Techstrong TV – January 16, 2025

Hello, everybody. I'm Mike Vard. Today we're talking about, well, the TikTok ban seems to be likely to happen.

We'll find out later, I'm sure, but right now it's a whole big, huge debate. Then we're gonna have a little trip to Carnegie Mellon about robots and ai. And finally, a little CES retrospective.

You're watching Textron Game. All right, everybody, welcome back. We have a small gang today.

We got Lisa Martin is with us from the Futurum Group, where she's the CMO advisor, and we're gonna be talking about a lot of marketing issues today. So great to have you here, Lisa. How are you?

Great, great, Mike. I'm excited to dig into today's topics. All right, and John Schwartz, who is some sort of regal person in Silicon Valley, and You've been listening to, uh, Alan's, uh, bantering.

Yeah, that's, yeah, he's, he's, he's elevated me to a state that I have never been associated with, nor probably ever will. Gee, I, yeah, I, I'll take it. I have to say I missed the ceremony and I was hurt that I didn't get invited, so, you know.

No, I'm sorry. Next time. Invite, either I was here.

I invited you to be V IP front row. Uh, there you go. All right, well, let's jump jump into our first topic here, which is this whole TikTok band.

And this has been a long time incoming, and apparently, uh, some folks are saying it's a good thing. Some folks are saying it's a bad thing. Other things folks are saying, we need to postpone it a little while, so it can just become an American company.

But, uh, Lisa, I know you've been following this. What's going on here? What's the pace of this and what, what is the concern?

So the concern there, it's, it's a very polarizing issue, Mike, as you kind of alluded to, there are, it, it, on one hand, president Biden signed in, uh, about a year ago, um, a ban effective, uh, event or sale mandate effective January 19th, which isn't a couple days from now, because of concerns for national security. Um, you know, the challenge, the, the concern that some legislators have, and the Supreme Court is hearing this now, is that tiktoks proprietary algorithm is really way more advanced than a lot of other apps and how it is targeting you with data collected by you and surfing up ads and things like that. And the US government is saying that is way more dangerous and effective.

Um, come this Friday, the Supreme Court will hear from TikTok and its Chinese parent company bite Dance, who's seeking to, to, to block the law that Biden signed in, as I mentioned a, a minute ago. And, um, the TikTok is also saying, so it's a ban or sale issue. TikTok is saying we cannot be sold.

It's not technologically possible. It's not, um, uh, commercially possible either. And even if it was to be sold, say the Elon Musk, there are rumors about that it would only be the US version of the platform, which would not include the algorithm, which is the secret sauce.

Then you have other legislators like Massachusetts Senator Ed Markey, who is planning to produce legislation to delay the ban or sale by an additional 270 days, which would put it in the administration of President Trump, who in 2020 was against it and got a lot of support on TikTok for the election, and is now on the side of keeping it. So it's such an interesting, uh, polarizing topic. Then you have the content creator perspective at a really interesting conversation earlier this morning with a very popular TikTok, or who's got about 110,000 followers and said, you know, there's a, there's an economy here that isn't reported in the labor markets.

And so if the, the app were to be shut down, uh, if it were, if the band goes into effect on, uh, the 19th, then Apple and Google would have to remove it from the US app stores, and, uh, US internet browsers would not be able to surf up the TikTok website. So content creators would lose income. There are creators like Charlie d Emilio, Addison Wright, who are getting hundreds of thousands of dollars per video.

So this content creator that I was talking to said it's such an engaging algorithm for community development that it would be analogous to mass layoffs, even though we won't see those figures. So you've got folks that are staunchly on the side of sales on the side of ban, and we have a matter of just hours before this comes to, uh, home plate. Alright.

John, what is your take on this? And I just find the whole bite dance argument here somewhat, uh, duplicitous to be frank. I mean, six months ago they were saying, uh, whoa, it's all in the us so it's all different.

So it's not connected to China, so you don't have to worry about it. And now they're saying, oh, you can't sell the company because our algorithms and actually all these things are intertwined. So it seems to me that that's kind of silly, but what is the take on, at one point, uh, Larry Ellison was supposed to buy it, and now we got Aon Musk car to buy it, and who's gonna step up to buy it, and what would you actually get if you bought it?

Okay. So I understand the motivation for somebody like Musk to buy the company or to have the US version of it as, as Lisa pointed out, which would be about 170 million users, including people like my son who's 24, and I believe your son Mike uses it as well. They really don't care who owns it.

But I think Musk, the appeal for Musk would be just think about the instant credibility or benefit to the advertising for X, which has been flagging, it's been miserable the last couple of years, and also think about some of the data that would be generated that could be used for X ai. So there's that benefit. I also think, uh, Meta's gonna be watching this really closely.

So Meta really has no traction as far as I'm concerned, among younger users. They, they, they prefer TikTok, and I'm wondering if they, I mean, they're probably, they're, they're acquiescing to Trump in many ways. We could.

We've already probably talked about that. So I think they're watching, hopefully hoping for a ban. I think what's gonna happen, there's also a rumor, by the way, uh, Politico has written that, uh, Steve Mnuchin who served in the Trump administration, I believe his treasury secretary was interested in perhaps buying TikTok.

I suspect, and I, I'm not a gambler, uh, that they're gonna punt this down the road and leave it to Trump. I mean, I've seen Trump, um, change his position on this among other things. I think that's, that's probably what may happen rather than this kind of in imminent showdown.

But, uh, it has a ripple effect, you know, perhaps a ripple effect here in the Valley and throughout social media. Um, it's the absence of its, especially for young users is just, it, it just decentivizes any ch choice of using social media for, for the most part. So, um, again, I think they might go the path of what Mark is suggesting.

Mm-hmm. As it turns out, my 14-year-old is also a big user of Discord. So you go figure, you know, they're, they're, the kids are changing their platforms.

Which comes to my next question, Lisa. So, um, allegedly there's something called Red Book out here that people are being encouraged to move over to, and I'm not quite clear what the relationship between the people who make Red Book is and by dance, but what is that for real? Yeah, It is for real.

It's called Red Note, and it actually was, became the most downloaded app in the Apple App store a couple of days ago. So these TikTok refugees who don't seem, who probably are concerned with making money, aren't concerned with geopolitical considerations, but it's not, red Note, from what I understand, is not a quo of TikTok. It's really a mix of lifestyle content, videos, community building tools, but it's really resonating with tiktoks, what is this displaced audience.

And it's gotten the attention of investors. It's reportedly in 2024, it was valued at 17 billion. And despite being owned by, uh, a company in China, it's rise suggests that again, those content creators are more concerned with making money on the platform, which I mentioned earlier that there's a tremendous amount of money in there.

Now, what we don't know is from a safety perspective, I hear the algorithm is quote impeccable. Um, meaning that what it's really doing is fostering that community sense of community, um, that TikTok users have loved and have benefited from. Um, but it's gonna be, but another thing that's really curious here that we don't know yet, Mike and John, is that the T'S and CES are in Mandarin.

So every US user, and John said there's 170 million American users of TikTok don't really know what they're signing up for. So it's a bit of a black hole, but because the TikTok users don't seem to be concerned with geopolitical considerations, they want to go where the community is and where the money opportunities are. John, we've talked about this in the past, but I always found this whole quote unquote algorithm approach to things to being somewhat problematic in the sense that people don't know what they're signing up for.

That there is this algorithm that is manipulating content and behavior in a way that drives a specific outcome. And I guess they're entertained, but at the end of the day, um, is by dance any better or worse than what Facebook's trying to do? Or no acts for that matter.

It seems like, you know, they're all kind of, this is, you know, they're all the same path ones of Kettle. Yeah, they're all swimming in the same lane. I mean, that was one of the things that Sheryl Sandberg, who used to be the chief operating officer of, of then Facebook and then met for a while, she'd always talk about the people are the, the, the people are the product.

I mean the, the people who sign up for these, these services, regardless of where they are, it's free and they are feeding into the system. They are being used as part of the product that is the, um, tangent in, in a sense, tangible agreement or partnership they've signed up for, even though they don't read the fine print, they never read the fine print. Most of us don't.

I don't. And so, yes, I, I think you're right. It's hard to distinguish one service from another, from another.

And I think in the end, if there is a ban or if there is a, a push to, to go to an alternative, people will find a way to go somewhere else. I mean, we see this even with the extreme version of older users who've left x I've semi left it, I, I rarely use it. I've went to Blue Sky, which is, which is fine.

I don't use threads at all. There's a lot of pushback on, in terms of meta, a lot of folks leaving that now for various reasons. So they will migrate, they will migrate somewhere else.

But I think in the end, I mean, maybe we're reaching the age or the, the stage where people are just kind of have fatigue over all these services for multiple reasons. And when they find that they're not using it, they may not miss it as much as they thought they would. So, um, with TikTok, I'm, I'm, I think we're, I think we're gonna still see some version of it.

I just dunno what tangible sense. Well, I mean, I'm on Facebook still otherwise sometimes referred to as Boomer book, and a lot of that has to do with the fact that everybody I know is still on there and that's how we kind of interact with each other and shifting and lifting that whole thing would be a major challenge. But at the other side of it, I feel like the younger generation maybe is more willing to, uh, use multiple platforms and kind of is more, uh, multi social media channel lingua, I don't know what the word is, but Lisa, is there a difference in the genders and the demographics and the age of who uses what?

Yes, there definitely are. Um, I never felt cool enough for TikTok, so maybe that's because I'm Gen X, I'm not, I don't know. I never got sucked into, um, this constant scrolling.

We know that a lot of the younger users have moved, as you both said, off of Facebook onto TikTok, discord, other platforms where they're engaging with more like-minded folks. The interesting thing about Red Note is that it's predominantly female. I think the number I saw of its 300 million users, and this is before the TikTok refugees whenever was about 79% female.

So I think we're gonna see that dynamic potentially shift, um, depending on what happens on January 19th. But we do see gender and aged based decisions and interactions on different social platforms. Um, I'm a big Instagram fan.

Maybe that's 'cause I'm Gen X, I'm not sure, but it'll be interesting to see what happens if, if everything is delayed, um, will Red Note and TikTok become equal at some point? It's gonna be a really interesting showdown, and I'm one that I'm particularly keen to watch. John, what's your read on Washington here?

Because, um, from what I see so far, Democrats and Republicans are all over the map on this particular issue, and there isn't a lot of agreements. So, uh, you know, will people not vote for candidates who decided to ban TikTok or, uh, and will that become a political calculation? That's a good question.

I, I, it's, it's, it's hard to read through the tea leaves. That's why I suspect we're gonna, this is gonna be punted down the road because in, in true Washington fashion or their normative behavior is to not make a decision, right, or, or to, to not address a problem. They will address it in a public hearing, but they won't do anything tangible in terms of a legislation.

And I, that's why I think they might push this down the road to give themselves by themselves some time. And I think in a sense, the influence, and this is gonna a weird thing, is that the influence of TikTok has benefited candidates like Trump, especially among younger mobilized voters, which I never thought would happen, especially male voters. Um, there's this whole nother world, social media of, of sites and services that people like us in our age, at least, Mike and i's age, I'm not gonna say it's about you, Lisa, um, have kind of, kind of, kind of adhered to, right?

They, they, they follow this, this stuff that, and, and I think there's this strong, strong, um, membership as part of this, these communities. So I think Washington, maybe they don't wanna disrupt it too much, although they do wanna disrupt and have disrupted the way Meta goes about doing its business. Um, and also this is a big boost for x and I I suspect if there is some sort of partnership between X and TikTok, it is is a, a political calculation.

I mean, as we talked about before, Musk spent $250 million to be to whisper in the ear of Trump, and he has a lot of influence. And I would not be surprised if we see some sort of steroid version of X in some sort of iteration of a US TikTok company. All right.

We'll see, Lisa, there's been a lot of noise about a lot of things involving China, and this is only one of them. Um, is this be gonna become a bigger marketing issue as when we sit back and we start thinking about, well, you know, where did this thing come from? Whether it's network processors from, uh, some manufacturer in China or, you know, chips that we're not gonna give them access to early enough.

And aren't we on the cusp of some more larger issue here in this, this whole TikTok thing, just a piece of a larger puzzle? It that's a great point, Mike. It could be the piece of a larger puzzle.

I think, as I was mentioning earlier, if we look at the demographics of TikTok and all those refugees that are now flocking to Red Note, they don't seem to be concerned a a about the geopolitical issues. So I don't know if it would become a marketing issue, if, if social media companies would have to be much clearer with their users and their followers about where data is coming from, where it's being processed, who's owning it. I think we will see, um, I think we'll be informed by what happens over the weekend if there is a ban that goes into effect or if there is a a is a delay.

Um, but I think what we're seeing is the younger users just don't seem to be concerned that this is a chi that TikTok is owned by a Chinese company, that Red Note is owned by a Chinese company. They want to have the opportunities to engage with their community and to make money. And that seems to be the priority of the followers.

The majority of the followers. And we've got 170 million American users of TikTok. Yeah, Well, John, I was having a chat with my kids about this, and they weren't making much of a distinction between whether it was by dance and the Chinese that had their data, or Elon Musk or Zuckerberg, they Don't care.

They don't care. They don't care. They All, they, all they want to do is discover, scroll and watch videos.

I mean, that's how they consume news. They don't, they don't read newspapers or, or go to conventional news websites. You know, you mentioned that, um, this, this whole idea of China and, and if we, the, the, the chips export the security lists involving Chinese companies that might have ties to the military, um, we're gonna see more of this.

And it's can only accelerate with the tariffs up to 60% against, uh, up against China. Um, I think in, in, in a sense this is, this is a moving target that every tech company's gonna have to contend with, especially companies like Nvidia and Apple that have these huge presences in, in mainland China, China, um, this, this, this is kind of an interesting sidelight. This is getting a lot of attention because of the popularity of TikTok, but I think it's part of an undercurrent of constant conflict and, and it's only gonna accelerate with AI development in this fear that the US is gonna fall behind China.

And, and again, everything's about ai, everything is refracted through the lens, the prism of ai, whether it's politics, economics, culture of thought leadership. So TikTok is just a little bit, a little part of it, and, but I, I think we're gonna see some form of it so our younger users will be happy. Alright, well, Lisa, lemme ask you this.

Do the marketers care about this anymore than the kids care about it? Or are they just gonna go follow wherever the audience is and away we go? Or are they gonna be, you know, more subject to a conversation with, I don't know, the FTC or somebody?

That's a great question, Mike. I think right now is that it's, they're gonna go where the followers go. They're gonna follow the money trail, they're gonna follow what the followers are asking them to do.

Um, I think that that's probably what we're gonna see because ultimately every marketer wants to show customer value. And if the customers are demanding a TikTok or a Red note or both, marketers are gonna follow that because that's where the money is. All right.

Well, I think I'm gonna end this here, but I find it ironic that the thing that we're asking people to move to is called Red Note, because you know, that is the favorite color of the Chinese Communist Party. But there you go. Alright folks, we'll be back in a minute.

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Visit the builder community hub to learn more. All right, folks, we're back. And if you visit Techstrong AI or maybe some of our other sites, you'll see a lot of content there from our good friend John here.

But he recently paid a visit to Carnegie Mellon University and, um, checked out their robots and ai and, and as I understand it, you actually got a ride from something, but I'll let you explain for you. Yeah, It was, it was pretty interesting. So I have gone back to Pittsburgh three or four times over the last decade because I'm interested in Carnegie Mellon.

I got invited there for this reporting fellowship around robotics 10 years ago. So for me, this is a really interesting contrast to what I last saw there and kind of the state of things back then. It was kind of an anomaly.

It was, it was a kind of a niche, cute market. I mean, based on the opinions of people out here in the West Coast, you know, something that was gonna happen eventually it will happen. Well, it is happening now and it is accelerating and I think, think we're gonna talk about CES later and like the physical AI element of it and the idea that we're moving from generative AI to agent to physical AI in physical AI or robots.

So I went to Carnegie Mellon and I had a couple of things, hands-on experience, including one thing that was really interesting. It was a robot doesn't have a name yet, but it's being, uh, conceptualized for healthcare use. Basically, one of the things, and I'm going through this personally, is having a family member being transported from their bed to their wheelchair or from their wheelchair to the bed, which is incredibly hard to do.

It usually takes two medical personnel, especially if it involves like a back or hip injury. So there's this device, basically you're in a wheelchair, you are backed up to a bed, and this device slowly and easily moves you from the wheelchair into the bed and then props you up in the bed within two minutes. And I'm telling you that this transformation of the transition of doing that, watching somebody do it, the human do, it was kind of lurching and kind of alarming going through this process.

It just felt like I was being eased or sl sliding on a conveyor belt into a bed. And the bed basically had almost a personality underneath me. And this was all done within two minutes.

So there was that, wait, quick question. Did it tuck you in and read you a bedtime stirring or pluck the pillow? I seriously, they said, what degree of comfort would you like?

Mm-hmm. And it moved me to the exact PO point that I, that I preferred. So there was that, and then there was something called Halo, which is a, uh, PhD project.

It's a, it's a, it's a humanoid basically. There's a student with Apple Vision Pro goggles who's using hand motions to teach this humanoid how to act and avoid collision with humans. And the idea is you put this into an industrial task.

So the robot works side by side with the human, or for security reasons, you use the, the bot, whatever it is. It's basically something that moves and it moves in human type gestures. It was doing tai chi, it was boxing and, um, it, it's, it's, it's, if you went near the robot or try to touch it, it would move away from mutual void contacts.

Um, they're also teaching this robot how to work in a kind of a sense, uh, kind of an assembly line where if it's moving cans or items, if the human comes by, it avoids contact with the human and avoids knocking over the, the items. So that was pretty cool. Uh, there was also a, a fruit picking bot, and it's apples and grapes are hard to pip, you have to do it by hand in some cases, or in the, in the case of apples, you have to reach up under trees.

This technology, you sound as much as anything else because it's really hard to see an apple in a tree sometimes or reach it in a certain manner. So this, this is another product. So what's interesting to me too was these are these products now, like 10 years ago, literally at Carnegie Mellon, they would build a rock robots from scratch.

Now they have basically these starter kits. So they'll buy, uh, from a, a third party, the basis of a robot for 20 $50,000. And then they'll build the intelligent of the learning into it for the specific tasks.

And in, in a sense, what they're doing is they're showing you the physical manifestation of what we've been told will happen. Now, the, the difference between Pittsburgh and I think the approach there, especially Carnegie Mellon and pits, is that they know hardware is hard, expensive, and it requires something that's in short supply in Silicon Valley patients, right? They also understand this stuff is expensive and it's gonna take time.

So in a sense, they're kind of doing the grunt work to take us into these new era of using of robotics in terms of, uh, these tasks. And the other thing that's interesting to me is that Pittsburgh has multiple universities. They've got like seven or eight in the city or in the area, and they've got a lot of warehouse space that has been unused after the steel industry didn't leave, but didn't kind of deserted the area.

So in a sense, the warehouse space is ideal to safely test the robots. So you've got a lot of these factors all kind of coming together. You've got the intellectual power, got the space, and you've got the infrastructure.

The one thing that actually seems to be missing, and they're working on it, and there seems to be an obsession, is the use of a hand or a claw to do the manipulation. So, um, last example, I'll mention, uh, uh, PhD student who's about to graduate, let me manipulate a robot, uh, and use of fingers. And, and one of the tasks it's gonna do is it's going to, um, apply, uh, drugs to, um, an IV like through the use of a syringe.

It's not into the human, but into the iv. So there are all these things going on, they are a couple of years away. Um, but it puts a practical face for me on something that we're hearing a lot about, which is kind of, you know, kind of proof of concept that we're hearing from places like Nvidia and Tesla and others.

Mm-hmm. So let me ask you, what is your sense of how long it will take these technologies to get from the lab into, in the case of the, uh, robotic hospital aid? How long, you know, what, what's the timeline?

Yeah, so that's something that I kept pressing and I would get variations from three years to five years. And the thought there was, you know, we're gonna have a lot of examples or a lot of attempts that are just gonna fail or they're gonna fall short. Um, and you're gonna learn from the first version, like any type of technology, the first wave has its flaws, and eventually we get it right and we perfect it, or as close to perfection as we can.

And I think that's kind of the mindset here. Um, especially in uses of agricultural, um, tech. There's also, you know, the, the, uh, reception or the, uh, reaction of the workers.

You know, we, we can, we can test something as much as we like in a lab, but the real test comes when you actually put it in a real life circumstance. So we're gonna see this play out, and it's gonna take a lot, take a lot longer than this kind of brings me back to like this digital workforce we keep hearing about from Salesforce or Nvidia. I think they're referring to more like these kind of tasks that are involve things like email or transcripts or kinda low level thinking, not, but when it comes to robots, this is just a totally different ball game to me.

Mm-hmm. Lisa, I can see robots on the backside of making manufacturing a car, or to John's point, yes, working in the field and agriculture. But once we start integrating robots in with people, um, will there be kind of a marketing challenge that goes with that?

'cause if people are gonna have to have some level of confidence in these things, and are we gonna personalize these robots and call 'em by their a name? Or are they just gonna be, you know, machines or things that, and objects that we consider to be, you know, disposable? That's a great question.

I can see it going both ways because as consumers, we all expect, and marketers have to do hyper personalization to reach their target audiences with highly contextual, relevant content. And so we as humans kind of crave that personalization. Um, I think that we're going to see when humans and robots come together, I think a dynamic there that will probably favor some level of personalization, customization with, um, appearance to the way that it interacts, the way that it communicates, whatever LLMs it's using to have this conversation with a human.

Uh, I think we're a ways away from that, but I can see, um, that personalization coming from the marketing side and into the relationship side as this relationship AI really takes off. Hmm. I can see that.

You know, you know, Mike, go Ahead. You know, Mike, you know, you know something really weird. It just, that question sparked something that made me think about Halo, which is this three and a half foot tall humanoid that when I first saw it, I was kind of a little taken aback and a little bit somewhat intimidated by it, but the more the people who worked with it talked about it, they were talking about developing its face, its hands, like little things that were not technical.

And, and by the time you were finished with this hour long demo, you kind of got the impression from them that they were, they were, had built this in a, I don't know how to put this, like a weird re like, um, relationship with it. Like they've, they've built like a, a kinship to it, that it was like a, a child, it was like their child, like their offspring, and they were creating, it was like Dr. Frankenstein a little bit, but, um, it was, it was, it was like, uh, they had a sense of empathy and, and they wanted it to learn and they were so proud when it learned something.

I don't think, I've never thought of it that way until I saw it up close. Oh, no. Do any of these people have families of their own?

'cause that's how you feel about your kids, you know? I hope so. I don't think they did.

No, these are all young Polish. These are like college students. But in a, in a, in a weird sense though, I actually pull a little bit of a kinship or a little bit of like, oh yeah, hey, I, I, I was kind of rooting for alo to, to, to make some strides.

All right. Uh, I love dogs, don't get me wrong, and I'm probably gonna love robots, but they're not quite the same thing as having a baby, you know? Yeah, I know.

Exactly. I know. I mean, I, we've got kids, but it's, um, there was also behind humanoid, there was the robo dog.

So one of the things that Carnegie Mellon, oh, you walk on the, you walk around the campus and it was freezing, but if you walk on campus, you'll see like these little robo dogs being tested on the, on the sidewalk. So fun. Too funny.

Yeah, It was cool. Yeah. Um, Lisa, this is more psychological than marketing per se, but are we also in danger going the other way?

And I will take no responsibility for anything. 'cause if it goes wrong and the robot did it. Mm-hmm.

