a bug in production, why is it important that that's getting fixed as soon as possible? Because we've gotta draw that cost down because it's driving costs and support.

It could be driving a loss of customers, having that kind of visibility we just don't have today. And the only place you're gonna get that is in a holistic platform like GitLab. Alright folks, I know it feels like the cloud infrastructure is free, but trust me, there's a bill that shows up every month and somebody starts screaming about it somewhere.

So we need to get better at all this stuff. Hey Lee, thanks for being on the show. Thank you so much for having me.

All right. And back to.