Well, to John's point, and this is something that I saw at CES that we'll talk about on the next blog, there is a sense of pride with the creators of these robots. And what one of the companies that I saw that will cover in the next segment talked about is relationship ai. These are, they're, they're becoming companions.

There's a market for this, which I couldn't believe. So I think that from a psychological perspective, um, I don't know, I think we're gonna see more of that partnership come into play, because I think that's the nature of, of human psychology. Even though we'd be interacting with robots, I think we're a little ways away from that, but I can see a sense of, um, kinship developing there.

And John, you said you kind of saw the same thing with these folks that are developing these humanoid robots and they're proud of their accomplishments, they're proud of what the robots can do, but I to your point, Mike, I don't know, I, I would hope that humans would take some responsibility if something untoward happens because of a robot's actions, um, that they would then want to correct whatever anomaly occurred and, and take that responsibility. You know, there's, there was something I I, I, um, I wish Alan were here just for this one little thing I was gonna mention, but there's a section of Pittsburgh car called Lawrenceville, which used to be a hotbed for steelworkers. It's a series of row houses, and I think it's in like, like the northwestern part of the city, and it was kind of deserted.

It was kind of left for dead after the steel industry. That's because We're gonna use robots to make steel, but that's another issue. Yeah.

So that's the other thing. That's the weird thing is now it's become, because there's so many warehouses there, now, it's the young place to be in terms of robotics. So it's kind of come full circle, right?

Somebody mentioned the 1905 Detroit moment for Pittsburgh, which is a, you know, it's hyperbole, but given the focus, and here's another thing, Nvidia has announced its first, and to this point, only a partnership with the city is with Pittsburgh. And they wanna go pull, pull force into not only robotics on the ground, but under, under the ground in terms of water treatment. I saw a lot of robots that were being designed to do water treatment for safety reasons, and also because these are small gaps that they're working under.

But also the Pittsburgh is opening a new airport at the end of the year, which will be having, um, its share of robotics in terms of robotics, cleaning. And, um, you know, we always see the, at SFO, we have a Cafe XI remember writing about that years ago, like basically a robot that makes you coffee. Um, those are starting to pop up at airports, but it's, it, it is, is a real thing.

It is something that is happening. But again, I always want to caution that this stuff doesn't happen overnight. And the one word that kept coming up was patience, because this involves a lot of testing, a lot of money, a lot of, um, a lot of, uh, serendipity probably in the ends.

Um, but it is happening, it's happening much at, at a much real level. And we see it with autonomous vehicles. And I think that is, we, we've become used to that, accustomed to that.

So when you start seeing, um, robotic, um, apparatus doing certain things, it won't be as shocking as it used to be, or thought it would've been. Well, I think, and Lisa, maybe you have something to say this, but these things have to get to the point where this, they're disposable because now they cost millions of dollars to build. And when are you gonna do, you're gonna, like in New York, they had a robot running around the subway, but by the time everybody got done kicking the thing, they were like, oh, this thing isn't really gonna stand up to that kind of wear and tear.

So, um, so Lisa, I mean, you know, part of the issue of taking these things to market is they're gonna have to get to some level of cost that is affordable. And so when something goes wrong with one, it's not that big a deal to replace it. Exactly.

It, it's, it's a scale perspective. And I think, John, you brought up a great point about patients, and I think that's something that we're gonna have to take into strong consideration to your, to your point, Mike, they need to be cost effective. They need to be, uh, be able to be produced and put into market at scale.

So companies are gonna have to be not just considering that, but making sure it's a reality. And, but there's also the patience and the sort of, um, acclimatization, I guess I, I would think between the humans and the robots to be, um, really copacetic and, and, and coexisting together and understanding on the human side what the robots there to do from an assistance perspective. But I think that, I think we're a ways away, but I, I like the patience factor.

I think that it's definitely something that every company that's working in robotics is gonna have to take into effect. And then it becomes a marketing opportunity to condition, if you will. And that sounds, maybe that's the wrong word.

Um, and, and you customers are, or pe people to be able to coexist and understand the benefits that they'll get from coexisting with robots. It's gonna be a really interesting space to watch, and I'm sure you'll be not short of lots of exciting developments in the future. I'm, I'm, I'm pretty sure that the people side of that equation is gonna be the harder one, but we'll see.

It will absolutely be the hardest part. You bring up a great point. All right, folks, we'll be back in a minute.

We're gonna talk about CES in a retrospective because, well, it's one thing to be at a show, it's another thing to have a look at it in the rear view mirror 'cause things have a little more perspective back in a minute. Discover Textron Group, the epicenter of tech innovation. We are your go-to for reaching it leaders and practitioners worldwide.

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Contact us today and tell your story to the world in the most powerful way with Techron Group. All right, folks, we're back. And Lisa Martin was at CES and had a moment now to kind of take a look back and say, here's all the things we did look at and maybe add a little more context and, um, perception around what matters.

Because I think when you're there, you're overwhelmed by all the toys. And then, you know, when you get home, you could take a minute and you go, wow, well, which of those is actually gonna change the world? So in retrospect, and now that you're home and you're looking back at CES, what stood out the most to you?

There were really five things that I went there to see. I mean, now we have some numbers. 1 million square feet of exhibit space, thousands of product launches.

You hit the nail on the head, Mike, it's overwhelming. You, you have to plan it. And this was the first CES where it actually went specifically to look at the show floors, product launches, meet with companies versus doing shows.

And the best way to approach it, I think, um, is to really narrow it down to maybe four or five categories of products that you want to see. I wanted to see digital health. I went to see voice recognition specifically, um, aiding the driving experience.

I also wanted to see the humanoid robots, and I wanted to see the flying cars or the e VTOLs to see do they really fold up to a briefcase like they do in the Jetsons hint, they don't. But it was, what surprised me was the age check, the emphasis on age check. I talked about this last, um, last week's show where we talked with a company called Skip With Joy, who's making, uh, exoskeletons integrated into arcs, uh, uh, active wear.

And, and this whole, uh, uh, area is called move wear, what they're doing with not just to assist people with hiking or with knee issues, but how they're getting into helping patients with Parkinson's disease. So really wonderful applications, uh, for people who will really need it down the line. I saw a smart cane from a company called WalkMe that actually has GPS and voice integrated.

So for people that are visually impaired. So I thought that's, that's a, a great use cases there of helping people who need it. On the digital health front, I was excited about a product called Omnia, which is prototype from a company called Withings.

And it's basically, it reminds me of those fitness mirrors. It's a full length mirror that is aiming to do a full body scan. They have other products from smart watches to blood pressure machines, um, to urinalysis machines, et cetera, that will be able to track certain parts of your, of your health.

But this prototype could be used, say, in, in hospitals and doctor's offices, in gyms, to help people be able to understand more and take more ownership of their health and share that with their providers in real time. Um, what I also saw was the voice recognition really being able to be, um, more advanced and more integrated into automobiles so that drivers and people, passengers within the vehicle can have a conversation with the vehicle and ask it to do certain things and it will respond accordingly, um, in a way that allows you to actually have a dialogue as they're built on LLMs. Then I saw the flying cards, which we'll talk about, and, and the humanoid robots, which blew my mind.

Well, I'll tell you, the cane thing resonates with me. 'cause my father-in-law lives with us and he has a cane and I, I can tell as he gets older, he is less reluctant to go places on his own, not because he is afraid of falling down, it's just that he doesn't want to get lost and he doesn't want to call somebody to say, I'm lost. So he kind of starts to narrow in his Yeah.

Geography of places he's willing to go. So I think, you know, that GPS thing, I'm not quite sure how the visual thing works on that, but um, I kind of like that idea. It might be on his Christmas list next year.

I like it too. Yeah, I thought it was an outstanding way to really, from an assistance perspective that was in the age tech area. Um, but then I went to see, I wanted to see the flying car.

The, a company called Xang was featured there, and like I said, it, it does fold up into a support vehicle. It doesn't fold up into a briefcase, but this is an ev, EV, E-V-T-O-L, which is an electric vertical takeoff and landing aircraft. Right now, the use cases are more for search and rescue, and then for those people, like maybe like the Elon Musk types that have $300,000 to spend on a vehicle that they can drive into a beautiful part of the country and then take their, their flying taxi out to explore national parks and things like that.

But what was really interesting to me is that this is being produced out of China. It is being mass produced, it is piloted currently. Um, you have to have a pilot's license and only a driver's license.

I first saw driver standard driver's license, but that was when they were talking about the support vehicle. And I'm like, okay, wait a minute. Um, it's, it's controllable via like a joystick.

So for a lot of gamers, they're probably gonna be fairly adept at learning how to manipulate this aircraft. And while it's piloted currently they are there, there are autonomous tests in beta. But I think from a first responders perspective, that use case I think is quite strong.

There are other use cases down the line, but I think that I can see it being able to go into, especially with what's happening right now in southern California, go into remote areas and be able to assist, um, people, natural disasters and things like that. So that was a great one to see. And also the fact that it's being mass produced right now was a surprise to me.

There was one thing I saw that made me scratch my head in, that's not hard to accomplish that, but, um, there's a Maserati that was driverless or self-driving or whatever it was. Yeah. And I looked at it and I was like, well, that's kind of an interesting idea, but if it's gonna drive itself, why would I spend so much money on a car, car I don't get the drive.

Yes, exactly. There, the autonomous vehicle section was at the Las Vegas Convention Center was huge. Every manufacturer from Volkswagen to BMW is involved and a lot of autonomy there that's making the, that their objective is to make the driver experience more seamless, easier.

But I agree with you, Mike. I wanna interact with my vehicle. I I don't have a Tesla, so I've never tried the self-driving that autonomy that it can deliver, but it is everywhere from Hyundai to Toyota, you name it.

Every manufacturer, auto manufacturer was there showing the different types of states of development that they are in the autonomous experience. So I, I wonder how far we are from that being a reality and from people actually embracing it. Um, that was something that, like, it was like we talked about in B block in terms of like the, the robots and people being so proud of what they've developed, same type of mentality with these auto manufacturers and the autonomy that they're, they're aiming to deliver over time.

Right. So if I'm not driving the car, John, do I care what it looks like? Or is everything gonna Wanna be in a cyber truck from eon?

No, just want a functional, just want, yeah, a functional shell to get you from point A to point B. You know, that's interesting with CES. So I, uh, Lisa, I'm, it sounds like you had a great experience and I know you did because we were talking, um, we were texting, you were texting me about your discoveries occasionally.

And I have to say, um, from not attending this show, and I, I have for God, like 10 or 15 years in a row, I lost track. I actually found this a very, very interesting, more so interesting than the previous shows, um, in terms of substance and in terms of practicality, especially in areas like life expectancy technology. We're seeing a lot of that.

And it, it starts early, by the way. So for Christmas, our 24-year-old son wanted his smart ring to monitor his, his vitals. And, um, it's, that's not unusual.

And I, and I, I learned that there are people who are tr who have they look at technology to extend their life by decades or if not forever, like the, the documentary with the, was it Brian Johnson who wants to live forever, the tech executive? But this is like real, I mean, people are embracing this stuff. They're using it, they're finding ways of using it.

And to me it is like so refreshing that we are seeing technology that has profound influence on people's lives rather than the stuff I was seeing the last several years, which was, here's an app that you can, you can use to drop your car off while you go to a play, and while you're up the play, they'll wash your car and fill your tank. That that's the app. You know, it's like the, it, it's, you know, it's like we seem like we're taking like a quantum leap, so to speak, or we're doing things of much more substance than these kind of silly knockoff apps that we were seeing so many, so many companies doing, including some of the big ones.

So I I I, I came away very impressed by this. Uh, yeah, the practicality aspect was definitely there. Like Mike, you talked about when I mentioned the, the cane from WalkMe, how that really resonated with you, the practical applications, I saw a lot more of that and I gravitated towards it.

So it's great to hear your perspective done because I don't, this is only my second CES but before I'd been there as, as a broadcaster, and so I actually had the time and I was captivated into things that I thought this could really change the human experience. I mean, everything from, I talked about to like, like s sorry, John, like bulletproof, smart, secure pet doors, for example. I mean, you name it, it there, Yeah, the reason why I bring this up is that I, I used to every year go to an app, the Apple events, and in my, the previous jobs, I would go every year and you'd get caught up in the reality distortion field, right?

And you get caught up in the hype, all the reporters did it. Then you'd step back a couple of days later and say, okay, a marginally better battery camera, bigger form factor, oh, a different color. And you'd always feel as if you'd been cheated or, or in a sense kind of hood winged.

And I think now, I mean, I think there's like a real push for technology that does good because we hear so many bad things about the tech culture and the bro culture and the oligarchs, and I think it's, we, we also have a huge number of people who do really interesting vital work that helps people. And I, I'm glad this is coming to the fore, And I always think back, and for all these great positive use cases, it's always worthwhile to take a minute and go, well, what could be the, the negative side of that? And you were talking about a ring that would measure your vital, imagine if you would, there's five people sitting in a bar trying to drive their vitals up through the roof by seeing how much they can drink, and then they can measure who's messing each other up, right?

That's gonna happen. I never thought about that. That you're right.

There's also, there were also things, and I do wanna talk about this company called Robotics, because both of you turned me onto it, the humanoid robots, and I expected it to be a huge display. It wasn't, but the first word, first of all, it took my breath away when I saw it. These are humanoid, very realistic.

But the first word that came to mind literally out of my mouth, and I was there by myself, was creepy. But it's, it's a customizable human robot. It looks like something out of a plastic surgeon's office.

I'm not even kidding. But what they're doing is, and these guys were like, like John, what you saw at Carnegie Mellon that we talked about in the last block, so proud of what they built. This is, this is a robot called Aria that I inter interacted with.

She gave me some recommendations, um, to have sushi specifically at the Mandalay Bay, which is not where we were. So she knew her way around Las Vegas. She's been on Fox Business, she's had a conversation with Fox Business, but it's built on an open source platform.

So it allows integration with not only real bio's, proprietary companionship, ai, and that was a new term for me, the companionship, AI or relationship ai, but also integration with third party platforms like chat GPT, it's customizable. You can take the face off, you can change the hair. It was manicured.

The only thing it doesn't have right now, uh, is a plausible thumb. So I thought, oh, just like my dogs. Um, but you could actually have a conversation with this.

And they say it's from an application perspective, people are buying it from a companionship standpoint, and it's got application to real world applications. And Bill Healthcare on the education sector, um, it surprised me and it still, I, I think why do we really need this? But they seemed very proud of what they've developed.

Although I will tell you in the photos, if you know the photos I sent you guys last night, I'm wanted you to see what this looked like before we talk about it. The females were very young. Um, beautiful skin, very like, like came outta the plastic surgeon's office or something like that.

The male that they had, there was just a bust and he had gray hair, really bushy eyebrows that came out like this, wrinkles. Um, but what it has, and, but I wanted to talk about was this eye tracking and object recognition. So there was this, there was a stand of, of two or three busts and there was a, a screen there and you could actually see what the robot sees.

Um, the micro cameras embedded into their eyes, for example, that can track movement. You could see who in a, a little audience it was looking at, um, interchangeable modular body parts for companionship and apparently healthcare and education. So there's still some things there that are like, what do we really need this?

But it was interesting to see because I haven't seen anything like that in, in person ever. All right, folks, it's gonna be a strange new world. In fact, I'm trying to figure out right now how I'm gonna manage my multiple robots that are likely to get jealous of each other, but who knows What's true.

They're like children and like dogs. Yeah. There you go.

Anyway, hey, Lisa, John, thanks for being on the show today. It's always great fun. Likewise, this was a blast.

Like it Was all Thanks folks, and thank you all for watching the latest episode of The Techstrong Gang. We have a whole lineup of tech strong TV content coming up right behind that. By all means, stay engaged.

We do this all for you guys. We have fun doing it, but it's all for you guys. Thanks for spending time with us.

This is Techron tv. Hey guys, thanks for the throwaway here with Jeff Grays, the CEO for glue ware, and they just got an investment from a, uh, what they call a venture capital firm these days still. And, uh, we're gonna be talking here about, well, what is going on with intelligent network automation, because I feel like we're on the cusp of something different here in the age of ai, but we're jumping to that in a minute here.

Jeff, welcome the show. Thanks, Mike. Appreciate it.

Great to, great to be here. All right, give us the backstory on the investment. Where did it come from and what are you guys thinking about using it for Sure.

2 billion serviceable addressable market. The, the, the reason for the investment is because of our platform and our team and going out to attack and be able to serve customers in what is now becoming a mature market. We have been paddling a well ahead of the wave for years, and now there are actually conferences, network automation, forum auto Con 0 1, 2, and three.

Uh, the next one will be in, in product. So it's actually a conference directly around network automation. And the thesis is that customers are currently self building scripts and playbooks and cobbling together their own software.

And the, this has happened before in the market. So if you think of data storage, backup, and recovery, uh, this happened before. If you think of compute, this has happened.

If you think of traditional RPA, this has happened. So instead of global 2000 customers needing to write their own scripts, if there is a platform that is commercialized, that is secure, that is safe and predictable, that actually allows customers to take the scripts and playbooks that they've been writing for years and to be able to leverage what works and reuse their self-build, uh, scripts, but also institutionalize that knowledge, uh, bring self-operating and safe and predictable change to enterprises that this can reduce cost, increase security, and really help customers and enterprises become much more agile. So there's a meeting of the minds between quadri capital and glue air.

And I would say one of the, the, the key things is that their operating advisor, ZUI, who was a founder of Ansible and the former chairman and CEO, is now on our board. And if there's anyone that knows about DIY self building scripts and playbooks at Sayeed, and the meaning of the mind is that, hey, this is the future that customers need a commercialized platform to really get to the next level and bring that value. This is what what is ahead for, for us.

I can go into roadmap and, and whatnot momentarily, but let me, let me pause there because that's the thesis for the investment. So where are we on the journey here? Because I feel like a lot of the times, the people with the expertise to automate the runbook are too busy holding things together to create the playbook.

So, um, have we gotten to some point where maybe it's becoming easier to create those playbooks? And are we on the cusp of maybe democratizing network automation? I, I would say that, that through a platform and we have what is called dial, uh, device interaction automation layer, that brings something new to the table.

Uh, I would argue that if, if customers continue to script and write playbooks that it's actually script and playbook sprawl, and they actually can't just magically automagically figure it out, everything starts working. That new humans need to come in to relearn, uh, scripts and playbooks when, when those developers or or scripters leave a company that it's very task oriented. That, uh, there's only a handful of companies that have been able to do this well at scale.

Almost every customer, every enterprise has an automation team and it's become mainstream in terms of looking to tackle this problem. Yet, one of the things, and the key takeaway at Network Automation Forum, which is in in Denver and November, is that throughout the industry, customers are really scratching the surface. They're looking for help.

In fact, one of the taglines of Network Automation Forum is why haven't, and I, I, I may not get it perfect, but you know, why hasn't this taken off? And so you have a whole bunch of like-minded individuals that have gotten together to try to start to share ideas and recipes and solutions. And meanwhile, we have a platform that has over a million, uh, hours of, of death, uh, 301 releases strong, where in major customers that are public, uh, in terms of references, MasterCard, ey, Merck, uh, a variety of customers that, that have, have been able to put their brands associated with Glue Air, and they're running glue air throughout their entire enterprise.

So with through a platform approach where you bring best of breed called scripting and ideas and task oriented development. And if, if a platforms such as Glue Air can go and call those scripts for some of the more simple things, but then for more of the complex, uh, things where it comes to like, say an EVP and VX land fabric with 30,000 switch ports and being able to iterate on the model and constantly change it and tune it for the enterprise need, you have to have safe and predictable automation. It has to be intelligent with pre-checks and post checks, declarative item potent, et cetera.

That's what Blue Air brings to the table. So instead of like self build on one side and then a platform on the other side, if you could bring these worlds together, if we can go ahead and provide an on-ramp for the developers to accelerate, uh, their, their path in the enterprise, then you can bring both of these solutions together. And so what we're doing moving forward is really opening up our platform catering to the developers, and it's a both and not an either or.

We're better together with Ansible, we're better together with Python, we're better, better together with Linux operator, GitHub, NetBox, you name it. And so what you're gonna see from us moving forward is embracing the developer while, uh, making sure that the executives at, at the businesses get the value. And there's a dedicated business case for every single customer.

So I would say through platform as well as subscription and these coming together, that there really is a tipping point that's happening. You take one or just the other, and you know, it's not, it's not going to, not gonna work out throughout the enterprise. You've got some, some competing interests.

Um, but this is a really, really exciting path for the future is, is my belief and our belief and really the thesis for the investment. Mike, Of course, these days you can't walk down the street without somebody leaping out to tell you about their great new AI thing. But how will network automation and large language models and gen AI and all these things come together from your perspective?

Well, I will, uh, let me speak, uh, directly from our perspective. We announced Gluer ai and we have two co-pilots, uh, not one, uh, our CMO likes to Jo joke, uh, once better than one network automation co-pilot, two network automation co-pilots. So we have one that's focused on NetOps, which is being able to interact with the network to understand what is going on with the network, what are the vulnerabilities, all through a, a prompt, uh, where we fully integrated LMS and fine tuned and trained models.

And what this means is that you can now say not only what is going on in network, but go ahead and make changes. And so if you were gonna do that with scripts and playbooks alone, without building all the pre-checks and post checks, it could be very, very scary. With Glue Air, you can interact with the network devices and elements and fabrics in a safe and predictable manner.

So now you can type commands and say, go ahead and upgrade all of the routers at this certain time and integrate with ServiceNow where we are vulnerable and, and Gluer and Gluer as copilot, NetOps copilot can go and do that. There's a a ton of value there. We're working with customers and early field trials, which we announced getting feedback, and this will be very exciting.

The second co-pilot is our net DevOps co-pilot. And this is targeted for those sophisticated users that know how to script, they know how to write playbooks. And now instead of our IDE, our integrated development environment, that's 10 x faster and more efficient, uh, in cranking out intelligent automations where our sophisticated customers are writing applications, apps, and abstractions on, on top of our platform.

Now, with an integrated LLM, it's like a hundred times faster, and I'm not exaggerating, it's like 10 seconds, boom. You've got an automation for Cisco, you've got an automation for Arista, you can do vendor swap, you can do some really, really amazing things. So sky's the limit of where this is gonna go.

So with two copilots, one for NetOps and one for net DevOps, and being able to share, have customers share in a community, which will be, you know, we're going that direction. Uh, now you've got the good guys and gals that can go out and be able to share best practices, share secure, uh, secure, uh, architectures and policies, and have like-minded customers really starting to, what, what it means is they don't have to reinvent the wheel, they can put it in production. It's been tested and validated, and we will continue to stamp the architectures that we've gone through and tested.

So these are real world, uh, AI value where it's not like, Hey, here's just a, an agent that doesn't, can't do anything. The the fact that we're taking an agentic approach, but we have the ability to push and, and be able to, to make safe and predictable change. There's very, very exciting value ahead, Mike.

My suspicion is there might be more agents in the future as well, but how do all the agents talk to each other so that they can hand off tasks back and forth? Because the networking agent will need to talk to somebody else's server agent and somebody else's, uh, application agent at some point, right? So, so this has really, in my my view, this has not been, uh, accomplished yet in the industry.

This all, it's all talk at this point. Um, having said that, you're completely right. This is where the future is going.

So Federation of Agents, now agents can, uh, gain information from each other and be able to interact and you know, it's yet to be determined exactly where the human will be in the loop. But from our customers, they do want a human in the loop, uh, to be able to make important decisions where if there are, you know, some more simple types of tasks, things that are, let's say, gold standard that needs to be remediated, the customers will allow that to just run on itself. I would say the sky's the limit of where this can go.

And we're gonna take a very real world approach because the last thing I, I want to do, because I've never done this, I've never got ahead of our ourselves and our skis. I wanna make sure that our customers bake it. I wanna make sure that it's in their networks at global scale with 30,000 to 150,000 plus devices and they're seeing value from it.

Well, let me give you a couple of examples. Uh, one example is an observability agent. So most of the observability platforms out there, well actually all of them, they look at problems.

They don't actually fix problems. We're not interested in being an observability company. That's not what we were designed to be.

There are great companies that are out there, you know, ala Ken Tech, uh, you know, you look at some of the things selectors doing that, you know, some of the folks that, that spoke in network automation form, and they go and see issues. So if we have their agent be able to interact with our agent and say, okay, here's an issue and we believe it's, it's down to these devices or these parameters in the network, they can actually hand that off and create an integration where Gluer can go off and make those changes. Then gluer to ServiceNow inform Ken Tech that the problem is fixed and, and life is good.

In fact, two years ago at NU Open Network user group, we showed Ken Tech and Luer interacting with a live Zoom call. Where in that POC, there was a Zoom call, someone made a change in the network, the Zoom quality went bad. Uh, Ken Tech notified Gluer gluer often fixed it.

Now, these were not two agents that were talking, these was, this was a dedicated integration. It's gonna get much more interesting when agents can interact and talk. We're not there yet.

I don't believe anyone's there yet, but that is exactly where the future is, is going. So that's my view. To what degree will I need to be a specialist?

I mean, I think we all agree that some human will be in the loop, but does that human need to be a networking specialist going forward, or can they be more of a traditional IT administrator who is maybe networking savvy to a degree, but they're trying to manage all these things in concert with one another? We have, we have roles for both. And that is the, the part of the key with the glue wire platform.

In fact, one of our, one of our customers calls it the, uh, the brain drain prevention, uh, machine that, that a lot, one of the most sophisticated network engineers come and go when they go. A lot of the, the, the, the, the historical knowledge goes with them. Once the designs and architectures are baked into glue air, and there are, there is part of our platform that allows the network architects and the senior network engineers to be able to onboard their designs.

Once that's done, then that can be handed over to some of the less sophisticated folks that are more network operators and expose a handful of knobs to turn things that won't like go out and break the network at scale. But, but some of the best practices to go through and be able to remediate, uh, things that are, that, that may be broken. You know, one of our, one of our advisors, Kevin Carney, uh, he was the chief network architect at, at MasterCard, and he, he selected us when he was at, at MasterCard, has been on auto UGA a whole handful of times.

Like what is, what is one of the biggest issues that happens when you're troubleshooting a network? It's like, well, uh, when you're removing security restrictions, a lot of times either network engineers forget to reapply them or they don't stick because they're in there manually troubleshooting. And so, you know, part of the value for customers is that if there is something that's detected that, that the network ops team has the core best practices that are safe and predictable, where they can go through and push a button or agree that this can go back to gold standard, even with a human in loop or without, and things will get, get, get remediated and, and put those security, uh, the, the, the security policies back on, on, on the devices.

And so it doesn't require everyone to be an absolute complete rockstar and black belt. Um, that there has to be both. And the other thing that I would say is that there's a lot of challenges, and we've seen this throughout our customer base, you can't be the best network engineer in the world and the best developer and scripter in the world.

Now, there are a handful of unicorns, but that's not, you know, everybody. And so a way, you know, as you said, democratize, uh, automation, I believe, I think you said something democratize scripting or playbooks or, or whatnot. I mean, that really is the approach that we're taking, but it's more about enabling those to what, what their strengths are and allowing them a path to be able to move to the other side more of an elegant manner.

Because if you have to build everything from scratch, you have to have really, really great network engineering. That's one person. Then you have to have really, really great, uh, development.

That's another person. Now you're taking two people out of the workforce to be able to try to self build something. So if you can give 'em a platform that allows them to go faster with the skill sets they have and meet in the middle, that's why customers are buying blue Air.

Where do you think security will fall in this equation eventually? 'cause it does seem we're seeing a lot more convergence of networking and security, especially around security operations. Um, are, are these things going to meld somehow and what does that look like?

They already are, and that's a great observation. I believe this is happening very, very fast, Mike. So we've seen a lot of teams that, that have actually come together where you have both the network and security that have been collapsed.

And so security policy is, is is a really big deal for ware moving forward. Um, it's difficult to have a security policy platform for traditional firewall rules and then a network platform. And customers are asking us, why are the two platforms?

So you'll be seeing some pretty large announcements from, from glue air, uh, and what we've already done with security policy throughout, throughout networks and, and these roles, uh, are, I'm seeing converge because you can't just think of networking separate and have your hands in the air about security. That security has to be a key ingredient. It has to be a way of life and make sure that everything that network engineers do in their designs are secure.

And so if you can have multiple roles in an automation platform that cover both traditional networking as well as next gen networking as well as IO OT and o ot, and by the way, massive security vulnerabilities in in iot, NO OT throughout, you know, those networks that are not regularly patched, that are not regularly, uh, segmented with macro and microsegmentation. And it's a real difficult pain point for customers to be able to, to to solve. And so if you can have a platform, if customers can have a platform that not only handles multi-vendor, multi-domain and traditional networking as well as next gen networking, also, uh, security policy, firewall rules, et cetera, and then moves into iot O ot, this is a platform the customers are highly, highly interested in.

Outside of, you know, acquiring your platform, what are you seeing customers who were well prepared for this next wave of automation doing? Or is there a mindset here? Is there a cultural change?

Are there, uh, is it just technical training? What's kind of the secret sauce? It's a work in progress.

And I would say that that it, I would say the big, the big change is doing things differently. And it has to be driven from the top, meaning top of customers has to be exec driven. The customers that have been able to get the furthest along and have the most value, both financially from security and outage prevention.

This is driven by the C-I-O-C-T-O VP of it, depending on the size of the organization, where they've bonused their teams on the transformation. And if they do it the old way, meaning the manual way or go off and, and write a script, if it's already baked in a model with glue air, if it's not baked with glue air, if it's, if it's something that the script's been working and that's part of an RPA workflow that is tested and approved, then great. But if they go off the rails and they go and do something manual that there are repercussions that like, no, they can't just go off and make manual change.

And so, you know, the adoption of everyone rowing the same direction, not they say something in, in, in a meeting and then they go off and, and still do it, the, the, the, the same way that doesn't work. And so this is more human behavior changing at operations more than anything else. And the best part about it is that these network engineers and these developers, they get their weekends back.

Like they're not getting the calls at four in the morning. We've had, uh, over a 99% reduction in network outages throughout our customer base, 300, uh, times faster OS upgrades. We're seeing a 50 to one time and cost savings with customers.

And so if they adopt it, if they get with the times and they look to do, they're gonna free themselves up for more of the strategic things. And instead of, you know, wrapping themselves into just like, trying to keep their hands on the keyboards, that actually they'll do be doing things for the business that moves the needle more. And, and when the IT execs recognize that behavior and they reward that behavior, then we see transformation.

Now what I would say is that it's really, really hard to get that transformation if everyone is taking just a pure scripting and self building mentality. If you couple 'em together, meaning with best of breed platform plus some customization and scripting on, on the side, I mean, it's pretty amazing what what can happen. And we've got some just amazing, amazing customers that have, have been able to move mountains and get a tr tremendous amount of value, which by the way, is why a private equity firm has now moved forward with gluer because I believe we are the first company that has gone the private equity route, which means we get to continue on the mission, we get to continue to grow, and we really get to continue to be a leader in the space instead of ending up in, you know, a business unit of, of a Strategic.

So, all right, folks, sharing it here. There's an old saying, if you keep doing the same thing and expecting a different result, you just might be crazy. So it's time to think about it differently, right?

Hey Jeff, thanks for being on the show. Thanks, Mike, I appreciate it. All right, and back to you guys in the studio.

Hello and welcome to the Techstrong AI podcast. I'm Amanda Ani, and with me today, I, I'm excited to have Thaa Vasu Devvin. He is the executive VP of Product for Sky High Security.

How are you doing today? I'm doing very well, Amanda. Thanks for having me on this podcast.

Happy to have you on the show. Can you share a little bit about Sky High Security and what services do you provide? Absolutely.

Um, Skyhigh Security is a cybersecurity vendor, and we are specifically in the security service edge market, which is part of the broader SSE market secure Access Service edge. And at the heart of it, uh, Skyhigh security helps organizations protect their users, uh, as the access, uh, applications. Applications could be cloud applications, applications would be on-prem applications, doesn't matter.

But to be able to provide the zero trust, uh, security for an access standpoint for their users. And once they have access to these applications, we help organizations to be able to protect the crown dwells, which in today's world is really the data. So being able to protect access and then being able to protect data is skyhigh security's, uh, biggest strength.

And we do this, uh, from, in a unique perspective because we are the only vendor that offer this ability to do this both on-prem and on the cloud, and also do this in a hybrid fashion. Wonderful. Thank you for sharing that.

So, our topic of discussion today is, uh, why an AI leader is necessary in creating a strong and secure AI strategy. So from your experience, can you share what are some of the challenges that leaders are facing right now? And and how do you suggest that they solve this, uh, with, um, a strong AI strategy?

Yeah, that's a very relevant question, uh, for today's times. Um, as you know, we all know that AI applications, um, uh, are indeed on the rise, uh, in pretty much every organization that we speak to. That is all that is already an initiative for leveraging the power of AI into their day-to-day operations.

So what this means though, is that this comes with, uh, heightened security concerns. Uh, as an example, if these applications, um, you know, the way AI applications work is that they all, they require access into vast amounts of sensitive data within the organization, and then they have what they call a, a learning on top of this data before these applications can be, can be leveraged. So at the end of the day, uh, how do you make sure that your sensitive data, uh, is not being leveraged or being trained into these AI applications?

So that's one part of the puzzle. The second part of the puzzle is, uh, do the right set of people have the right access for these AI applications? So, as an example, if it's an application that's being built internally to mine, a lot of the customer data, and then being able to provide rich valuable insights, there may be certain insights that only the executives should be able to see, and there may be certain insights where a marketing person or a customer success person can see.

So to be able to ensure that the right person with the right role has the right access to the right AI application becomes a key part, uh, of the security, uh, ask as well. And lastly, every region, every industry is, uh, you know, evolving, uh, as, uh, you know, the use of ai. And so a lot of regulations are coming up, uh, on the use of AI and, and the use of AI applications in the industries.

So to be able to understand the compliance and regulatory challenges, uh, becomes, uh, an interesting challenge as well. So why do I mention all of that? Because these are all the big challenges that an AI leader within an organization needs to think about.

A, he or she needs to think about what is the business need of that organization and how can ai, how can they bring in the power of AI into the organization so that they can bridge the gap between, uh, the power of technology and power of what, what the organization really needs. So we have to make some IT decisions, as in when we build these AI applications, what LLM models are we going to use underneath the covers? Are we going to use a pass service host an AWS or Azure or GCP or OCI, or are we going to run this in-house?

Uh, which means that's gonna be capital intensive, uh, but it's much cheaper to run it on an operational basis. So then they'll have to make these decisions on security. What kind of security controls do they need to ensure that, uh, data doesn't, uh, get exfiltrated the right person has the right access and ensure that we manage through the regulatory and compliance risk?

So really the, the significance of the AI leader then becomes defining the AI strategy and roadmap, which includes, of course, security and then ensuring that the organization is well positioned to take advantage of the power of AI for their business needs. So once you have a good AI leader in place, do you have any advice or tips to ensure that there is good communication between that AI leader across the organization, organization in be and between all important departments? A hundred percent.

And I think that is one of the key responsibilities of an AI leader, is to drive what we call strategic alignment, which includes cross-functional collaboration as well. So what I mean by strategic alignment is if the AI leader does not define a clear strategy for the organization, it can very quickly lead to disjointed efforts across the organization. You'll have different business groups leveraging, as an example, different LLM models for meeting their own needs or leveraging different SaaS applications to meet their own needs.

So the, so one of the key goals of the AI leader is to ensure that all AI investments within the organization, they're aligned with the company's strategic priorities. And the, the important thing to do here is because most of the time these AI initiatives, they span multiple departments. So the AI leader then acts as the unifying force, you know, ensuring collaboration across technical teams, business units, and also of course, the executive leadership so that they can achieve these cohesive results.

And then how do they track the end results? Um, you know, as, as we look to integrate ai, a lot of company leaders, I've been reading some articles, they, they don't see that end value result that they expected. Uh, so what advice do you have for how do they achieve that end goal and track, uh, the value from it?

Yeah, I think this is a very good question because this has come up in so many of our customer discussions about as they're double, you know, adopting AI within their organization, how do you find the right return on investment? I think it all starts by setting the right measurable goals, uh, by the AI leader. So for each ai, I mean, where we have seen this work well is where for the AI projects, the AI leader, uh, you know, defines the success metrics for each of that project.

And it depends upon what that initiative is. So as an example, um, one of the com uh, one of our customers, they were leveraging the power of AI to mine through, uh, customer documentation. And the reason why they wanted to implement that was so that their end customers have a two things.

It, they can find the right information faster, and number two, they don't have to spend a whole bunch of, uh, time in navigating through the documentation site to figure out the information. So again, kind of related to the first point is they wanted them to find the information faster. So then if the, in, if that is the overarching business goal that defined the AI leader will say, if I'm going to be leveraging the power of ai, first of all, how does the experience then get better?

And now as the end user comes and searches for information, then how do we make sure the information that they're looking for is indeed the right information and the customer's satisfaction is still very high. So those are the two key measurable goals that they will track. And if they see that the changes that they have done on the AI project are already lead leading to better measurable outcomes, that is indeed the success from that initiative standpoint.

Wonderful. And then another question is, AI is advancing so quickly and rapidly. Um, how do business leaders keep up and, and keep the most current, um, aspects of AI as it advances so quickly by the time they get one thing done, it it might be already obsolete?

Yeah, yeah. This is, um, you know, it's indeed true. Um, this, the space is evolving very, very fast.

So there are multiple, uh, way things that are evolving. I think the first piece of the, the technology itself is evolving, right? There's no questions about that.

Uh, every day, um, you hear about new AI SaaS applications, uh, coming up, which should solve a specific problem for the business. Every day you hear about new LLM models coming up, or existing LLM models getting updated. 0.

You know, this just keeps on enhancing as things go along. So that's the technology being up to date on technology. The other aspect of it is that the security aspects of it are also evolving.

Uh, what, what I mean by that is, you know, you, you need to ensure that you are on top of what, what are the security considerations I need to have for AI applications? What you used to be doing for other SaaS applications may not be necessarily true that I need to do for AI applications. As an example, previously you were probably happy just saying, I'm going to allow or block a certain application, or I'm going to not allow sensitive data from getting uploaded into that application.

But in today's world, for you need to look at one level below, what is the LLM underneath the covers, what is the risk of the LLM model being used? And so as the AI leader, you need to think about and keep evolving your understanding of security as well. The, the next awareness that they, they always have to be is on regulations, uh, governance and regulations and compliance, right?

Because every day you hear about different countries or different regions coming up with new AI regulations. So if you are a multinational, if you are operating in, in different geographies, then and, and you are dealing with a whole bunch of AI application with sensitive data, uh, then you need to be on top of what are my, uh, you know, how do I make sure that I'm, I'm compliant with the regulations of those regions? Because anytime you're non-compliant, then all the then, and then the, you know, the impact of not being compliant would be fines and bad PR and all that, which is all going to be a negative impact.

And it'll, it'll wash away all the great work that he or she might be doing with the use of AI within the company. Absolutely. So if there was one key takeaway you could leave our audience with today, what would that be?

Yeah, I mean, the one key takeaway would be embrace the power of ai. It is once in a lifetime technology and it is going to make your life yours as an organization's life much, much easier, making them super more productive. But as you do that, think about definitely the security implications of that.

Security needs to be bolted in right from the very beginning, making sure that sensitive data is well protected, making sure that you have zero trust access to these applications. And of course, last but not the least, always be on top of governance and compliance as you build these AI applications and then deploy them within the organization. Wonderful.

Well, thank you so much for coming on our show and sharing your insights with us today. Thank you, Amanda, for having me. And thank you to our audience.

Stay tuned. There's more. Hey everybody.

Mitch Ashley here with DevOps Dialogues. I'm VP and practice lead of DevOps and application development with the futurum Group. DevOps dialogue is all about conversations about creating software in the era of ai, cloud security, all kinds of aspects of the we have to deal with of creating the best kinds of applications that deliver the results that our customers, our business, our partners, are gonna be most fulfilled from and receive the greatest outcomes.

So we're gonna be talking about Gen AI and doing AI projects and, uh, some tips and information that will help you all in doing and, uh, taking on those. And maybe you've already started a, a project and looking for some good advice or information, some insights. We're gonna do that by the two folks that are joining me on the podcast today.

First is Ed Eduardo from, uh, do It. He's Senior cloud architect and Joby George, who's global head of partnerships at Wvi eight. Welcome gentlemen.

Eduardo, would you introduce yourself, tell us a bit more about you, and also, uh, tell us about Do It? Absolutely. Thank you.

Well, um, I'm in Canada, so the weather over here is very cold. Sorry to get cold. I'm a senior cloud data architect, and I've been working with AI mls, uh, for quite a few years now, uh, before Gen ai and at Do It, what we do is we'll help our customers really unlock the true value of the cloud.

And so we help them strategize, create architectures, and really become efficient when using the cloud. Fantastic. Joby, I'm Joby George.

I run the global partnership at VVI eight and manage all our cloud technology and system integrator partners. VVI eight is an open source vector database, uh, for building, uh, AI native applications. Without developer first approach, developers are able to go and use LMS and embedding providers and frameworks to build their AI native applications.

And we, and they are using it to build applications like hybrid search, semantic search rag applications, or some new things like recommender systems or generative feedback loops. And now the agent rags, we have around a thousand plus customers like Cisco, Morningstar Bunk, who are building all kind of gen AI and search applications. I'm glad to be here.

Thank you for inviting me. Excellent. Thank you both for being here.

Thanks to your companies for being here as well. You know, le let's start out about talking first about, uh, kind of taking on gen AI projects. Uh, it's an exciting time to be in the industry.

There's, you know, technology's changing every day. There's announcements every, seems like every week, every day, whether it's Microsoft or AWS or you name it, you know, a new model coming out. There's so many, uh, kind of technologies to learn.

I know from my own experience leading software teams, you don't always want to venture into this alone, um, for fear of it becoming too much of a science project rather than really getting value out of a project. I'd love to hear your thoughts. Um, maybe Eduardo, if you wanna start out first, since you work with so many customers building these kind of applications, um, what are, what are some of the best ideas about how to get started and how to decide what kinds of applications are best to take on with generative ai?

Yeah, I mean, we, we are, there is so much going on. There is just so many things as you were mentioning. Um, and we take an example of AWS, there is so many services available to our customers.

So a lot of the times we hear like, Hey, I wanna use GN ai. I really wanna leverage what is possible with the technology. I don't know where to start.

And really it's starting from the place where, what is a good use case for personalizing the customer's journey? How can you really hyper-personalized that experience to get the most value? And everybody's collecting all this data, it's just very powerful data that allows us to get into the nitty gritty of that personalization.

Now with Gene ai, we are able to unlock that. Before we were creating personas, we were creating buckets that will put a customer in a certain category, but with Gene ai, you can really go dive into that custom personal experience. And if we take a, like I was mentioning in AWS as an example, they have great service called Bedrock, where you don't have to create an infrastructure, you can start utilizing it with, uh, on demand and send text.

You only pay for the amount of tokens or the amount of words that you send out and off you go. And it's a really great way to be able to start testing what is possible. Now, I do have to, uh, whenever I talk with my customers, I do have to mention that HN AI is not a silver bullet.

Like nothing in nothing in technology, right? So it is great at certain things. And when you are doing this test with Bedrock or with any other cloud provider, make sure that you test that limit.

Where does it start breaking? Because that's the iteration where you are gonna be able to start making progress and start adjusting things. So it fits your use case.

But, uh, to start with is think enough about that hyper-personalized customer experience that you wanna deliver and then start testing with the, the managed services available to you in the cloud. So that's kind of like where I will start. And That's a, that's a great, great point.

And, um, I'll, I'll just add to that, uh, that itself, like, um, uh, so for example, VV eight is one of the companies which integrate with Bedrock. I think, uh, just to roll back a bit, right, uh, generative ai, this is a totally new way with almost feels like, uh, almost like the internet browser coming into the market type of a landscape. And everybody wants to build all of these application.

Everybody wants to build an application, which looks like a chat GPT or equivalent of those applications. But, uh, they, they might not have the skill gap, they might not have the real understanding of all the use cases which they can go and tackle. So kind of taking a more selective approach to how and what they can build and what is the platform kind of fits in for them as they're building these applications becomes super, super critical.

But the possibilities are endless. Uh, as Eduardo said, there are all kind of applications, paper are buildings starting from simple search type of applications, rag applications. We always had a search or a database application architectures to build these applications.

But, but machine learning and the generated AI has totally changed what the information retrieval and the quality of the information which is being retrieved from dramatically. So now we are able to get to relevant information. Eduardo talked about personalization, so kind of being able to do that, personalized apps, uh, so we have an approach where we call it as a generative feedback loop, which allows you to build these personalized application at scale.

And, and that's where the motion is. So some of the things I would kind of like highlight is that be very careful on what the use cases you're trying to, uh, approach. Do you have the skills to kind of do those and kind of quantify and, uh, approach it in a way that you can de uh, deploy and get ROI on these applications quickly?

Go ahead. Sorry. No, go, go ahead.

I like, you know, you mentioned a lot of great things and you start getting a sense of it, uh, when you are starting to test because you get a sense of where the gap is on the skills and where the gap is on the data. 'cause the data is a great like model and the oil that will get all these going, right? And for, for all, for every company out there, you have access to the same models.

You have access to better, you have access to all these other model for other cloud providers, but the differentiator becomes the data. And so when you start testing, you'll be able to start identifying, oh, I can I start using this other data this way or, or this other way. So yeah, I think those are great points.

Yeah, I, I was just gonna bring up the data, right? It's really the center of the, the universe is also oftentimes is deciding where do I put my turn of AI application? Where, where in the cloud, et cetera.

Where, where do we need that data? Uh, you know, one of the, with so many things happening in, in the a AI fields, particularly generative ai, you know, we heard a lot about rag people, augmented generation. Let's talk a little bit about what that is and why, why we use that with the importance of it.

And then also, of course, we hear a lot about agents, right? Being able to, uh, create your own agents and a lot of different capabilities that are being launched in the market. I suspect we'll see a lot more over time.

Um, anybody wanna take on rag? I think maybe, um, Joby, you were kind of mentioning that earlier. Yes.

Um, so RAG is a very logical, uh, evolution from a search based application to kind of building a retrieval based approach to building application. So think of a native rag pipeline consists of two parts. One is a retrieval component, typically basically composed of an embedding model and a vector database, which is VVI eight is one of the providers in that.

And then there is a generative component, which is the LLM or the models which you can use. You can use foundation models, you can use your own custom models, whatever you want. So at the inference time, or basically what you're trying to kind kind of, when you do a query, what you run try to do is do a similarity search, try to find an index of documents, which is a subset of the documents which are more closer to the kind of curry you are you are trying to put in there, which allows you to retrieve the most similar document and provide it as a context to the LMS to build, uh, build their response, a generative response back to you.

This is fundamentally different than just doing a searching or aql query type of an approach. Now you're able to build some interesting applications on top of it. You are able to take the plain old chat bots, think of it as like four or five years ago.

And now they are really looking like they're really answering your questions in a more relevant way. You're also hitting information which is very relevant to the context because you have actually done that similarity search and that has provided that context, which allows you to now provide a response back, which is very relevant to the information you're trying to retrieve at. So that's a rag approach, and I see that the RAG is now kind of transforming to what we call as an agent rag, where you are taking the, both the LLM and the database and now adding some kind of a memory and a workflow tools, uh, as an elements to it.

So it's a very logical, um, evolution of the generative AI applications from rack to agent rags and to eventual, uh, more complex workflow based applications. And to add to, and to add to this is that there is a race to be able to identify the best content for the question or the task at hand. And we need to, like, organizations are looking for ways to optimize that search.

And so databases like Aviate, uh, help allow these search to be more efficient. The longer the text that you send to the, to the model, the longer it will take, the higher the cost will be, and the more noise you will introduce into, into the inference. And so there is a lot of repercussions about that.

Um, and so we are trying to identify what is the right amount of data, the minimum amount of data required to answer these tasks. And there is a huge feel that is going into that. Now, on the agent side, there is also great, uh, work being done around that.

And in a way I'm like, I don't like the work agent too much. I like the word tools better than agent just because it is more like a tool that LLM can use to retrieve data or to do other actions. So there is this combination of like vectors and embeddings that you have in a vector database, but also a lot of organizations have data, data in relational database like my SQL or Postgres.

And so they wanna access this data as well, right? So it's another way of being able to use tools to access data, get the specific points that they want, and the, the prompt that is being sent to the model and make it as short as possible. So that's kinda like the sweet spot that everybody's looking at, how can I get there?

And these databases, vector databases are making a big difference when, when doing that. You know, a couple other points too, uh, rag is a way of, uh, leveraging data as part of kind of the front end to the model without exposing the model to your data or vice versa, exposing your, your data to the model, kinda how you can augment or add to that additional content. And to your point, SQL databases, it could be documents.

There's all kinds of different, um, uh, points of which we can pull data from. And you mentioned vector databases, uh, Joby, one of the ways we can kinda do high performance retrieval of information out of, out of, uh, generative AI models, correct? Yeah, No, that, that's absolutely right.

And um, like one of the point I want to kind of come back to is like around the data silos and the data we are talking about. Like I come from the data space. I, I lived through the big data wave and kind of see this kind of a wave come in.

So, um, and I think we are in a very early ginning of the gen ai, uh, genai landscape from that point of view. There also was a promise of going and taking the unstructured data and making it available relevant for you to kind of go and build applications, insights, and analytics on top of it. I think we are kind of doing the same iterative loop in that that way.

But now the game has changed a lot, uh, from the point of view of like earlier it was a text based, it was structured data, which you can actually go out now by taking each individual data element. So be it data sets tables have the metadata and the schema and some context which you can capture in a structured data. Taking PDFs, taking images, being able to take all of those multimodal data sets and building, bringing it all into the same vector space and then kind of doing a relevant data search makes it super, um, super intuitive and super useful for creating some very natural looking applications, uh, which are very different than a typical database type of application.

So I think that is the big jump which we are seeing. And again, it's a journey. Like if you look at the progress around like the new things which have come around Colbert Co Poly and some of the stuff implementations we have done around acon, this is a journey and I think we are just in the early leaning of it that where the lot of innovations will come in, which will make it super important, uh, for a vector database to be a core component to building some of those applications.

And that's how we tell our story, which is like we talk about AI native stack, and when we talk about AI native stack, basically LLM is the core foundation and a vector database so that you can actually build the applications on top of it. And then the underlying layer, the architecture has to be right, like it has to be containerized, it has to be scalable in the cloud. Like AWS being able to kind of get that scaling is critical part as your data sets grow and you bring in all the data into a vector space point of view.

Very good. You know, one of the things with working with the new technology is kind of keeping costs in mind, right? And these are new services that we might be using from a cloud hyperscaler like AWS, um, but we don't have experience.

I mean, we had that happen early in the cloud, right? What, what does our cost profile look like as we start to consume these resources? Um, one, one of the things to cons, what are some of the things to consider, uh, in terms of costs, terms that either anticipate learning from from experts like yourself or that you'll experience once you start to develop an application and see what its performance and consumption profile looks like?

Um, Eduardo I also like test a lot the rate fast and fail fast. I mean, it seems like, uh, there is a lot of entrepreneurs out there that are doing this to be able to get business up and running. Uh, when it comes to gene AI and these workloads is the same thing.

There is no one solution fits all. And so you need to iterate fast and understand what are the limits of the technology, right? Sizing these workloads is very, very important to reduce costs.

And that's true across entire cloud, not only on gen ai, but every workload. Uh, it's not simply by going to the cloud means that your bill is gonna be cheaper. Uh, rather you need to understand what the architecture is, make important choices, right?

So for example, on the data side that we've been talking about, we can easily put a lot of data in S3, but if you don't have the right format and the strategy there, you can be paying 90% more or a hundred percent more to access that data that if you have the right strategy, that you have the right format and know how to access this data, right? And that has an implication not only on the cost, but on the latency of the model, which ultimately is your paying for how long that model is running. So all of this is, has a effect if you do not understand the building blocks to optimize every layer of the workload.

Um, and that is goes with tools as well. Like we were talking about rack and agents, you can create a lot of tools for the model to access many different things. Great, fantastic.

But it doesn't have to because it needed in order to do that. And just a quick tip for those that are listening and you are working with agents, when you start adding four or more agents to the model, there is gonna be a diminishing return on the quality of which tool is being utilized. And there is a tendency to use the first tool presented to the model.

So if you have four or more, the fifth tool is not being utilized and you are having this code there for nothing or paying for it for nothing. So all these architectures we need to revise. That's something I love to do.

I love to drive it with my customers and say, okay, what is it that you're trying to accomplish? How do we architect this to fit your current estate, reduce your bill, and be able to get you in a path where you are able to build for the future? So, I mean, it's a long answer because there is no really super bullet to all of this, but I mean, Joey, you, you, you experience that, I mean, you're in the database space, right?

Yeah, I I can certainly add add to that, right? And I kind of like break it down into two parts. Uh, one is on the like development and developers and others on the deployment side, right?

So if you look at from the developer and the a and the development side of the things, everybody wants to build charge p everybody wants to build the most complex generative AI applications. And hey, choosing the tool, being able to look at the use cases and quantifying the use cases to the segment so that, what is the ROI going to look like? Uh, because nothing comes for free, as Eduardo said.

Uh, you can do as fancy applications as you want. Uh, if you're doing a e-commerce application and you're trying to put out a personalized shopping cart and you have certain SLA on QPS for you to do that serving, you cannot do all the aspects of the generated AI to kind of bring that applications to life. So being able to kind of do that trade offs, like very standard practices across any workloads, those are all the standard practices which apply costs do add up very fast.

And in the sense that this technology is evolving, the, it's in that stage of the eco, uh, stage of maturation where a lot of innovation happening. Optimization is still kind of coming in as part of the logical maturation curve. We have started focusing a lot around cost versus performance.

So a lot of the features we have introduced things like multi-tenancy where if you are not you, you can break your workload into multiple tenants, then you can offload some of those tenants down, you can offload them all the way to the disk and save cost. So be being aware of the cost consciousness about these deployment models is super critical. Coming back to the developer side, we kind of look at it like we built it on Kubernetes.

We built it on AWS uh, basically to provide that choice where customers can start on a multi-tenant SaaS type of an offering with like $25 a month type of an offering and build an experiment. But as they go bigger, they can actually go and get to more of a managed hosting. So we basically, we are able to take the same, same environment, move them into a single tenant offering from our end and or they could start with they bring your own cloud kind of an offering and experiment with that.

Most funny thing is that as people are deploying this, uh, use cases, what you're saying is that their cost and ability to manage these deployments is, is not there because again, evolving ecosystem that they're coming back to, uh, us in some form to go and host it for them and run it because that is a lot more cost efficient than them trying to figure out and going and running it. So there are a lot of these pieces coming in because of the state of the market it is. That's really good point, because oftentimes the architectural decisions you make the service not just the services you choose to make.

You can create very high consumption, you know, types of applications, particularly in AI and not realize you're racking up a lot of costs. And that's of course the fastest way to get your second projects, uh, canceled is have the first one go way, way outta out of control. So you wanna think about those costs upfront, right?

Even if you don't know what they are yet is, is understanding what's driving costs and maybe you can make some adjustments in your design, your architecture along the way to better utilize the money that you can be spending to operate these kind of applications. And I think that's where the cloud really also gives another benefit. All these managed services where you don't have to spend human hours setting up an infrastructure, but rather start getting a sense of how much things are gonna cost at a smaller scale.

And then you can start seeing, okay, if I send this product, if I get this output, this is how much it's gonna cost me. And starting to extrapolate on putting thresholds to be able to get there. Let's talk a little bit about, um, I mentioned earlier some of my experiences when working with new technologies is it's fun to learn it yourself, but that isn't always the most effective way to get it done.

I mean, you make, make a lot of the same mistakes that other people have already made. Some of the learnings you can gain from working with others. Let's talk about the value of working with, uh, organizations like, uh, yourselves.

People that have been down this path multiple times, maybe worked in different, uh, different scenarios, different kinds of applications, and how that can help accelerate, uh, maybe a new customer that you're working with to get not only their, uh, their project delivered on time or successfully, but accelerate their own learning. Absolutely. I mean, for, for me, I've been in the trenches, right?

I was a DevOps engineer before trying to figure out AWS and trying to be the most optimized way to it. Spend hours going through documentation, trying to figure out, and at the end of the day, I'm like, am I doing this right? And that's what I love about working with DUET and myself here, because we are a group of 300 engineers across the glove that have expertise in everything around AWS.

And so we collaborate with one another to be able to get the right answer to the customer, understand the requirement and what the customer is trying to solve or trying to build for. And then being able to cut all the noise of what is not necessary and be able to hyper focus on like, this is the best architecture that you can build today that will grow with your, with your business, and will keep the cost in balance. And then that will just help the, the customer reinvest, reinvest all those savings back into the cloud and growing the business.

So for us, dot, uh, is an extension of every team, every engineer, team, and every organization. That's true. And I can add add to that, like, um, I think, uh, being able to, like we, we are creating a distributed new database and, and we are an early stage where a lot of experimentation going on and now a lot of them are now going into production.

So a lot of like moving parts at this point and being able to have the help across from AWS and others being able to kind of build the up the database in the right way and e out wherever we can, the cost and optimize the performance and cost for the customer is super critical. Because as, as we were talking before, the cost starts adding up pretty fast. And being able to go there and go under the hoods and find out like how you can go and do this particular optimization at the Kubernetes level four S3 would help you to get some of the cost advantages is critical.

We also do things like, uh, based on your QPS, like what is your expected query speeds and things like that, we can then go and find the cheapest way for you to go and run a certain machine configuration. So, for example, we run a lot of our production workload for our customers on graviton and, and, uh, a lot of, when, when people talk about generated ai, they talk about cheap use. But I think we are seeing a lot of our actions on the low cost side of things because cost is a big factor.

And as more and more deployment starts happening, it'll become a bigger and bigger factor. What is the performance and cost goes are going to look like and how you deploy things in production, uh, becomes very, very important. Very good.

I would just add to that my, my general advice in this particular area, things are changing so fast. So many things are being introduced seems like on a daily basis, it's, it's tough to say what's the best technology you use? 'cause it's changing that rapidly.

I think you're more, more, uh, on, on the good track by picking the right partners to work with because the technology will change. Our understanding will evolve. Our learning will certainly increase, um, as the technology changes too.

So you wanna kind of pick the right horses to, to be a part of, to ride if you will, not just necessarily the best technology. 'cause there are gonna be a lot of really good things, not just available today, but coming out real soon. Just, uh, just open up your browser the next day.

I think you'll probably see a new announcement. Well, let's, let's, uh, wrap up. Uh, I'd love to hear a little bit more about, so folks that are interested, wanting to find out more.

Like how can you, how can you help me on the journey that I'm, maybe I'm already down the, down the path on an app or I'm looking to get started and wanting to, uh, kind of of accelerate my own learning. Um, what, what are some, what, what's a good way folks can engage you to find out more? Sure.

com. We have a wide range of services available. And just to point out, GA accelerators my favorite program we offer.

So hopefully you can check it out to get you started in the gen AI journey. io where we, there are a lot of gateway to a lot of ways. You can get a lot of information on documentation, slack forums via are open source.

You can go to GitHub, play with the code. If you're of that type or you want to go and basically just get started, go to our VV cloud. Uh, you can get started and get 15 days, uh, for a free trial.

You can, you can run your sandboxes there and provision your clusters and get started in a few minutes. Uh, so test it out. That's where to learn.

Get started, right? Jump in. It's a con.

AI is a contact sport. Yeah, get engaged. Well, thanks to you both, uh, Eduardo and, uh, Joby.

It's been a pleasure talking with you today. And please folks, check out other respective websites, uh, thanks to both your companies to, uh, do it. And, uh, we ate.

We appreciate having you both on DevOps dialogues. And thanks to our listeners, we wish you the best on your journey into generative AI and applications and beyond. Take care everybody.

Welcome back to Textron Unplugged. My name is Cassandra Chin, and today we have Al Correct. Hey Cassandra, how are you doing?

Doing Well. Can I introduce yourself? Yes.

Uh, my name is Liron. I'm a developed advocate at sny, which basically means I enjoy teaching developers about security, so application security topics and stuff like that. How to write secure code.

Can you talk a little bit about how you do security education? Uh, yes. So there's like several ways of doing that, whether you like teaching developers at conferences or events or just having, you know, fun workshops.

Um, I think specifically having fun is like a really good trait of doing it. So for example, I would say if we are making it like a, like a fun, like giving them like a puzzle to solve to developers related to security, they actually like really connect to it. And so in security there's this, uh, game called, uh, like capture the flag, which comes obviously from like the gaming aspect of it.

Uh, so you essentially try to like maybe hack a system and get the flag, get something in the system. So I find that developers and like probably a lot of people as well, just like find out like a fun activity, like an escape room, trying to figure out two bunch of activities and trying to like access the system in different ways. So like, that's one way of asking, doing security.

Oh, you mentioned hacking. Like normally we think of security as a good thing and hacking as a bad thing. So how are the two related?

Um, a good thing or, and a bad thing For security? I would say good security is often invisible. That's kinda like the curse of security because like, until you get hacked until like something bad happens, you really like, don't know, right?

It's, it's kind of like invisible. So until you get hacked, until there's like a data breach that does something goes wrong, like security is invisible, no one can like pay attention to it. So that aspect is maybe like the bad part of it.

Uh, Like why do we wanna teach developers how to hack? How to hack? I think putting them on, uh, on a space where they, where they know how to hack, like they understand how an attacker mindset works, uh, in those kinda like CTF games, we kinda like give them an appreciation to why writing secure code, why like using, you know, secure practices when they, when they write code, when they ship production apps is important because they can see how sometimes easy it is or how sometimes the mistakes are made in a simpler way, um, to like that they shouldn't have done that before.

So you really understand why security is important when you hack the code yourself. I think it gives you one perspective. Yes.

Are there any other ways that you teach security, which you find interesting? Um, well I think it, they're all somewhat related to gaming. So for example, um, there's like a cars game, so if you like, uh, I, I've been to one recently actually, uh, in Vegas, in Defcon, uh, there was, uh, gig Guardian was a company was like sponsoring one of the boots there.

And what they had is this, um, uh, secrets game. So you get a card, like a physical deck of cards, they all have secrets in them. You don't know what is an actual true secret and what is not.

And you have to like filter it pretty fast. Like, you know, also like win like get on the leaderboard. Um, and you can like sometimes make a mistake and you think something is a secret and really it's not or the other way around.

And so I think like those kind of like activities around like having a game, having, you know, something fun to do, it is, uh, is a, is a fun way of teaching it. Um, for these games, like are they targeted at a certain age of developers? Um, no, definitely not.

I think some may require some kind of like pre-experience, like understand the concept, but like secrets is like very shallow, like understanding, like I think any, any, any age, any experience level is, is relevant. So do you think that even older developers can benefit from learning security? Oh yeah.

I think everyone can benefit from knowing security, application security and information security. Everything in and around it is, is just sometimes so far away from reach because like focused as developers to just like, you know, get on the backlog, get bug fixes, get features done, and all the other crosscutting concerns like security, like sometimes, uh, performance and testing and those kind of things get kinda like, you know, trickle down because they're not as important, but it is and everyone can win from it. Uh, can you think of any like specific security incidents where like, Uh, so many, I dunno.

The recent one was, uh, I think much in the news, uh, was the X exit details. That was an interesting, uh, kind of like crossroad of several things happening. So potentially like nation state actors, like a very major, uh, uh, incident in which, uh, an open source package, a library that is installed in like, you know, regular computer systems, uh, gets, uh, you know, gets someone else malicious actors, uh, to kinda like pay a key, a key role in there and potentially like slip in back doors and Trojans and that kinda like integrates a lot of things.

Uh, both like how open source works, uh, the importance of, you know, libraries, uh, and like ownership for that. Uh, you know, CICD practices, supply chain security, there's like a lot that goes into it. And that story alone could like, you know, pivot into different, uh, uh, education parts that we could better empower developers, like understand how to kind like practice, uh, in a, in a more safe way for, for the ecosystem.

And have you always been in the security space? Like do you feel I'm actually a, a developer gone into teaching developers about security because I kinda like care about security, but, uh, my, my background is totally not security. It is, um, has been being a developer myself.

So kinda like transitioning into, uh, enjoying security to a lot of, uh, to a good extent, uh, writing about it, you know, being able to like, you know, talk to my colleagues about security aspects and practices when you are building our apps. And, uh, I think like anyone can transition into a security space. So do you think things have been more fun since you transitioned to security?

Um, this job has been more fun in general doing that. Yes. Um, going back a little further, like how did, how did you get into technology?

Um, um, it's, I don't know if it's a, it's a regular path, but, you know, I've been, uh, my, my dad was, uh, was like in and around computers back then when I was a kid, um, which is like 94 or five or so, um, to, he was really, uh, kind of like, you know, building app apps and like, what would be an excel back then, uh, for his business. And I was, uh, just spending a lot of time, you know, in front of the computer doing a lot of things. And I think my, my surroundings were basically around that.

So I kinda like, got into computers and the moment I was kind of like learning, uh, uh, basic cubase editor thing, like building my own programs was like very satisfying that I could actually do that. So, um, that really got me hooked onto computers. And from there, uh, you know, I kinda like knew that all I wanted to do was basically work on, you know, software development all day.

So whatever the, uh, kinda like the, the journey it took, um, school work experience, whatever that was just turning into, into tech has been kind of what I was, uh, envisioning it from childhood. I think it's a lot of fun that computers let you create things. Yes.

I, I think that's the key aspect of it, being able to create something sometimes out of nothing. And at this conference, are you giving a talk? Uh, yeah, just in and hour actually.

So it's gonna be fun, I hope. Do you wanna tell me a little bit about what your talk is about? What is this?

Um, it's, uh, it's much related to us, obviously security, fun games. Um, I am basically going to tell developers how even though these days there's a lot of hype around AI in, you know, generative AI chat, GPD uh, coding assistance in their, uh, integrated development environments, then why having that is actually sometimes a security concern. Uh, so my talk is gonna have like live hacks and demos of the coding assistance, um, auto completing and suggesting vulnerable code that we could exploit.

And I think until developers like see that in practice, they sometimes don't even think about it. So that's gonna be, uh, that's gonna be my ambition to show them how bad things can happen as well. So they have like the mindsets of, you know, more responsible person when they work with ai.

I think that's an interesting topic 'cause AI is really popular and we're like training developers to use co-pilot. Yes, I would say, I don't know, training developers is one thing. I think we're kind of like going into a defacto state where we're having, so, so having a gen AI tool, like a coding assistant is not, I think, inherently bad.

We use them and that's, that's gonna be, I think, the future. That's, there's no denial for that, but I think the way that we make use of that technology and the amount of responsibility we apply to it is going to be a decisive factor in terms of what is the quality of the end products that we produce as developers at the end of the day. And I think that is something that needs a bit more kind of awareness and education onto what could potentially go wrong.

I think what's really important, I think so too. Uh, I think we've had a really good talk today. So thank you.

Thank you so much. 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.

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Hey everyone, it's Alan Shimmel for techron, and you are watching the last great cloud transformation. This is a, for those who you, I'm not familiar, this is a biweekly show that we do in partnership with our friends from CloudFlare, and we're talking about what's going on. You know, the cloud has been around, well, it burst on the scene around 2005, 2006 actually, so it's been almost 20 years.

And you know what, over that 20 years, a lot of organizations have moved at least some of their infrastructure to the cloud. There's a good chunk of people who have it yet, and they may never, there's a good chunk of people who are still planning to, but you know, the initial move to the cloud was sort of these hyperscale kind of core cloud data centers, if you will. And they're great and they're big and they take a lot of energy and they throw off a lot of heat.

And we're hearing all these things about them, you know, now, especially with AI and GPUs. But we're also seeing gen two, gen three, even gen four migrations in the cloud, where, hey, it's not just putting it in the, in the one big hyperscaler data center, or even on their network. It's hybrid cloud, it's multi-cloud, it's cloud on the edge data located on endpoints in space, underwater in ice, you know, and everywhere in between.

So that presents its own series of challenges, and that's the kind, that's what we explore here on this show. Um, I'm gonna go into today's topic in a second, but I first wanna introduce you to our panel for today. First of all, joining us, I, he's actually up in Canada looking at his background today.

Um, he is pretty well known in the security space, has a lot of experience in, in the kind of things I've just been talking about, as well as currently serving on, uh, some cisa uh, panels on SBO m and Dbo and other security related frontiers, our friend Chris Bla. Hey Chris, how are you? Loving life.

Good to see Alan. Good to see everyone. Nice to have you here, um, joining Chris and I, uh, she's with, been with us before, if you've been watching this series, Emily Hancock of CloudFlare and Emily, if you, I I couldn't do the whole bio I gave there for Chris, so why don't, if you don't mind, why don't you introduce yourself to the audience?

Sure, yeah. Uh, it's great to be here again. I'm the Chief privacy Officer at CloudFlare.

I also manage our legal product privacy and IP team and our privacy operations team. And I've been with CloudFlare a little over six years now. Fantastic.

And and that's a job. Yeah. Um, God bless you.

Uh, next up my co-host and he's the CTO here at, uh, tech Strong as well as CTA at future of our sister company that we're merging with my friend Mitchell. Ashley. Hey, Mitch, great to have you here.

Always good To be talking about security with friends, and let's have a great time. All righty. So let's jump into it today, guys.

Let me, I'm gonna read you from the abstract and, uh, we will, we'll jump from there, but, you know, how can organizations keep moving forward in the cloud while at the same time adhering to numerous, and I do underline the word numerous data sovereignty and data localization laws. They need a new kind of cloud, one that transforms the network, restoring visibility and control for their complex environments. Um, beyond that though, let's dive into this, right?

People, we are so concerned about supply chains, we're so concerned about where our data is being stored or we're starting to be really concerned. And there are, and when I say there are numerous data sovereignty and local data localization laws, we're dealing at the federal level of the US state level, EU level nation states throughout. I mean, what's a poor company to do?

I mean, you know, a single entity. How, how the heck do you navigate this? What, you know, I I could see paralysis by analysis setting it.

Emily, you, you know, your chief privacy officer at CloudFlare, CloudFlare carries a good chunk of the internet over, its over its wires, over its network. How do you, how, what's the answer here? What, what comfort do you, can you give our audience?

Yeah. Well, I, I can give, I can give some comfort, um, but I will commiserate with the audience because it is an increasingly complex world out there. Um, we're seeing data sovereignty and localization, not just from data protection laws.

And you mentioned like GDPR, um, there's laws in a bunch of countries, Japan, South Korea, to name a, a couple, um, that regulate the processing of personal data of their citizens outside of their countries. And they don't always pro prohibit it, but a lot of times what they say is, if you're going to take that personal data outside of the com the country, you need to, you know, clear these hurdles or, or do these things or put these contractual provisions in place. So there's that.

Then there's a whole slew of industry specific procurement requirements that may require things to be stored locally. India banking regulations, for example, require some of that banking information to stay in India. And then we've got some certifications.

Um, one of the big ones that's on the horizon, it's not there yet, but is the EUCS in Europe, which would say that if you're providing, uh, support, uh, critical infrastructure types of, um, services, or you're supporting critical infrastructure, some of that data has to be localized. And, and then there's other countries, smattering of which, um, you know, Russia for example, have localization laws where they want data to stay local so their government can access that for whatever purposes they may want. Um, so yeah, there's a, a really complex, um, situation going on.

And so what you have to look at when you're moving to the cloud, because there is this concern of, well, what is the cloud? It's, you know, with, it's the cloud, it's, it's, where is it? What, what does that mean?

Um, you know, when it's on-prem, I know that it's kind of in my server in the basement or next door or wherever the servers are. So when it's in the cloud, you need to be looking for providers that can help you store things in jurisdictions, if that's the requirement, or that can process data in certain jurisdictions, if that's the requirement. A lot of it is really dependent on what requirements you think you have to meet.

And then you have to look for the cloud provider who can check those boxes based on the services they provide. Yeah. Panel.

Chris, Mitch, any, I, I have thoughts on this, but I'll let you guys go first. Mitch, you gotta learn to take the mute off. Okay.

Uh, oftentimes I'm, I'm kind both sides of this fence. One, have as a provider, but also having been a provider, but also as a, uh, service or of data, if you will. And it seems like one of the things that you can look towards, and I know that, um, full disclosure tech strong is a customer of CloudFlare, um, but there are, there are resources that, uh, working with companies can provide.

Like for example, the EU has their US data privacy framework. Now, security people were very used to frameworks, right? Process.

Here's how you document what you need to do and what you have to report on. Um, but that's, that's also I think a comforting factor is you don't have to go unwind all this stuff yourself. You have folks like Emily, providers that you can work with.

And I don't mean that as just a commercial for CloudFlare, but that's who, who we rely on one of the companies. And, uh, so that way you, you know, they're not there for legal advice, but they're there to help point you to the resources that you can use to try to unwind these things and figure out what you need to do in your situation. And I can think of, you know, two examples of this.

See, you know, I mentioned on the show before been public, uh, public knowledge working with, uh, Columbia and South Carolina, that the nation of Columbia, you know, on long term, uh, infrastructure plans and this level of, of visibility into cloud infrastructure. You know, saying that as you move forward at a certain point in ot, as we accept now in the operational technology world, people understand cloud is part of it. But this was about six years ago.

We didn't, you know, to have exactly these sit down conversations where, as you said, Emily, where you, you get the stakeholders together and say, you need to le localize inside my jurisdiction inside my country, this stuff, or, you know, cloud all you want, we're not doing it. And at that point they weren't. Now they aren't.

And, you know, and, and current, you know, this is how timely, this is it where we are right now. So in the Department of Homeland Security, there's a bunch of tiger teams working on supply chain issues, and we're just finishing a two month, uh, tiger team right now, uh, looking at ISACs as SBO and distributors. So information sharing and analysis centers, you know, that, that share threat intelligence already and have the last, you know, 10, 20 years, uh, doing the same sort of things with SBOs and what some of the things both of you is, uh, particularly Emily said, had me thinking about this as recently as today, working through how an ISAC can be there for the discovery of an SBO m but not actually for the access or transport.

The other two phases of sharing because of localization and liability and all those sorts of things. And just today, I, as far as I know, having the best of the first conversations about how we, you know, a year and two from now, how we work on the conversations where maybe, you know, and ISAC specifically does want to hold some of those SBOs because of conditions anyways, without going into all that. So yeah, my whole obsession with change over time leads to the kinds of things you said, Mitch, that about now I'd expect companies about like, like CloudFare to be offering about this sort of service.

'cause that's where we are. And if you need it, it's probably available. And if it's not, you know, the VC world is waiting, start a company, now's the time.

Yeah. I, I, I'll, I'll add something in here. Emily, you, you touched on it.

Chris and Mitch are both, you know, there are some jurisdictions, I think em, you mentioned Russia, uh, that said, Hey, I want my data or data relating to my citizens kept in my sovereign cloud so that I can access it if I want to. There's the flip side of that, which is I want my citizens' data kept in my country's sovereign cloud, because if it's not in my sovereign cloud, I don't have access to it. And some other country will access my citizens' data.

And, and this is a market I I, I don't remember if CloudFlare was in, but I know some of the hyperscalers were certainly doing it, saying, Hey, we will, we won't crumble, right? From a government subpoena, right? I think it might have been Apple was involved in this too, right?

We're not gonna turn, we're not gonna give them access to your iPhone. We're not gonna give them access to your data. Now, you know, once you start getting into areas of localization like this, I don't know how a CloudFlare or an Apple or an Amazon or any of these folks are gonna withstand a full on government press here and, and the power of the courts and the full, you know, faith, full faith and credit of the United States of America, right?

Telling me, you gotta turn this over. I mean, what I mean, Emily, this is, this is kind of your, you know, you live this day to day, what do you do? Yeah, well, you're, you're now getting into the history of the cloud act a little bit, um, which is a law, it's a, the, the very first big case on this was, uh, when Microsoft said, no, we're not going to turn it over data because it's actually stored in Europe.

And, um, long story short, that led to a cloud act. And so governments are supposed to have these information sharing agreements put into place so they can share this national security information or law enforcement information. We're not there yet.

The governments are, are still kind of fighting all that out. In the meantime, um, this is a little bit of what we started seeing before the US EU data privacy framework was implemented. The courts in Europe, uh, the, the, uh, CJ EU specifically was saying, we're not sure that there can be any appropriate protections for EU data as long as it's held by a US company, even if it's held in the eu.

We've started to see the tide turn on that a little bit. There's been some smaller courts, and I think there was a smaller court in Germany, for example, that said, um, just because the US government might be able to get data does not necessarily mean they will. Because if you look to the agreements that these providers have in place, um, those agreements with their customers mean that they won't turn over data.

For example, um, Kler signs a data processing addendum with all of our customers. And in that data processing addendum, we specifically say that if there's a conflict of law that that exists, um, between what the US government might try to get from, uh, get, if they wanted to get data about one of our customers and our customer's data was related to people who are not US citizens. We specifically say that if there's that kind of conflict of law, we will push back.

And we have a, um, transparency report that has warrant canaries that are kind of these promises of things that we've never done for, you know, in response to government requests. Um, so we maintain those and, and that's the kind of thing that you would wanna look for from a cloud provider, is look at their record on those kinds of issues and what kind of government requests they've gotten, because all of the big companies have these transparency reports now, and you want to make sure that you're looking for that, and that's how you can balance this, this concern. The other thing that's really important is the data privacy framework.

Part of the reason that it got implemented was because the Biden administration packed an executive order that really reframed how the US government surveillance agencies, um, and could, could exercise powers under the Foreign Intelligence Surveillance Act. And those powers were what, um, have been at issue in all the shreds, uh, jurist, the REMS debates. So, um, but, but I think going back to the providers, you wanna look at those that have the privacy guarantees, that have the commitments to push back on government requests and then maintain those commitments.

Agreed. And that sounds really reasonable to me. You know, think, you know, ba you know, as I said a minute ago, I would expect folks like you to be doing about those things about now.

And I think you hit on a lot of the key points. You, this, you know, to be clear, the rules and legislation aren't done yet, you know, in any one country, you know, the US, Canada, or, uh, internationally, but we keep moving down in that direction. So I would agree that you want, you know, as your concerns justify this, having someone, you know, the, uh, the, what was the term policy canaries, I love those, right?

Uh, the war And canaries, war, war And canaries, right? Yeah. You know, to somebody pay attention to this because if you follow down the path, you know, so the Department of Energy and the, and uh, DHS are pushing, uh, uh, cyber informed engineering.

And one of the key terms in there is radical transparency. And I like those, those words. Exactly.

And I like the, the, the forces lining up behind that because we need to have this conversation. Radical transparency does not mean that everybody gets everything just like it did with vulnerabilities and threat intelligence. And now supply chain, it doesn't mean that everybody gets everything.

It means that you get the information you need in a time you can actually use it. So your time to transparency window doesn't collapse before you can do something with it. And today, yeah, I would like someone, uh, a corporation with all the motivations to stay in business and not get sued and so forth, to have taken those steps and have those, uh, those warrant canaries, you know, waiting on policy trips because no individual individuals and even well-resourced enterprises can't keep track of all that themselves yet.

You know, I, I think one of, one of the dual complexities of this, and I'm particularly interested in your perspective about this, Emily, is that there's the regulatory compliance side of this, which is always moving, you know, changing, evolving new things, popping up, things, taking a long time to get put in place. So there's a, a bit of uncertainty about it, but at the same time, we're deploying applications and data and infrastructure across the cloud, you know, at the edge in the core, wherever it might be. And, and that's, that's become more of a fluid thing.

It used to be that infrastructure was, I set it up and then we leave it and we run it for a while and I might change it. And there's a real specific procedure. Now you've got infrastructures code and APIs into the cloud and into, into your software infrastructure.

Um, I I, is there any guidance that we can provide people on as you create more of a dynamic environment, um, how to help you understand where you may need to reexamine your compliance when it comes to privacy and localization? Well, I mean, that's, that's a, that's a very big question, so I don't wanna give a lot of Might that year, year I had to ask you. I'm like, I need to ask you about This question.

Well, I mean, yeah, so like, that's one of the, you know, that's one of the advantages potentially of, um, of going with sort of a bigger company because for example, we've developed this global network, so we had to look at all the laws of all the countries where we have data centers and where we're operating. And so we've done that work. Um, and if you don't have the bandwidth to, to do your own 200 country survey or, or whatever, um, to figure out what the applicable laws are, you know, we're global, so we're respecting the global laws and regulations, and we've developed these tools that help you figure out if you need to localize.

Um, and, and so, you know, the idea is that you shouldn't have to trade off your fears about not being able to comply when you move to the cloud, when you go from an on-prem solution to the cloud. Um, and, and so, you know, our whole goal is, and I imagine the other hyperscalers too, our whole goal though is to have the benefit of this global network with the performance reduced latency, all these built-in security features, all this kind of stuff, while you still can figure out how to comply with the regulations, we have a data localization service, for example, that allows you to restrict where data is inspected, um, where you keep your keys. So if you wanna keep your, your encryption keys out of a particular country, for example, uh, we can do that.

And then, um, we have some limited availability now, but we also have some ability to store logs in particular jurisdictions. And right now, that part is a little bit more limited to the EU and the us, but we're, we're building that capability out. And, um, you know, so, so I think if you are trying to expand and you're trying to move to the cloud, you wanna just, again, look for, uh, you know, a cloud provider that also has, has thought about these things, but it also has done the certification work.

So for example, um, we've certified to ISO 2 7 7 0 1, which maps to the GDPR, um, and we have an EU cloud code of conduct certification and, and some others that show that we have looked at some of these particulars jurisdictions and have kind of checked the box to say, yes, we're, we're compliant in these areas. And, and I think that's the kind of thing you would wanna look for. You can't do all that legal work yourself.

Okay. Very good. Very helpful.

Um, I, I think another reason why you wanna, and this, this actually is a, I guess one of the advantages of going with a cloud FLA flare like thing is this is such a, a liquid situation. It's far from static. And so portability, right?

And that used to be one of the hallmarks, one of the foundational things of being in the cloud is elasticity and portability, right? I, today, it's here tomorrow, I wanna move it there. It's fine.

And you know, I I I think that may or may not be something CloudFlare can assist with. I'd love for you to weigh in, but we need, I mean, for organizations out there dealing with this issue, gotta recognize that whatever solution works for you today may not in fact work for you tomorrow or next week. And we need plan Bs and Cs on, on, in terms of portability to, to adapt to whatever the, you know, the latest and greatest are, uh, in terms in terms of regulations and compliance.

So, uh, again, I'll throw it to him and Chris, Emily and Chris, I mean, Emily specifically CloudFlare, how, how do you guys help with that? And then Chris, what are you hearing on that issue, you know, through your contacts? Yeah, sorry.

So in terms of, I mean, if portability, if you wanna leave CloudFlare, which we hope you, you don't do, right? Um, we don't lock people in and there's not a lot that we store. So right now there's, we have a couple storage products.

Um, so it, you're able to move your data. We don't own it. Chris, how do you facilitate people Who have to move their data or around, Well, I'm just going to pile in here and praise of vendors, right?

And Cloudflare's here, and you know, Emily has really good answers. So there's nothing I, I, I would, uh, see that would make me think that that's not a good choice. But it reminds me of, uh, uh, 90 19 92, the first firewall, you know, sit standing there in a booth all by myself.

And these two young folks came up with a great question, and it's like, Ooh, I know the answer to this one. I'm really proud of myself. Then the second one, and they came up the third time, and I said something that, that has lasted to this day.

And it's true. Look, while you're really buying is a relationship with a bunch of people who should anything go wrong, we'll live on anger and caffeine until it's fixed, right? See, and what, you know, the value of working with, with good vendors who are reputable and manage to keep their customers is that by definition, they're worrying about this stuff all the time.

And I love, you know, geeking into the individual answer then how we do things and privacy and localization. But unless you wanna do that stuff for a living, at a certain point, you're gonna have to have appropriate trust in someone else, right? And there are, outside of the very large enterprises, very large enterprises, there's not a lot of this you're gonna be implementing yourself and by definition, right?

Unless you are, you know, that cloud provider, you know? So you need to be appropriately aware of your policy environment, what legal risk do I have? If you don't have good lawyers, get them, um, get good technical people who can question the, the answers of, of vendors and providers and so forth, and, and ask the kind of questions that come up on shows like this.

But, you know, this is, and this is where, where my radical transparency obsession comes in. I, I think today you can do business with good vendors and, and suppliers and vice versa. But the contracts and the actual vehicles we use to protect IP, for example, are almost just a wave in a, at, you know, we're just, we're hoping our employees understand them.

I think we get better and better at, at automating these sort of things so we can get the kind of visibility, so in this case, the vice president of it at a, at an enterprise can say to the, the, the C-suite and the, and the board, yes, in fact, we've done our diligence, we've got the right providers, and I can see what's going on. You know, when it comes to portability too, Alan, is, there's portability within the cloud, within the service provider or providers that you use, right? And that's, it can be portability of the workload as well as portability of data.

Though data is much more difficult, consequential to move. We get into localization issues and things like that. But even within, you know, one network, one network provider, moving those workloads around to the edge, you know, edge workers that are doing different parts based on traffic that might be coming in, or security issues that are happening in the network, I think more and more we're moving to you.

It isn't set it on flexible and it'll allow to automatically move all over by itself, but you're getting to a point where you can make parts of your application more portable, say to the edge or different locations at the edge, uh, versus things that need to run in a certain location or at the core. And that's part of the architecture design of our applications. And that's where this portability cloud starts to overlap into software architecture, infrastructure design, things like that.

They, there aren't hard lines separating the two anymore because so much Of the cloud is programmable. That's exactly what I was getting at. But as part of that portability, do I run afoul of these sovereignty laws, right?

Because you're going, I, I happen to know you're going to Barcelona next week for a conference. What, what does that mean for my data? I mean, you know, yeah, I, I keep it stored here in the US right now as per our, you know, policy.

But what are you going to take with you over there? Or what are you, are you going to use, uh, let's say I'm on CloudFlare or some other network. Am I gonna temporarily have data on the edge over there?

And and what does that mean? Yeah, I mean, that, that's, that's kind of the, the benefit really of the cloud in some ways, right? So your data is, if it, it's being processed at the edge, if you're using CloudFlare, your data is being processed at the edge of the network and, um, you know, the sovereignty is, it's kind of funny.

It can mean kind of a couple different things. And, and one of the things is you want that data to be processed as close as possible to where the person is, whose data is being processed. Um, but the benefit of the global network is that if there's DDoS attack or some other kind of congestion on the network, you can route that data to where it needs to go.

So what we've done with our data localization suite, for example, is, um, if you are a European citizen and you are traveling within Europe, um, your data will still only be inspected in Europe if that's turned on. Now, if you go outside and you're still trying to access a customer and that customer's website is based in Germany, but you're a German person who now has gone to the United States for vacation, and you're trying to access your German bank account, and the German bank is behind CloudFlare and they have data localization suite turned on, you might experience a little latency because that bank has chosen that they still only want their data inspected in the EU as opposed to in the United States. Um, but as far as I can tell, the European data protection regulators aren't too worried about the scenario of their, their people going on vacation and, and accessing from outside, but it's really more when they're inside the EU and accessing the eu.

But that's, that's Europe in its own its own glory. Um, so different jurisdictions may have slightly different rules, but generally that traveling, what you really want is you wanna make sure that your customers, wherever they are in the world, can get their stuff or can access their stuff wherever it's stored as quickly as possible. And that's the beauty of, of processing, um, at the edge closest to where the individual is located.

My answer was gonna be, I was gonna ask to borrow your phone and your laptop when I go to Barcelona, Allen. So Of course it's your problem. Um, I think I left on there, but I mean, look, this is, this is real world and here's my other kind of fear in many ways.

Our world today, on the geopolitical front between wars and competitions and, and nationalist movements and sovereignty laws and all this stuff is getting more and more fragmented, more and more complex, more and more fraught with, with craziness, for lack of a better word. There's gotta be a better way. How do, I mean, Chris, you work with the federal space at the federal level, and the US I know is big on alliances and you know, at least with their friends on, on trying to get these things done.

Abilene, you know, you represent CloudFlare here, chief Privacy officer, is it pie in the sky? And, and you know, naivety to think that we can come to some sort of global kind of rules around this that'll make life easier. Chris, you're raising your hat, right?

Yeah. So taking everything we talked about right now, and this is the state of the world we're in, and we got here, you know, in, in many ways you, you know, recent history last three years, 5, 7 90, you talk about decades and for everything we talked about, I, I've got one word for you, starlink. Alright?

You know now, so you're not inside that ju you're orbiting the planet rats. What do I do there? Well, and, and as an interesting step beyond that, you know, Gwen Shotwell, who's COO at, uh, SpaceX, um, is or was anyways, uh, also CEO of a company called Orbit's Edge.

So imagine Starling satellites in tens of thousands that aren't just routers, they're servers. So we actually get that, you know, everything we're dealing with, with data centers and clouds and soen and jurisdictions. Now imagine those data centers are orbiting the earth in 17,000 miles an hour, and it's distributed across end space.

And as with all things, either we get into this future and the, you know, society collapses and we all go back to, you know, eating, you know, deer or we work it out. And I'm pretty sure, you know, the kinds of things, policy visibility and processing, the ability to say, my data is here, and I know this to a usable level of the word no and is subject to these policies that I don't have to hire a bunch of, you know, uh, interns to look up, but are are known all the time as that's accessed. I think that sort of thing is inevitable, and it is in the, in the, the working lifetimes of everyone here.

You're an optimist, Emily, Are you as optimistic? Um, well, I don't think we're gonna get to a place where we're all, you know, back to, you know, hunting and gathering and, and shooting deer for dinner. We hope not.

I think, I think we're, we're maybe away from that, hopefully. Um, I mean, it is, it is tough though because we do see a lot of governments who are really pushing for keeping their data local because they view it as a national security concern, right? They, they want it, I want it in a, in an on-prem data center.

And yet we saw when Russia invaded Ukraine, that that actually is a really risky proposition too, because Ukraine had to suddenly put all its stuff on the cloud because they were worried that the Russians would take over the data center where the data was. So there's real benefits to having things in the cloud. There's also real security and cyber intelligence benefits when, for CloudFlare, for example, we're looking at traffic, you know, we, we sit in front of about 20% of the internet and we're looking at global traffic, we're looking at cyber threats, DDoS attacks, you know, bots, all the things, all the bad things that can happen.

And we are figuring out how to adapt and evolve to that and protect our customers. And you can't do that if you're only looking at the risks that come from France or the risks that come from Germany or that come from Chile or, or whatever. So you have to have that global visibility, you have to have that interconnectedness.

And I think we have seen a little movement, I let EUCS certification I was talking about, there was a storm contingent, um, led by France that was saying, no, no, no, this data has to be processed locally. And we've actually seen the Europeans move away from that slightly. I don't think that means that countries are going to just drop these sovereignty requirements altogether, but I think there is a real recognition that as providers like us and others are developing, um, you know, ways to program how the network works for you as our customer, as we continue to evolve, that you're going to be able to fine tune, fine grained control what's happening to your data on our network while taking advantage of the global network and, and all the advantages, um, for cybersecurity that, that brings.

So, and I, and I think the cybersecurity officials are really recognizing that some of the data protection officials are maybe a little slower to agree. And then you've gotta deal with the, the geopolitics of, you know, countries trying to compete just economically and trying to boost their own economies. And one way to boost your own economy is to insist on a sovereign cloud that is, is gonna have data centers.

So you've got a lot of competing tensions there, but I mean, we hope we can rise above that as, as the cloud that offers a lot of different, um, ways for our customers to kind of program what they need to meet their specific, specific legal obligations. Yeah, I'm really glad Ggl, glad to hear you say that because, you know, in security we talk about compliance, but we also talk about guardrails, which is sort of the automation of adjusting those rules based on the context of the situation. Um, and that seems to be what we, what we need because that situ, 'cause we're in a fluid environment, it's gonna mean this, today it's gonna be have stronger or less, less strength than in its enforcement tomorrow.

And so you need a way of being able to work in an environment where when those things change, I don't have to lift everything up and go decide to how to re-architect it somewhere else. I've got a way of, okay, that won't move there anymore because that's not meeting those conditions any longer. So those kind of guardrails sound extremely useful to me.

Well, I mean, you think about it, you know, my, on the international Space Station right now, we have Americans and Russians and other nationalities creating and using data that I think is, is a, you know, a litmus or a little canary or something about the future. We're moving into, you know, as we establish spaces on the moon and more, more things in orbit, and you know, if you think starlink and Orbit's Edge is hard to process in this context, figure it out. And we will have control of our data.

There will be nation states and corporations with enormous stakes in it, and that that will know where their data is and who's touching it. So sovereignty, to your point, Emily, you know, may not always just mean literally on this two dimensional space inside my boundaries, but inside my sovereign space, and I know it at the nation state level, we will get there. We see optimist.

Chris, I think let's try to end it on a high note though, Emily, first of all, thank you for coming on this edition of the last great Cloud transformation. My pleasure. Thanks for having Me.

Yeah, no, and you know what, thank you for CloudFlare two, CloudFlare convey our thanks because quite frankly, there are only a handful of companies that have the scale to deal with these global, uh, regulations that we're all having to deal with now and, and, and increasingly slow. So, so it's good to see that someone's actually thinking about this and, and doing something. Chris is always my friend, thank you for coming on.

Keep up all that you do that a lot of it goes unsaid or unrecognized, but we'll recognize it here. Thank you. And Mitchell, I'll give you the last word.

You know, I was just thinking about, um, I remember reading the book about globalization and the concepts of, you know, everybody working in the global economies and this is what it looks like, what globalization really looks like. There's a lot of lot to it as you get into the devil's in the details and, uh, you know, partnering with the right people can make all the difference for sure, as someone who's had to, to operate those things. It, it makes a meaningful difference.

Great. All right. We hope you've enjoyed this edition of our last great Cloud transformation show here in partnership with, as I said, with our friends at CloudFlare.

We'll be on it another, I guess, uh, two weeks after this with another one. And we actually have another live event, which I really, if you like, talking about these kinds of things, these live events give you a chance to weigh in and ask questions. So please join us for that.

But until then, this is a shimel, have a great day everyone. Thanks for joining in. Hello, uh, my name's Mark Callahan and I am the CEO and founder of Cloud Canaries.

0 or two point x. It kind of depends, but also what happens beyond that. And I think that's pretty important to bring up right now as we go through this, uh, evolution.

Uh, also we're gonna touch upon what is driving obs observability as well. So stay tuned. We're gonna go through, uh, some slides that I hope you find, um, very interesting and helpful and a, and a little bit entertaining.

So here we go. 0, predictive visibility, and that includes a, a list of, of, of items. It's continuous monitoring, SLA, uh, compliance actual and forecast data.

So you can, you can work with, you can alarm notify on actual data or forecast data from API to workflows. So observability, uh, it has to be more than just monitoring APIs. It really has to be monitoring the, the user experience, and that really includes workflows.

Um, the second pillar is, uh, autonomous mitigation. Now, what that really means is, you know, prevent fix, but do no harm without interventions of the op ops team. It also kind of means that you wanna be able to do this really fast if something bad is happening.

And so that's within 15 seconds, but it could be weeks or even months that, um, the system will basically self-heal. So you have to keep that in mind. Um, when you have this type of mitigation, you want to be predictable in the outcome.

Well, it, it, it has to, the way it has mitigated an issue, uh, or resolved it, uh, has to be kind of within the, I think, a set of, uh, domains that are, uh, are reasonable. Um, also kind of aligning actions within intent. And this kind of goes back with predictable outcomes.

Um, you can fix a problem with a hammer or, um, a sledgehammer and, and you wanna be able to, uh, do it. That kind of aligns with the intent. 0, we believe at cloud canaries is convergence of metrics.

Connecting the dots. The dots are from the cloud to your business. And once you've done that, you've opened the door for a set of very interesting opportunities.

Predict anything, forecast anything, add business insights. So connecting the dots between the business metrics, cloud metrics, convergence of those two, this is, again, a really important thing. All these three pillars are, are, um, very important and will change the way DevOps functions and operates, and the requirements that are gonna be built upon.

Um, um, that for, for the DevOps teams, and I think all really, really good. 0, we have our three pillars. 3, but the idea is this will be set of increments that, um, that will, um, happen in observability.

Um, I just didn't wanna confuse everyone. 0, as we mentioned, three pillars. Uh, predictive visibility, autonomous mitigation, uh, convergence of the metrics, cloud metrics, business metrics.

This will have an incredible impact on the business, very positive. And the cloud too and beyond. At Cloud Canaries, we believe that AI models, and these are models built by machine, um, uh, learning tools that are being supplied by many different vendors that create, um, models.

Basically, for the most part, neural networks will become the solution. In other words, you're gonna have one platform and many different models, is the models are gonna contain all the intelligence that you need to, to, um, solve that a particular problem. And because, and because it's not a, you don't have as many platforms and, and models provide the solution, you're gonna have many solutions because they're gonna be much easier to, um, to deploy, to manage, and you're gonna be able to roll it out to your entire, uh, all of your infrastructure, which again, is going to be amazing.

It's gonna be incredibly helpful for the cloud and the business. So once again, beyond, it's gonna be, you're gonna be buying or renting or subscribing to models, not platforms. Think about that.

It is gonna happen. It's happening right now in some areas. 0 and beyond?

So let's take a look at that. Um, developer leaning and people say, moving to the left, I, I don't like to use that term, but it's gonna be, um, developer leaning. DevOps is gonna be, um, leaning to developers to do a couple things.

Building observability in code. There's some things that you just have to do. You have to do it in the code, and you can't just add on a little library to do it.

And, and the, the, the, the clearest example is workloads, um, and workflows. So, um, how does a, how does a workflow, um, work? It, it's a, you know, it's a, a process of going through, uh, several different stages within, uh, kind of a user experience.

And you create this, this workflow. And, um, you might, it might be a series of workloads, don't really care, but, uh, observability is gonna have to be built in to really capture that, um, that information. Also leveraging CI and cd, continuous integration and continuous delivery with ai.

So again, I think that's gonna work with observability to actually make a better, more, um, um, better applications, but also better models. And we'll talk about that a little bit later on. Second pillar, cloud gatekeepers.

It's not gonna be just an ops, it's going to expand, and DevOps are going to be the gatekeepers. If there's something that's going to happen in the cloud, you're gonna be talking to DevOps, the DevOps organization. I call 'em the guardians of the cloud.

And that's really what it is. What, you know, DevOps will be focused on predictive outcomes. Your reaction time is going to, you're not gonna have that much re uh, the re reaction time that you're gonna be investing is gonna be fairly small because it's working.

Everything's working. So predictive outcomes and DevOps is gonna own p and l, new profits and losses. It's gonna own the performance.

And SLA targets, again, it's more of a gatekeeper, guardians of the cloud kind of role versus an operations or developer security. It's all kind of all the above within an organization. The third PO pillar is, is cloud analyst.

And, and maybe there's a better term for it, but it's really kind of making sure that, um, the cloud is aligning, uh, to the business intent and that the reefers, uh, is, uh, is understood. So aligning how the cloud is being operated, what you're doing to the end in mind, which is making customers happy. Uh, measuring success, predicting success.

It's important also on the downside is, you know, measuring, uh, when things go wrong are when there's a failure and learning from that. Um, and those failures might have ha might have nothing to do with, uh, the cloud. It's really driven by the business.

0 and what's gonna come beyond that. And I think what's coming beyond that is gonna be very, very exciting too. So let's take a quick look at that.

0, developer leaning, it's in the code ban, a cloud gatekeeper, um, guardians of the cloud. Uh, the scope of, uh, DevOps grows roles, grow cloud analysts, making sure that the business and and activities in the cloud are aligned and using the, the massive data through, um, understanding the flows of, of, uh, solutions or workloads of solutions, uh, uh, or creating models that can help the business, um, you know, take care of customers and have a fair, you know, profit. So beyond is, um, kind of extension of that more diverse specialized roles for DevOps.

And we're seeing this to some extent today. We see this in, uh, security. We see this in infrastructure, uh, solutions.

This is gonna grow. Uh, the key will be to always make sure that the DevOps continues as a community. So that's really what it has been.

Um, and, and that's gonna be the key to long-term success. 2. You can, you can decide when we, when we accomplish that.

Let's take a look at, uh, the next slide. So I've tried to map this into like, what's the average time that a DevOps individual will be spending working, and what will they be doing? Um, and, and I hope you, the, the big takeaway, I hope you see here, and, and it's, you know, pretty obvious is rea the time that you have to spend on reaction, there's a disaster.

There's two disasters, there's three disasters will be reduced. It has to get reduced. That's really the only way to allocate the time to do the other tasks that will have immense value for the business and the customer.

0, uh, the time that you have to spend on reacting to some emergency is gonna go down. And the reason for that are observability is being building the code. Um, we're able to mitigate, um, the problems before they even happen through forecasting.

We're able to gen through our forecast due prevention, understand what the problems are, and, and deal with them in a very reasonable time. And that will impact the amount of time that DevOps development, operations security will actually have to be reacting to something versus planning to, you know, solve problems that are, that are six months or two years, uh, away. Um, beyond that, i, I, I think it will, um, it will continue.

There might be a little bit less development aspect because observability has been built into the code, and that's now a standard process. There should be more economic to actually do. The big takeaway in the beyond is the addition of business insight.

As DevOps teams continue to build their mod forecast models, um, you're gonna be able to generate business insight that, you know, the business side will, will be very excited, will be, it'll be, it'll be their secret sauce for the business, the how to take care of the take, how to take care of their customers. So you're gonna see a, a massive amount of that because DevOps are gonna be managing the, the models through AIOps or however you wanna define it to me, is still part of DevOps. Um, so that's the big takeaway with beyond the ability to really communicate value and insight to the business.

0, but on a smaller scale. So then the question is, well, what's driving OB observability? 0, right?

You know, it boils down to limited ROI talk to anyone, well, at least, and at least I have in the DevOps world, and you, you get the end, the end in mind that businesses aren't getting their, the RROI that they expect it someone has to do with solutions being overpriced, consumption based. And so you can't, you know, do a full rollout as I mentioned here. Um, these, the, the company can't afford it.

Cloud costs, they really dropped when you moved from the, the, your data center to, to the cloud, but now they're going back up cost to support overlapping vendors. You don't necessarily need that. If, if the, uh, if the model that you're generating is the solution, you don't need as many platforms, the number of vendors and, and cons consult consultants will go down at, you know, um, because you're just not gonna need them.

Uh, regulatory rules that's continued, that will continue, uh, to, to increase. Um, especially if you're, uh, a global company, whether you're in EU or you know, the US or, you know, wherever you, you, you're, you're positioned, um, rules are gonna increase. We mentioned, um, organizations can afford to do a full rollout.

And again, I, you know, it's, it's just amazing when, when I go and speak with, uh, DevOps, uh, you know, VPs of DevOps, uh, operations, and, and they say, well, we only actually rolled our observability solution out to like 20% of our infrastructure because we really can't afford it. Um, and the turbo insights not delivered fast enough. Ultimately, that is the, the big driver for, uh, a limited ROI.

0 and beyond. So let's go to the next, next slide here. So what are the drivers?

Um, um, so there's a, there's software drivers. Biggest one is Open Telemetry tool is tool slash vendor agnostic. High adoption.

It's amazing how many of software vendors have adopted open, uh, telemetry. It's all standard standardized. It might not be necessarily be perfect, but vendors are all supporting it.

And again, if there is an issue, it can be resolved pretty, pretty quickly because it's standardized. Second pillar code first. This is having a dramatic impact on the quality of software that's being generated.

Um, you know, both as a feature, as features, but also as within a steady state, uh, where software is released and, and running. Um, you'd be able to provide better monitoring, observability, um, security, uh, and, and, you know, all the time, uh, with, uh, continuous, um, observability. So building code build in code, not a add in.

And it's standardized code first. The last one, the last pillar is, uh, can you continuous integration and continuous, um, deployment in code. That's pretty, pretty typical right now.

CICD, um, but also in the forecast models that you're creating. Um, there's no reason why we shouldn't be doing that. We should always be making our models, um, smarter.

Um, and we can do that now. We'll talk about that in a few, few, few moments. Better code and models, better user experience.

Ultimately, that is our goal. Observability technology drivers model ready data at Cloud Canaries, since we collect the data, we know how to easily, uh, use that data in our models that we, we create no matter who, whose tools the customer is actually using. Pretty cool, um, machine learning AI tools, again, there's a lot of different flavors or shapes, I guess shapes.

Um, all of them have certain pros and cons. It's should be up to the, the user to actually determine what they want to use. And the last piece here is compute, because our theory of cloud canaries is that you don't necessarily need, uh, you know, a thousand data scientists.

What you need is a lot of compute and generalized, um, uh, uh, tools that, that generate, generate models. You'll get your best performance bang for the buck that way that's happening right now. With that, we can, we can take, um, we can allow continuous model, uh, validation.

And this is the key to, to, uh, one part of the puzzle because we're constantly collecting data, workload data telemetry. We're using that data to build a model. We're using the models to generate forecasts.

Eventually in time, we generate actual data that can be compared to forecast data, and we can say, Hey, it's right on, uh, uh, uh, or No, it's not. And then tune that model, uh, and, and take the error out or as much as you can until you get, you know, more actual data or you get more forecasts. So it's a continuous loop, and this is gonna have a massive impact on the whole idea of models.

Become the solution, not the platform. Think about that one platform. Many solutions 'cause of many models.

What happens? Solutions get smarter, better, faster. It's pretty simple.

You know, collect, validate, build your model with, uh, models, with, uh, tools that you want to use, forecast, predict, add insight, notify an act. And act is obviously the, a critical piece here for our customers. Beyond solutions, as I, as I said, you're gonna have more models, fewer platforms.

You know, our claim at Cloud Canaries is that the, the, the number of solutions are going to become very, they're very numerous because you can be able to build models and be able to use the same infrastructure, same, uh, platforms to use different models for different problems. And from digital experience, contract negotiation, you name it, we can have a model for that. Costs are gonna come down, support for those.

That platform is gonna cut, is, is going to be low. And you're gonna build a set of consultants who are focused on a couple platforms and, um, validating models. So it's, it's gonna be, it's, I think it's, it's, uh, it's a paradigm shift.

And, uh, it's going to, uh, make, uh, better products, happier customers and, and, and more time for us to, uh, think of new, new ways to innovate. That concludes our little presentation. I wanna thank you very, very much for listening.

Uh, if you wanna learn more, you know, just go to Cloud Canaries. Thank you very much. Have a great day.

Hello everybody. I am Mike Vard. Today we're talking about, well, the TikTok ban seems to be likely to happen.

We'll find out later, I'm sure, but right now it's a whole big huge debate. Then we're gonna have a little trip to Carnegie Mellon about robots and ai. And finally, a little CES retrospective.

You're watching Textron Gang. All right, everybody, welcome back. We have a small gang today.

We got Lisa Martin is with us from the Futurum Group, where she is the CMO advisor, and we're gonna be talking about a lot of marketing issues today. So great to have you here, Lisa. How are you?

Great, great, Mike. I'm excited to dig into today's topics. All right, and John Schwartz, who is some sort of regal person in Silicon Valley, and You've been listening to, uh, Alan's, uh, bantering.

Yeah, that's, yeah, he's, he's, he's elevated me to a state that I have never been associated with, nor probably ever will be. I, yeah, I, I'll take it. I have To say I missed a ceremony and I was hurt that I didn't get invited, so, you know.

No, I'm sorry. Next time didn't invite either. I invited you to be V IP front row.

There you go. All right, well, let's jump jump into our first topic here, which is this whole TikTok ban. And this has been a long time incoming, and apparently, uh, some folks are saying it's a good thing.

Some folks are saying it's a bad thing. Other things folks are saying, we need to postpone it a little while, so it can just become an American company. But, um, Lisa, I know you've been following this.

What's going on here? What's the pace of this and what, what is the concern? So the concern there, it's, it's a very polarizing issue, Mike, as you kind of alluded to, there are, it, it, on one hand, president Biden signed in, uh, about a year ago, um, a ban effective, uh, aban or sale mandate effective January 19th, which is in a couple days from now, because of concerns for national security.

Um, you know, the challenge that the concern that some legislators have, and the Supreme Court is hearing this now, is that tiktoks proprietary algorithm is really way more advanced than a lot of other apps and how it is targeting you with data collected by you and surfing up ads and things like that. And the US government is saying that is way more dangerous and effective. Um, come this Friday, the Supreme Court will hear from TikTok and its Chinese parent company by dance who's seeking to, to, to block the law that Biden signed in, as I mentioned, a, a minute ago.

And, um, but TikTok is also saying, so it's a ban or sale issue. TikTok is saying we cannot be sold. It's not technologically possible.

It's not, um, uh, commercially possible either. And even if it was to be sold, say the Elon Musk, there are rumors about that it would only be the US version of the platform, which would not include the algorithm, which is the secret sauce. Then you have other legislators like Massachusetts Senator Ed Markey, who is planning to produce legislation to delay the ban or sale by an additional 270 days, which would put it in the administration of President Trump, who in 2020 was against it and got a lot of support on TikTok for the election, and is now on the side of keeping it.

So it's such an interesting, uh, polarizing topic. Then you have the content creator perspective. Had a really interesting conversation earlier this morning with a very popular TikTok who's got about 110,000 followers and said, you know, there's a, there's an economy here that isn't reported in the labor markets.

And so if the, the app were to be shut down, uh, if it were, if the ban goes into effect on, uh, the 19th, then Apple and Google would have to remove it from the US app stores, and, uh, US internet browsers would not be able to surf out the TikTok website. So content creators would lose income. There are creators like Charlie d Emilio, Addison Wright, who are getting hundreds of thousands of dollars per video.

So this content creator that I was talking to said it's such an engaging algorithm for community development that it would be analogous to mass layoffs, even though we won't see those figures. So you've got folks that are staunchly on the side of sales, staunchly on the side of ban, and we have a matter of just hours before this comes to, uh, home plate. Alright, John, what is your take on this?

And I just find the whole by dance argument here somewhat, uh, duplicitous to be frank. I mean, six months ago they were saying, uh, whoa, it's all in the us so it's all different. So it's not connected to China, so you don't have to worry about it.

And now they're saying, oh, you can't sell the company because our algorithms and actually all these things are intertwined. So it seems to me that that's kind of silly, but what is the take on, at one point, uh, Larry Ellison was supposed to buy it, and now we got Elon Musk car to buy it, and who's gonna step up to buy it, and what would you actually get if you bought it? Okay.

So I understand the motivation for somebody like Musk to buy the company or to have the US version of it as, as Lisa pointed out, which would be about 170 million users, including people like my son who's 24, and I believe your son Mike uses it as well. They really don't care who owns it. But I think Musk, the appeal for Musk would be just think about the instant, uh, credibility or benefit to the advertising for X, which has been flagging, it's been miserable the last couple of years.

And I also think about some of the data that would be generated that could be used for X ai. So there's that benefit. I also think, uh, meta is gonna be watching this really closely.

So meta really has no traction as far as I'm concerned, among younger users. They, they, they prefer TikTok, and I'm wondering if they, I mean, they're probably, they're, they're acquiescing to Trump in many ways. We could.

We've already probably talked about that. So I think they're watching, hopefully hoping for a ban. I think what's gonna happen, there's also a rumor, by the way, uh, Politico has written that, uh, Steve Mnuchin, who served in the Trump administration, I believe his treasury secretary was interested in perhaps buying TikTok.

I suspect, and I, I'm not a gambler, uh, that they're gonna punt this down the road and leave it to Trump. I mean, I've seen Trump, um, change his position on this among other things. I think that's, that's probably what may happen rather than this kind of in imminent showdown.

But, uh, it has a ripple effect, you know, has a ripple effect here in the Valley and throughout social media. Um, it's that the absence of it, especially for young users is just, it, it just decentivizes any choice of using social media for, for the most part. So, um, again, I think they might go the path of what Markee is suggesting.

Mm-hmm. As it turns out, my 14-year-old is also a big user of Discord. So we go figure you, they're, they're, the kids are changing their platforms.

Which comes to my next question, Lisa. So, um, allegedly there's something called Red Book out here that people are being encouraged to move over to, and I'm not quite clear what the relationship between the people who make Red Book is and by dance, but what is that for real? Yeah, It is for real.

It's called Red Note, and it actually was, became the most downloaded app in the Apple App store a couple of days ago. So these TikTok refugees who don't seem, who probably are concerned with making money, aren't concerned with geopolitical considerations, but it's not red, from what I understand, is not a clone of TikTok. It's really a mix of lifestyle content, videos, community building tools, but it's really resonating with tiktoks, what is this displaced audience.

And it's gotten the attention of investors. It's reportedly in 2024, it was valued at 17 billion. And despite being owned by owned, uh, a company in China, it's rise suggests that again, those content creators are more concerned with making money on the platform, which I, I mentioned earlier that there's aous amount of money in there.

Now what we don't know is from a safety perspective, I hear the algorithm is quote impeccable. Um, meaning that what it's really doing is fostering that comm sense of community, um, that TikTok users have loved and have benefited from. Um, but it's gonna be, but another thing that's really curious here that we don't know yet, Mike and John, is that the Ts and Cs are in Mandarin.

So every US user in John said there's 170 million American users of TikTok don't really know what they're signing up for. So it's a bit of a black hole, but because the TikTok users don't seem to be concerned with geopolitical considerations, they want to go where the community is and where the money opportunities are. John, we've talked about this in the past, but I always found this whole quote unquote algorithm approach to things to being somewhat problematic in the sense that people don't know what they're signing up for.

That there is this algorithm that is manipulating content and behavior in a way that drives a specific outcome. And I guess they're entertained, but at the end of the day, um, is bike dance any better or worse than what Facebook's trying to do? Or no acts for that matter?

It seems like, you know, they're all kind of, this is, you know, they're all the same ones of cattle. Yeah, they're all swimming in the same lane. I mean, that was one of the things that Sheryl Sandberg, who used to be the chief operating officer of, of then Facebook and then Meta for a while, she'd always talk about the people are the, the, the people are the product.

I mean the, the people who sign up for these, these services, we, regardless of where they are, it's free and they are feeding into the system. They are being used as part of the product that is the, um, tangent in a sense, tangible agreement or partnership they've signed up for, even though they don't read the fine print, they never read the fine print. Most of its don't, I don't.

And so, yes, I, I think you're right. It's hard to distinguish one service from another, from another. And I think in the end, if there is a ban or if there is a, a push to, to go to an alternative, people will find a way to go somewhere else.

I mean, we see this even with the extreme version of older users who've left x I've semi left it, I, I rarely use it. I've went to Blue Sky, which is, which is fine. I don't use threads at all.

There's a lot of pushback on, in terms of meta, a lot of folks leaving that now for various reasons. So they will migrate, they will migrate somewhere else. But I think in the end, I mean, maybe we're reaching the age or the, the stage where people are just kind of have fatigue over all these services for multiple reasons.

And when they find that they're not using it, they may not miss it as much as they thought they would. So, um, with TikTok, I'm, I'm, I think we're, I think we're gonna still see some version of it. I just dunno what tangible sense.

Well, I mean, I'm on Facebook still otherwise sometimes referred to as Boomer book, and a lot of that has to do with the fact that everybody I know is still on there and that's how we kinda interact with each other and shifting and lifting that whole thing would be a major challenge. But at the other side of it, I feel like the younger generation maybe is more willing to, uh, use multiple platforms and kind of is more, uh, multi social media channel lingua, I don't know what the word is, but Lisa, is there a difference in the genders, in the demographics and the age of who uses what? Yes, there definitely are.

Um, I never felt cool enough for TikTok, so maybe that's because I'm Gen X, I'm not, I don't know. I never got sucked into, um, this constant scrolling. We know that a lot of the younger users have moved, as you both said, off of Facebook onto TikTok, discord, other platforms where they're engaging with more like-minded folks.

The interesting thing about Red Note is that it's predominantly female. I think the number I saw of its 300 million users, and this is before the TikTok refugees whenever was about 79% female. So I think we're gonna see that dynamic potentially shift, um, depending on what happens on January 19th.

But we do see gender and aged based decisions and interactions on different social platforms. Um, I'm a big Instagram fan. Maybe that's 'cause I'm Gen X, I'm not sure, but it'll be interesting to see what happens if, if everything is delayed, um, will Red Note and TikTok become equal at some point?

It's gonna be a really interesting showdown, and I'm one that I'm particularly keen to watch. John, what's your read on Washington here? Because, um, from what I see so far, Democrats and Republicans are all over the map on this particular issue, and there isn't a lot of agreements.

So, uh, you know, will people not vote for candidates who decided to ban TikTok or, uh, and will that become a political calculation? That's a good question. I, I, it's, it's, it's hard to read through the tea leaves.

That's why I suspect we're gonna, this is gonna be punted down the road because in, in true Washington fashion or their normative behavior is to not make a decision, right, or, or to not address a problem. They will address it in a public hearing, but they won't do anything tangible in terms of a legislation. And I, that's why I think they might push this down the road to give themselves by themselves some time.

And I think in a sense, the influence, and this is gonna a weird thing, is that the influence of TikTok has benefited candidates like Trump, especially among younger mobilized voters, which I never thought would happen, especially male voters. Um, there's this whole nother world of social media, of, of sites and services that people like us in our age, at least, Mike and i's age, I'm not gonna say it's about you, Lisa, um, have kind of, kind of, kind of adhered to, right? They, they, they follow this, this stuff that, and, and I think there's a strong, strong, um, membership as part of this, these communities.

So I think Washington, maybe they don't wanna disrupt it too much, although they do wanna disrupt and have disrupted the way Meta goes about doing its business. Um, and also this is a big boost for x and I I suspect if there is some sort of partnership between X and TikTok, it is is a, a political calculation. I mean, as we talked about before, Musk spent $250 million to be to whisper in the ear of Trump, and he has a lot of influence.

And I would not be surprised if we see some sort of steroid version of X in some sort of iteration of a US TikTok company. All right. We'll see, Lisa, there's been a lot of noise about a lot of things involving China, and this is only one of them.

Um, is this be gonna become a bigger marketing issue as when we sit back and we start thinking about, well, you know, where did this thing come from? Whether it's network processors from, uh, some manufacturer in China or, you know, chips that we're not gonna give them access to early enough. And aren't we on the cusp of some larger issue here in this, this whole TikTok thing, just a piece of a larger puzzle?

It that's a great point, Mike. It could be the piece of a larger puzzle. I think, as I was mentioning earlier, if we look at the demographics of TikTok and all those refugees that are now flocking to Red Note, they don't seem to be concerned of about the geopolitical issues.

So I don't know if it would become a marketing issue, if, if social media companies would have to be much clearer with their users and their followers about where data is coming from, where it's being processed, who's owning it. I think we will see, um, I think we'll be informed by what happens over the weekend if there's a ban that goes into effect or if there is a a is a delay. Um, but I think what we're seeing is the younger users just don't seem to be concerned that this is a ch that TikTok is owned by a Chinese company.

That Red Note is owned by a Chinese company. They want to have the opportunities to engage with their community and to make money. And that seems to be the priority of the followers.

The majority of the followers. And we've got 170 million American users of TikTok. Yeah, Well, John, I was having a chat with my kids about this, and they weren't making much of a distinction between whether it was by dance and the Chinese that had their data, or Elon Musk or Zuckerberg.

They, they Don't care. They don't Care. They don't think they care.

They all, they all they wanna do is discover, scroll and watch videos. I mean, that's how they consume news. They don't, they don't read newspapers or, or go to conventional news websites.

You know, you mentioned that, um, this, this whole idea of China and, and we, the, the, the chips export the security lists involving Chinese companies that might have ties to the military. Um, we're gonna see more of this and it's can only accelerate with the tariffs up to 60% against, up against China. Um, I think in, in, in a sense this is, this is a moving target that every tech company's gonna have to contend with, especially companies like Nvidia and Apple that have these huge presences in, in mainland Chi China.

Um, this, this, this is kind of an interesting sidelight. This is getting a lot of attention because of the popularity of TikTok, but I think it's part of an undercurrents of cost and conflict and, and it's only gonna accelerate with AI development in this fear that the US is gonna fall behind China. And, and again, everything's about ai, everything is refracted through the lens, the prism of ai, whether it's politics, economics, culture, thought leadership.

So TikTok is just a little bit, a little part of it and, but I, I think we're gonna see some form of it so our younger users will be happy. Alright, Well, Lisa, let me ask you this. Do the marketers care about this any more than the kids care about it?

Or are they just gonna go follow wherever the audience is and away we go? Or are they gonna be, you know, more subject to a conversation with, I don't know, the FTC or somebody? That's a great question, Mike.

I think right now is that it's, they're gonna go where the followers go. They're gonna follow the money trail, they're gonna follow what the followers are asking them to do. Um, I think that that's probably what we're gonna see because ultimately every marketer wants to show customer value.

And if the customers are demanding a TikTok or a Red note or both, marketers are gonna follow that because that's where the money is. All right. Well, I think I'm gonna end this here, but I find it ironic that the thing that we're asking people to move to is called Red Note because you know, that is the favorite color of the Chinese Communist Party.

But there you go. Alright folks, we'll be back in a minute. Modernize your business to fuel innovation and elevate customer experiences with the builder community.

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All right, folks, we're back. And if you visit Techstrong AI or maybe some of our other sites, you'll see a lot of content there from our good friend John here. But he recently paid a visit to Carnegie Mellon University and, uh, checked out their robots and ai.

And as I understand it, you actually got a ride from something, but I'll let you explain for that. Yeah, It was, it was pretty interesting. So I have gone back to Pittsburgh three or four times over the last decade because I'm interested in Carnegie Mellon.

I got invited there for this reporting fellowship around robotics 10 years ago. So for me, this is a really interesting contrast to what I last saw there and kind of the state of things back then. It was kind of an anomaly.

It was, it was a kind of a niche, cute market. I mean it, uh, based on the opinions of people out here in the West Coast, you know, something that was gonna happen eventually it will happen. Well, it is happening now and it is accelerating and I think we're gonna talk about CES later and like the physical AI element of it and the idea that we're moving from generative AI to agent to physical AI in physical AI or robots.

So I went to Carnegie Mellon and I had a couple of things hands-on experience, including one thing that was really interesting. It was a robot doesn't have a name yet, but it's being, uh, conceptualized for healthcare use. Basically, one of the things, and I'm going through this personally, is having a family member being transported from their bed to their wheelchair or from their wheelchair to the bed, which is incredibly hard to do.

It usually takes two medical personnel, especially if it involves like a bat or hip injury. So there's this device, basically you're in a wheelchair, you are backed up to a bed, and this device slowly and easily moves you from the wheelchair into the bed and then props you up in the bed within two minutes. And I'm telling you that this transformation of the transition of doing that, watching somebody do it, the human do, it was kind of lurching and kind of alarming going through this process.

It just felt like I was being eased or sl sliding on a conveyor belt into a bed. And the bed basically had almost a personality underneath me. And this was all done within two minutes.

So there was that, wait, Quick question. Did it tuck you in and read you a bedtime Stirring or flu? The pillow?

I, seriously, they said, what degree of comfort would you like? Mm-hmm. And it moved me to the exact PO point that I, that I preferred.

So there was that, and then there was something called Halo, which is a, uh, PhD project. It's a, it's a, it's a humanoid basically. There's a student with Apple Vision Pro goggles who's using hand motions to teach this humanoid how to act and avoid collision with humans.

And the idea is you put this into an industrial task. So the robot works side by side with the human, or for security reasons, you use the, the bot, whatever it is. It's basically something that moves and, and moves in human type gestures.

It was doing tai chi, it was boxing and, um, it, it's, it's, it's, and if you went near the robot or try to touch it, it would move away from mutual void contacts. Um, they're also teaching this robot how to work in a kind of a sense that kind of an assembly line where if it's moving cans or items, if the human comes by, it avoids contact with the human and avoids knocking over the, the items. So that was pretty cool.

Uh, there was also a, a fruit picking bots, and it's apples and grapes are hearts to pip. You have to do it by hand in some cases, or in the, in the case of apples, you have to reach up under trees. This technology, you sound as much as anything else because it's really hard to see an apple in a tree sometimes or reach it in a certain manner.

So this, this is another product. So what's interesting to me too was these are these products now, like 10 years ago, literally at Carnegie Mellon, they would build a raw robots from scratch. Now they have basically these starter kits.

So they'll buy, uh, from a, a third party, the basis of a robot for 20 $50,000. And then they'll build the intelligent or the learning into it for the specific tasks. And in, in a sense, what they're doing is they're showing you the physical manifestation of what we've been told will happen.

Now, the, the difference between Pittsburgh and I think the approach there, especially Carnegie Mellon and Pitts, is that they know hardware is hard, expensive, and it requires something that's in short supply in Silicon Valley patients, right? They also understand this stuff is expensive and it's gonna take time. So in a sense, they're kind of doing the grunt work to take us into these new era of using of robotics in terms of, uh, these tasks.

And the other thing that's interesting to me is that Pittsburgh has multiple universities. They've got like seven or eight in the city or in the area, and they've got a lot of warehouse space that has been unused after the steel industry didn't leave, but didn't kind of deserted the area. So in a sense, the warehouse space is ideal to safely test the robots.

So you've got a lot of these factors all kind of coming together. You've got the intellectual power, you've got the space, and you've got the infrastructure. The one thing that actually seems to be missing, and they're working on it, and there seems to be an obsession, is the use of a hand or a claw to do the manipulation.

So, um, last example, I'll mention, uh, uh, PhD student who's about to graduate, let me manipulate a robots, uh, and use of fingers. And, and one of the tasks that's gonna do is it's going to, um, apply, uh, drugs to, um, an IV like through the use of a syringe. It's not into the human, but into the iv.

So there are all these things going on, they're a couple of years away. Um, but it puts a practical face for me on something that we're hearing a lot about, which is kind of, you know, kind of proof of concept that we're hearing from places like Nvidia and Tesla and others. Mm-hmm.

So let me ask you, what is your sense of how long it will take these technologies to get from the lab into, and in the case of the, uh, robotic hospital aid? How long, you know, what, what's the timeline? Yeah, so that's something that I kept pressing and I would get variations from three years to five years.

And the thought there was, you know, we're gonna have a lot of examples or a lot of attempts that are just gonna fail or they're gonna fall short. Um, and you're gonna learn from the first version, like any type of technology, the first wave has its flaws, and eventually we get it right and we perfect it, or as close to perfection as we can. And I think that's kind of the mindset here.

Um, especially in uses of agricultural, um, tech. It's also, you know, the, the, uh, reception or the, uh, reaction of the workers. You know, we, we can, we can test something as much as we like in a lab, but the real test comes when you actually put it in a real life circumstance.

So we're gonna see this play out and it's gonna take a lot, take a lot longer than this kind of brings you back to like this digital workforce we keep hearing about from Salesforce or Nvidia. I think they're referring to more like these kind of tasks that are involve things like email or transcripts or kinda low level thinking. Now when it comes to robots, this is just a totally different ball game to me.

Mm-hmm. Lemme, Lisa, I can see robots on the backside of making manufacturing a car, or to Johns point, yes, working in the field in agriculture, but once we start integrating robots in with people, um, will there be kind of a marketing challenge that goes with that? 'cause if people are gonna have to have some level of confidence in these things, and are we gonna personalize these robots and call 'em by their a name, or are they just gonna be, you know, machines or things that, and objects that we consider to be, you know, disposable?

That's a great question. I can see it going both ways because as consumers, we all expect, and marketers have to do hyper personalization to reach their target audiences with highly contextual, relevant content. And so we as humans kind of crave that personalization.

Um, I think that we're going to see when humans and robots come together, I think a dynamic there that will probably favor some level of personalization, customization with, um, appearance to the way that it interacts, the way that it communicates, whatever LLMs it's using to have this conversation with a human. Uh, I think we're always away from that, but I can see, um, that personalization coming from the marketing side and into the relationship side as this relationship AI really takes off. Hmm.

I can see that happening. You know, you know, Mike, go Ahead. You know, Mike, you know, you know, it's something really weird that this, that question sparks something that made me think about Halo, which is this three and a half foot tall humanoid that when I first saw it, I was kind of a little taken aback and a little bit somewhat intimidated by it, but the more the people who worked with it talked about it, they were talking about developing its face, its hands, like little things that were not technical.

And, and by the time we were finished with this hour long demo, you kind of got the impression from them that they were, they were, had built this in a, I don't know how to put this, like a weird re like, um, relationship with it. Like they've, they've built like a, a kinship to it, that it was like a, a child, it was like their child, like their offspring, and they were creating, it was like Dr. Frankenstein a little bit, but, um, it was, it was, it was like, uh, they had a sense of empathy and, and they wanted it to learn and they were so proud when it learned something.

I don't think, I've never thought of it that way until I saw it up close. I know. Do any of these people have families of their own?

'cause that's how you feel about your kids, you know, I hope so. I don't think they did. No, these are all young college, these are like college students.

But in a, in a, in a weird sense though, I actually pull a little bit of a kinship or a little bit of like, oh yeah, hey, I, I, I was kind of rooting for Halo to, to, to make some strides. All right. I, I love dogs, don't get me wrong, and I'm probably gonna love robots, but they're not quite the same thing as having a baby, you know?

Yeah, I know. Exactly. I know.

I mean, I, we've got kids, but it's, um, there was also behind humanoid, there was the robo dog. So one of the things that Carnegie Mellon did, oh, you walk on the, you walk around the campus and it was freezing, but if you walk on the campus, you'll see like these little robo dogs being tested on the, on the sidewalk. It's So fun.

Too funny. Yeah, It was cool. Yeah.

Um, Lisa, this is more psychological than marketing per se, but aren't we also in danger of going the other way? And I will take no responsibility for anything. 'cause if it goes wrong and the robot did it.

Mm-hmm. Well, to John's point, and this is something that I saw at CES it will talk about on the next blog, there is a sense of pride with the creators of these robots. And what one of the companies that I saw that we'll cover in the next segment talked about is relationship ai.

These are, they're, they're becoming companions. There's a market for this, which I couldn't believe. So I think that from a psychological perspective, um, I don't know, I think we're gonna see more of that partnership come into play because I think that's the nature of, of human psychology.

Even though we'd be interacting with robots, I think we're a little ways away from that, but I can see a sense of, um, kinship developing there. And John, you said you kind of saw the same thing with these folks that are developing these humanoid robots and they're proud of their accomplishments, they're proud of what the robots can do, but I to your point, Mike, I don't know, I, I would hope that humans would take some responsibility if something un toward happens because of a robot's actions, um, that they would then want to correct whatever anomaly occurred and, and take that responsibility. You know, there's, there was something I I, I, um, I wish Alan were here just for this one little thing I was gonna mention, but there's a section of Pittsburgh called Lawrenceville, which used to be a hotbed for steelworkers.

It's a series of row houses, and I think it's in like, like the northwestern part of the city, and it was kind of deserted. It was kind of left for dead after the steel industry. That's, We're gonna use robots to make steel, but that's another issue.

Yeah. So that's the other thing. That's the weird thing, is now it's become, because there's so many warehouses there, now, it's the young place to be in terms of robotics.

So it's kind of of come full circle, right? Somebody mentioned the 1905 Detroit moment for Pittsburgh, which is a, you know, it's hyperbole, but given the focus, and here's another thing, Nvidia has announced its first, and to this point only, uh, partnership with the city is with Pittsburgh. And they wanna go whole full horse, horse into not only robotics on the ground, but under, under the ground in terms of water treatment.

I saw a lot of robots that were being designed to do water treatment for safety reasons. And also because these are small gaps that they're working under. They're also, the, Pittsburgh is opening a new airport at the end of the year, which we'll be having, um, its share of robotics in terms of robotics, cleaning, and, um, you know, we always see the, at SFO, we have the Cafe XI remember writing about that years ago, like basically a robot that makes you coffee.

Um, those are starting to pop up at airports, but it's, it, it is, it is a real thing. It is something that is happening. But again, I always want to caution that this stuff doesn't happen overnight.

And the one word that kept coming up was patience, because this involves a lot of testing, a lot of money, a lot of, um, a lot of, uh, serendipity probably in the ends. Um, but it is happening, it's happening much at, at a much real level. And we see it with autonomous vehicles, and I think that is, we, we've become used to that, accustomed to that.

So when you start seeing, um, robotic, um, apparatus doing certain things, it won't be as shocking as it used to be, or we thought it would've been. Well, I think, and Lisa, maybe you have something to say this, but these things have to get to the point where this, they're disposable because now they cost millions of dollars to build, and what are you gonna do? You're gonna, like in New York, they had a robot running around the subway, but by the time everybody got done kicking the thing, they were like, oh, this thing isn't really gonna stand up to that kind of wear and tear.

So, um, so Lisa, I mean, you know, part of the issue of taking these things to market is they're gonna have to get to some level of cost that is affordable. And so when something goes wrong with one, it's not that big a deal to replace it. Exactly.

It, it's, it's a scale perspective. And I think, John, you brought up a great point about patients, and I think that's something that we're gonna have to take into strong consideration to your, to your point, Mike, they need to be cost effective. They need to be, uh, be able to be produced and put into market at scale.

So companies are gonna have to be not just considering that, but making sure it's a reality. And, but there's also the patience and the sort of, um, acclimatization, I guess I, I would think between the humans and the robots to be, um, really copacetic and in and coexisting together and understanding on the human side what the robots there to do from an assistance perspective. But I think that, I think we're a ways away, but I, I like the patience factor.

I think that it's definitely something that every company that's working in robotics is gonna have to take into effect, and then it becomes a marketing opportunity to condition, if you will. And that sounds, maybe that's the wrong word. Um, and, and you customers are, or pe people to be able to coexist and understand the benefits that they'll get from coexisting with robots.

It's gonna be a really interesting space to watch, and I'm sure we'll be not short of lots of exciting developments in the future. Um, I'm, I'm pretty sure that the people side of that equation is gonna be the harder one, but we'll see. It will Absolutely be the hardest part.

You bring up a great point. All right, folks, we'll be back in a minute. We're gonna talk about CES in a retrospective because, well, it's one thing to be at a show, it's another thing to have a look at it in the rear view mirror 'cause things have a little more perspective back in a minute.

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All right, folks, we're back, and Lisa Martin was at CES and had a moment now to kind of take a look back and say, here's all the things we did look at and maybe add a little more context and, um, perception around what matters. Because I think when you're there, you're overwhelmed by all the toys. And then, you know, when you get home, you could take a minute and you go, wow, well, which of those is actually gonna change the world?

So in retrospect, and now it's your home, home and you're looking back at CES, what stood out the most to you? There were really five things that I went there to see. I mean, now we have some numbers.

1 million square feet of exhibit space, thousands of product launches. You hit the nail on the head, Mike, it's overwhelming. You, you have to plan it.

And this was the first CES where it actually went specifically to look at the show floors, product launches, meet with companies versus doing shows. And the best way to approach it, I think, um, is to really narrow it down to maybe four or five categories of products that you want to see. I wanted to see digital health.

I went to see voice recognition specifically, um, aiding the driving experience. I also wanted to see the humanoid robots, and I wanted to see the flying card or the EVTO Ls to see do they really fold up to a briefcase like they do in the Jetsons hint, they don't. But it was, what surprised me was the age check, the emphasis on age check.

I talked about this last, last, um, last week show where we talked with a company called Skip With Joy, who's making, uh, exoskeletons integrated into Arteric, um, uh, uh, active wear. And, and this whole, uh, uh, area is called move wear. What they're doing with not just assist people with hiking or with knee issues, but how they're getting into helping patients with Parkinson's disease.

So really wonderful applications, uh, for people who will really need it down the line. I saw a smart cane from a company called WalkMe that actually has GPS and voice integrated. So for people that have visually impaired.

So I thought that's, that's a, a great use cases there of helping people who need it. On the digital health front, I was excited about a product called Omnia, which is prototype from a company called Withings. And it's basically, it reminds me of those fitness mirrors.

It's a full length mirror that is aiming to do a full body scan. They have other products from smart watches to blood pressure machines, um, to urinalysis machines, et cetera, that we'll be able to track certain parts of your, of your health. But this prototype could be used, say, in, in hospitals and doctor's offices, in gyms, to help people be able to understand more and take more ownership of their health and share that with their providers in real time.

Um, what I also saw was the voice recognition really being able to be, um, more advanced and more integrated into automobiles so that drivers and people, passengers within the vehicle can have a conversation with the vehicle and ask it to do certain things, and it will respond accordingly, um, in a way that allows you to actually have a dialogue as they're built on LLMs. Then I saw the flying carts, which we'll talk about, and, and the humanoid robots, which blew my mind. Well, I'll tell you, the cane thing resonates with me.

'cause my father-in-law lives with us and he has a cane, and I, I can tell as he gets older, he's less reluctant to go places on his own, not because he is afraid of falling down, it's just that he doesn't want to get lost and he doesn't want to call somebody to say, I'm lost. So he kind of starts to narrow in his Yeah. Geography of places he's willing to go.

So I think, you know, that GPS thing, I'm not quite sure how the visual thing works on that, but, um, I kind of like that idea. It might be on his Christmas list list next year. I like it too.

Yeah, I thought it was an outstanding way to really, from an assistance perspective that was in the age tech area. Um, but then I went to see, I wanted to see the flying card. The, a company called Xang was featured there, and like I said, it, it, it does fold up into a support vehicle.

It doesn't fold up into a briefcase, but this is an ev, EV, E-V-T-O-L, which is an electric vertical takeoff and landing aircraft. Right now, the use cases are more for search and rescue, and then for those people, like maybe like the Elon Musk types that have $300,000 to spend on a vehicle that they can drive into a beautiful part of the country and then take their, their flying taxi out to explore national parks and things like that. But what was really interesting to me is that this is being produced out of China.

It is being mass produced, it is piloted currently. Um, you have to have a pilot's license and only a driver's license. I first saw driver standard driver's license, but that was when they were talking about the support vehicle.

And I'm like, okay, wait a minute. Um, it's, it's controllable via like a joystick. So for a lot of gamers, they're probably gonna be fairly adept at learning how to manipulate this aircraft.

And while it's piloted currently they are, they're, there are autonomous tests in beta. But I think from a first responders perspective, that use case I think is quite strong. There are other use cases down the line, but I think that I can see it being able to go into, especially with what's happening right now in southern California, go into remote areas and be able to assist, um, people, natural disasters and things like that.

So that was a great one to see. And also the fact that it's being mass produced right now was a surprise to me. Yeah.

There was one thing I saw that made me scratch my head in, that's not hard to accomplish that, but, um, there's a Maserati that was driverless or self-driving or whatever it was. Yeah. And I looked at it and I was like, well, that's kind of an interesting idea, but if it's gonna drive itself, why would I spend so much money on a car buy that I don't get to drive?

Why would best Yes, exactly. There, the autonomous vehicle section was at the Las Vegas Convention Center was huge. Every manufacturer from Volkswagen to BMW is involved and a lot of autonomy there that's making the, that their objective is to make the driver experience more seamless, easier.

But I agree with you, Mike. I wanna interact with my vehicle. I I don't have a Tesla, so I've never tried the self-driving that autonomy that it can deliver, but it is everywhere from Hyundai to Toyota, you name it.

Every manufacturer, auto manufacturer was there showing the different types of states of development that they are in the autonomous experience. So I, I wonder how far we are from that being a reality and from people actually embracing it. Um, that was something that, like, it was like we talked about in B block in terms of like the, the robots and people being so proud of what they've developed, same type of mentality with these auto manufacturers and the autonomy that they're, they're aiming to deliver over time.

Right. So if I'm not driving the car, John, do I care what it looks like? Or is everything gonna wanna be in a cyber truck from El?

No, it just want a functional, just want Yeah. Functional shell to get you from point A to point B. You know, that's interesting with CES.

So I, uh, Lisa, I'm, it sounds like you had a great experience, and I know you did because we were talking, um, we were texting, you were texting me about your discoveries occasionally. And I have to say, um, from not attending the show, and I, I have for God, like 10 or 15 years in a row, row, I lost track. I actually found this a very, very interesting, more so interesting than the previous shows, um, in terms of substance and in terms of practicality, especially in areas like life expectancy technology.

We're seeing a lot of that. And it, it starts early, by the way. So for Christmas, our 24-year-old son wanted his smart ring to monitor his, his vitals.

And, um, it's, that's not unusual. And I, and I, I've learned that there are people who were, who have they look at technology to extend their life by decades or if not forever, like the, the documentary with the, was it Brian Johnson who wants to live forever, the tech executive? But this is like real, I mean, people are embracing this stuff.

They're using it, they're finding ways of using it. And to me it is like so refreshing that we are seeing technology that has profound influence on people's lives rather than the stuff I was seeing the last several years, which was, here's an app that you can, you can use to drop your car off while you go to a play, and while you're at the play, they'll wash your car and fill your tank. That that's the app.

You know, it's like the, it, it's, you know, it's like we seem like we've taken like a quantum leap, so to speak, or we're doing things of much more substance than these kind of silly knockoff apps that we were seeing so many, so many companies doing, including some of the big ones. So I I I, I came away very impressed by this. Yeah.

Yeah, the practicality aspect was definitely there. Like Mike, you talked about, when I mentioned the, the cane from WalkMe, how that really resonated with you, the practical applications, I saw a lot more of that and I gravitated towards it. So it's great to hear your perspective, John, because I don't, this is only my second CES but before I'd been there as, as a broadcaster, and so I actually had the time and I was captivated into things that I thought this could really change the human experience.

I mean, everything reason why from, I talked about to like, like s sorry, John, like bulletproof, smart, secure pet doors, for example. I mean, you name it, it was there. Yeah.

The reason why I bring this up is that I, I used to every year go to an app, the Apple events, and in my, the previous jobs, I would go every year and you'd get caught up in the reality distortion field, right? And you get caught up in the hype, all the reporters did it. Then you'd step back a couple of days later and say, okay, a marginally better battery camera, bigger form factor, oh, a different color.

And you'd always feel as if you'd been cheated or, or in a sense kind of hood winged. And I think now, I mean, I think there's like a real push for technology that does good because we hear so many bad things about the tech culture and the bro culture and the oligarchs, and I think it's, we, we also have a huge number of people who do really interesting vital work that helps people. And I, I'm glad this is coming to the fore, And I always think back, and for all these great positive use cases, it's always worthwhile to take a minute and go, well, what could be the, the negative side of that?

And you were talking about a ring that would measure your vital, imagine if you would, there's five people sitting in a bar trying to drive their vitals up through the roof by seeing how much they can drink, and then they can measure who's messing each other up. Right? That's gonna Happen.

I never thought about that, that you're right. Right. There's also, there were also things, and I do wanna talk about this company called Robotics, because both of you turned me onto it, the humanoid robots, and I expected it to be a huge display.

It wasn't, but the first word, first of all, it took my breath away when I saw it. These are humanoid very realistic. But the first word that came to mind, literally out of my mouth, and I was there by myself, was creepy.

But it's, it's a customizable human robot. It looks like something out of a plastic surgeon's office. I'm not even kidding.

But what they're doing is, and these guys were like, like John, what you saw at Carnegie Mellon that we talked about in the last blog, so proud of what they've built. This is, this is a robot called Aria that I in interacted with. She gave me some recommendations on to have sushi, specifically at the Mandalay Bay, which is not where we were.

So she knew her way around Las Vegas. She's been on Fox Business, she's had a conversation with Fox Business, but it's built on an open source platform. So it allows integration with not only real bio's, proprietary companionship, ai, and that was a new term for me, the companionship, AI or relationship ai, but also integration with third party platforms like chat GPT.

It's customizable. You can take the face off, you can change the hair. It was manicured.

The only thing it doesn't have right now, uh, is a plausible thumbs. So I thought, oh, just like my dogs. Um, but you could actually have a conversation with this.

And they say it's from an application perspective, people are buying it from a companionship standpoint, and it's got application to real world applications and built healthcare on the education sector. Um, it surprised me, and it still, I, I think, why do we really need this? But they seemed very proud of what they've developed.

Although I will tell you in the photos, if you know the photos I sent you guys last night, I wanted you to see what this looked like before we talk about it. The females were very young. Um, beautiful skin, very like, like came out of the plastic surgeon's office or something like that.

The male that they had, there was just a bust and he had gray hair, really bushy eyebrows that came out like this, wrinkles. Um, but what it has, and I, but I wanted to talk about was this eye tracking and object recognition. So there was this, there was a stand of, of two or three busts, and there was a, a screen there, and you could actually see what the robot sees.

Um, the micro cameras embedded into their eyes, for example, that can track movement. You could see who in a, a little audience, it was looking at, um, interchangeable modular body parts for companionship and apparently healthcare and education. So there's still some things there that are like, what do we really need this?

But it was interesting to see because I haven't seen anything like that in, in person ever. All right, folks, it's gonna be a strange new world. In fact, I'm trying to figure out right now how I'm gonna manage my multiple robots that are likely to get jealous of each other, but who knows?

That's true. They're like children and like dogs. Yeah.

There you go. Anyway, hey, Lisa, John, thanks for being on the show today. It's always great fun.

Likewise. This was a blast. All right, thanks folks.

And thank you all for watching the latest episode of the Textron Gang. We have a whole lineup of Textron TV content coming up right behind that. By all means, stay engaged.

We do this all for you guys. We have fun doing it, but it's all for you guys. Thanks for spending time with us.

This is Textron tv. Hey, guys, thanks for the throwaway here with Jeff Graves, the CEO for Glue Air, and they just got an investment from a, uh, what they call a venture capital firm these days still. And, uh, we're gonna be talking here about, well, what is going on with intelligent network automation, because I feel like we're on the cusp of something different here in the age of ai, but we're jumping into that in a minute here.

Jeff, welcome to the show. Thanks, Mike. Appreciate it.

Great to, great to be here. All right, give us the backstory on the investment. Where did we come from and what are you guys thinking about using it for Sure.

2 billion serviceable addressable market. The, the, the reason for the investment is because of our platform and our team and going out to attack and be able to serve customers in what is now becoming a mature market. We have been paddling a well ahead of the wave for years, and now there are actually conferences, network Automation, forum Auto Con 0 1, 2, and three.

Uh, the next one will be in, in product. So it's actually a conference directly around network automation. And the thesis is that customers are currently self building scripts and playbooks and cobbling together their own software.

And the, this has happened before in the market. So if you think of data storage, backup, and recovery, uh, this happened before. If you think of compute, this has happened.

If you think of traditional RPA, this has happened. So instead of global 2000 customers needing to write their own scripts, if there is a platform that is commercialized, that is secure, that is safe and predictable, that actually allows customers to take the scripts and playbooks that they've been writing for years, and to be able to leverage what works and reuse their self build, uh, scripts, but also institutionalize that knowledge, uh, bring self-operating and safe and predictable change to enterprises that this can reduce cost, increase security, and really help customers and enterprises become much more agile. So there's a meaning of the minds between Quada capital and Glue Air.

And I would say one of the, the, the key things is that their operating advisor, SAE Zui, who was a founder of Ansible and the former chairman and CEO, is now on our board. And if there's anyone that knows about DIY self building scripts and playbooks, that's Sayeed. And the meeting of the May is that, hey, this is the future that customers need a commercialized platform to really get to the next level and bring that value.

This is what what is ahead for, for us. I can go into roadmap and, and whatnot momentarily, but let me, let me pause there because that's the thesis for the investment. So where are we on the journey here?

Because I feel like a lot of the times, the people with the expertise to automate the runbook are too busy holding things together to create the playbook. So, um, have we gotten to some point where maybe it's becoming easier to create those playbooks? And are we on the cusp of maybe democratizing network automation?

I, I would say that, that through a platform and we have what is called dial, uh, device interaction automation layer, that brings something new to the table. Uh, I would argue that if, if customers continue to script and write playbooks that it's actually script and playbook sprawl, and they actually can't just magically automagically figure it out, everything starts working. That new humans need to come in to relearn, uh, scripts and playbooks when, when those developers or or scriptors leave a company that it's very task oriented, that, uh, there's only a handful of companies that have been able to do this well at scale.

Almost every customer, every enterprise has an automation team, and it's become mainstream in terms of looking to tackle this problem. Yet, one of the things, and the key takeaway at Network Automation Forum, which is in in Denver and November, is that throughout the industry, customers are really scratching the surface. They're looking for help.

In fact, one of the taglines of Network Automation Forum is why haven't, and I, I, I may not get it perfect, but you know, why hasn't this taken off? And so you have a whole bunch of like-minded individuals that have gotten together to try to start to share ideas and recipes and solutions. And meanwhile, we have a platform that has over a million, uh, hours of, of Dev, uh, 301 release is strong.

We're in major customers that are public, uh, in terms of references, MasterCard, ey, Merck, uh, a variety of customers that that have, have been able to put their brands associated with Glue Air, and they're running glue air throughout their entire enterprise. So with through a platform approach where you bring best of breed called scripting and ideas can task oriented development. And if, if platforms such as Glue Air can go and call those scripts for some of the more simple things, but then for more of the complex, uh, things where it comes to like, say, an EVP and VXLAN fabric with 30,000 switch ports, and being able to iterate on a model and constantly change it and tune it for the enterprise need, you have to have safe and predictable.

Automation has to be intelligent with pre-checks and post checks, declarative item potent, et cetera. That's what Blue Air brings to the table. So instead of like self-build on one side and then a platform on the other side, if you could bring these worlds together, if we can go ahead and provide an on-ramp for the developers to accelerate, uh, their, their path in the enterprise, then you can bring both of these solutions together.

And so what we're doing moving forward is really opening up our platform catering to the developers, and it's a both and not an either or. We're better together with Ansible, we're better together with Python, we're better, better together with Linux operator, GitHub, NetBox, you name it. And so what you're gonna see from us moving forward is embracing the developer while, uh, making sure that executives at, at the businesses get the value.

And there's a dedicated business case for every single customer. So I would say through platform as well as subscription and these coming together, that there really is a tipping point that's happening. You take one or just the other, and you know, it's not, it's not gonna, not gonna work out throughout the enterprise.

You've got some, some competing interests. Um, but this is a really, really exciting path for the future is, is my belief and our belief and really the thesis for the investment. Mike, Of course, these days you can't walk down the street without somebody leaping out to tell you about their great new AI thing.

But how will network automation and large language models and gen AI and all these things come together from your perspective? Well, I will, uh, let me speak, uh, directly from our perspective. We announced Gluer ai and we have two co-pilots, uh, not one, uh, our CMO likes to Jo joke, uh, once better than one network automation copilot, two network automation copilot.

So we have one that's focused on net ops, which is being able to interact with the network to understand what is going on with the network, what are the vulnerabilities, all through a, a prompt, uh, where we fully integrated LMS and fine tuned and trained models. And what this means is that you can now say not only what is going on in network, but go ahead and make changes. And so if you were gonna do that with scripts and playbooks alone, without building all the pre-checks and post checks, it could be very, very scary.

With Glue Air, you can interact with the network devices and elements and fabrics in a safe and predictable manner. So now you can type commands and say, go ahead and upgrade all of the routers at this certain time and integrate with ServiceNow, where we are vulnerable and, and gluer and glue's copilot, NetOps copilot can go and do that. There's a a ton of value there.

We're working with customers in early field trials, which we announced getting feedback, and this will be very exciting. The second copilot is our net DevOps copilot. And this is targeted for those sophisticated users that know how to script, they know how to write playbooks.

And now instead of our IDE, our integrated development environment, that's 10 x faster and more efficient, uh, in cranking out intelligent automations where our sophisticated customers are writing applications, apps, and abstractions on, on top of our platform. Now, with an integrated LLM, it's like a hundred times faster, and I'm not exaggerating, it's like 10 seconds, boom. You've got an automation for Cisco, you've got an automation for Arista, you can do vendor swap, you can do some really, really amazing things.

So sky's the limit of where this is gonna go. So with two copilots, one for NetOps and one for net DevOps, and being able to share, have customers share in a community, which will be, you know, we're going that direction. Uh, now you've got the good guys and gals that can go out and be able to share best practices, share secure, uh, secure, uh, architectures and policies, and have like-minded customers really starting to, what, what it means is they don't have to reinvent the wheel, they can put it in production, it's been tested and validated, and glue will continue to stamp the architectures that we've gone through and tested.

So these are real world, uh, AI value where it's not like, Hey, here's just a, an agent that doesn't, can't do anything. The the fact that we're taking an agentic approach, but we have the ability to push and, and be able to, to make safe and predictable change. There's very, very exciting value ahead, Mike.

My suspicion is there might be more agents in the future as well, but how do all the agents talk to each other so that they can hand off tasks back and forth? Because the networking agent will need to talk to somebody else's server agent and somebody else's, uh, application agent at some point, right? So, so this is really in my my view, this has not been, uh, accomplished yet in the industry.

This all, it's all talk at this point. Um, having said that, you're completely right. This is where the future is going.

So Federation of Agents, now agents can, uh, gain information from each other and be able to interact and you know, it's yet to be determined exactly where the human will be in the loop. But from our customers, they do want a human in the loop, uh, to be able to make important decisions where if there are, you know, some more simple types of tasks, things that are, let's say, gold standard that needs to be remediated, that customers will allow that to just run on itself. I would say the sky's the limit of where this can go.

And we're gonna take a very real world approach because the last thing I, I want to do, because I've never done this, I've never got ahead of our ourselves and our skis. I wanna make sure that our customers bake it. I wanna make sure that it's in their networks at global scale with 30,000 150,000 plus devices and they're seeing value from it.

Well, let me give you a couple of examples. Uh, one example is an observability agents. So most of the observability platforms out there, well actually all of them, they look at problems.

They don't actually fix problems. We're not interested in being an observability company. That's not what we were designed to be.

There are great companies that are out there, you know, ala Ken Tech, uh, you know, you look at some of the things selectors doing that, you know, some of the folks that, that spoke in network automation form, and they go and see issues. So if we have their agent be able to interact with our agent and say, okay, here's an issue and we believe it's, it's down to these devices or these parameters in the network, they can actually hand that off and create an integration where glue work can go off and make those changes. Then glue to ServiceNow informs Ken Tech that the problem is fixed and, and life is good.

In fact, two years ago at NU Open Network user group, we showed Ken Tech and Luer interacting with a live Zoom call. Where in that POC, there was a Zoom call, someone made a change in the network, the Zoom quality went bad. Uh, Ken Tech notified Glu Gluer were often fixed it.

Now, these were not two agents that were talking, these was, this was a dedicated integration. It's gonna get much more interesting when agents can interact and talk. We're not there yet.

I don't believe anyone's there yet, but that is exactly where the future is, is going. So that's my view. To what degree will I need to be a specialist?

I mean, I think we all agree that some human will be in the loop, but does that human need to be a networking specialist going forward, or can they be more of a traditional IT administrator who is maybe networking savvy to a degree, but they're trying to manage all these things in concert with one another? We have, we have roles for both, and that is the, the part of the key with the glue wire platform. In fact, one of our, one of our customers calls it the, uh, the brain drain prevention, uh, machine that, that a lot, one of the most sophisticated network engineers come and go when they go.

A lot of the, the, the, the, the historical knowledge goes with them once the designs and architectures are baked into glue air and there, there is part of our platform that allows the network architects and the senior network engineers to be able to onboard their designs. Once that's done, then that can be handed over to some of the less sophisticated folks that are more network operators and expose a handful of knobs to turn things that won't like go out and break the network at scale. But, but some of the best practices to go through and be able to remediate, uh, things that are, that, that may be broken.

You know, one of our, one of our advisors, Kevin Carney, uh, he was the chief network architect at, at MasterCard, and he, he selected us when he was at, at MasterCard. He has been on audio a whole handful of times. It's like, what is, what is one of the biggest issues that happens when you're troubleshooting a network?

It's like, well, uh, when you're removing security restrictions, a lot of times either network engineers forget to reapply them or they don't stick because they're in there manually troubleshooting. And so, you know, part of the value for customers is that if there is something that's detected that, that the network ops team has the core best practices that are safe and predictable, where they can go through and push a button or agree that this can go back to gold standard, even with a human in loop or without, and things will get, get, get remediated and, and put those security, uh, the, the, the security policies back on, on on the devices. And so it doesn't require everyone to be an absolute complete rockstar and black belt.

Um, that there has to be both. And the other thing that I would say is that there's a lot of challenges, and we've seen this throughout our customer base. You can't be the best network engineer in the world and the best developer and scripter in the world.

Now, there are a handful of unicorns, but that's not, you know, everybody. And so a way, you know, as you said, democratize, uh, automation, I believe, I think you said something democratize scripting or playbooks or, or whatnot. I mean, that really is the approach that we're taking, but it's more about enabling those to what, what their strengths are and allowing them a path to be able to move to the other side more of an elegant manner.

Because if you have to build everything from scratch, you have to have really, really great network engineering. That's one person. Then you have to have really, really great, uh, development.

That's another person. Now you're taking two people out of the workforce to be able to try to self build something. So if you can give them a platform that allows them to go faster with the skill sets they have and meet in the middle, that's why customers are buying glue air.

Where do you think security will fall in this equation eventually? 'cause it does seem we're seeing a lot more convergence of networking and security, especially around security operations. Um, are, are these things going to meld somehow and what does that look like?

They already are, and that's a great observation. I believe this is happening very, very fast, Mike. So we've seen a lot of teams that, that have actually come together where you have both the network and security that have been collapsed.

And so security policy is, is is a really big deal for ware moving forward. Um, it's difficult to have a security policy platform for traditional firewall rules and then a network platform. And customers are asking us, why are the two platforms?

So you'll be seeing some pretty large announcements from, from Blue Air, uh, and what we've already done with security policy throughout, throughout networks and, and these roles, uh, are I'm seeing converged because you can't just think of networking separate and have your hands in the air about security. That security has to be a key ingredient. It has to be a way of life and make sure that everything that network engineers do in their designs are secure.

And so if you can have multiple roles in an automation platform to cover both traditional networking as well as next gen networking as well as IOT and o ot, and by the way, massive security vulnerabilities in, in I OT, NO ot, throughout, you know, those networks, they're not regularly patched, that are not regularly, uh, segmented with macro and microsegmentation. And it's a real difficult pain point for customers to be able to, to to solve. And so if you can have a platform, if customers can have a platform that not only handles multi-vendor, multi-domain and traditional networking as well as gen networking also, uh, security policy, firewall rules, et cetera, and then moves into iot, ot, this is a platform that customers are highly, highly interested in.

Outside of, you know, acquiring your platform, what are you seeing customers who were well prepared for this next wave of automation doing? Or is there a mindset here? Is there a cultural change?

Are there, uh, is it just technical training? What's kind of the secret sauce? It's a work in progress.

And I would say that that it, I would say the big, the big change is doing things differently. And it has to be driven from the top, meaning top of customers has to be exec driven. The customers that have been able to get the furthest along and have the most value, both financially from security and outage prevention.

This is driven by the C-I-O-C-T-O VP of it, depending on the size of the organization, where they've bonused their teams on the transformation. And if they do it the old way, meaning the manual way or go off and, and write a script, if it's already baked in a model with glue air, if it's not baked with glue air, if it's, if it's something that the script's been working and that's part of an RPA workflow that is tested and approved, then great. But if they go off the rails and they go and do something manual that there are repercussions that like no, they can't just go off and make manual change.

And so, you know, the adoption of everyone rowing the same direction, not they say something in, in, in a meeting and then they go off and, and still do it, the, the, the, the same way that doesn't work. And so this is more human behavior changing at operations more than anything else. And the best part about it is that these network engineers and these developers, they get their weekends back.

Like they're not getting the calls at four in the morning. We've had, uh, over a 99% reduction in network outages throughout our customer base, 300, uh, times faster OS upgrades. We're seeing a 50 to one time and cost savings with customers.

And so if they adopt it, if they get with the times and they look to do, they're gonna free themselves up for more of the strategic things. And instead of, you know, wrapping themselves into just like trying to keep their hands on the keyboards, that actually they'll do be doing things for the business that moves the needle more. And, and when the IT execs recognize that behavior and they reward that behavior, then we see transformation.

Now what I would say is that it's really, really hard to get that transformation if everyone is taking just a pure scripting and self building mentality. If you couple 'em together, meaning with best of breed platform plus some customization and scripting on, on the side, I mean, it's pretty amazing what what can happen. And we've got some just amazing, amazing customers that have, have been able to move mountains and get a tr tremendous amount of value, which by the way, is why a private equity firm has now moved forward with gluer because I believe we are the first company that has gone the private equity route, which means we get to continue on the mission, we get to continue to grow, and we really get to continue to be a leader in the space instead of ending up in, you know, business unit of, of a strategic.

So, All right, folks, sharing it here. There's an old saying, like, if you keep doing the same thing and expecting a different result, you just might be crazy. So it's time to think about it differently, right?

Hey Jeff, thanks for being on the show. Thanks, Mike, I appreciate it. All right, and back to you guys in the studio.

Hello and welcome to the Techstrong AI podcast. I'm Amanda Ani, and with me today I'm excited to have th vasu Devin. He is the executive VP of product for skyhigh security.

How are you doing today? I'm doing very well, Amanda. Thanks for having me on this podcast.

Happy to have you on the show. Can you share a little bit about skyhigh security and what services do you provide? Absolutely.

Um, Skyhigh Security is a cybersecurity vendor, and we are specifically in the security service edge market, which is part of the broader SSE market secure access service edge. And at the heart of it, uh, Skyhigh security helps organizations protect their users, uh, as they access, uh, applications. Applications could be cloud applications, applications could be on-prem applications, doesn't matter, but to be able to provide the zero trust, uh, security for an access standpoint for their users.

And once they have access to these applications, we help organizations to be able to protect the crown dwells, which in today's world is really the data. So being able to protect access and then being able to protect data is skyhigh security's, uh, biggest strength. And we do this, uh, from, in a unique perspective because we are the only vendor that offer this ability to do this both on-prem and in the cloud, and also do this in a hybrid fashion.

Wonderful. Thank you for sharing that. So, our topic of discussion today is, uh, why an AI leader is necessary in creating a strong and secure AI strategy.

So from your experience, can you share what are some of the challenges that leaders are facing right now? And and how do you suggest that they solve this, uh, with, um, a strong AI strategy? Yeah, that's a very relevant question, uh, for today's times.

Um, as you know, we all know that AI applications, uh, are indeed on the rise, uh, in pretty much every organization that we speak to. That is all that is already an initiative for leveraging the power of AI into their day-to-day operations. So what this means though, is that this comes with, uh, heightened security concerns.

Uh, as an example, if these applications, um, you know, the way AI applications work is that they all, they require access into vast amounts of sensitive data within the organization, and then they have what they call a, a learning on top of this data before these applications can be, can be leveraged. So at the end of the day, uh, how do you make sure that your sensitive data, uh, is not being leveraged for being trained into these AI applications? So that's one part of the puzzle.

The second part of the puzzle is, uh, do the right set of people have the right access for these AI applications? So, as an example, if it's an application that's being built internally to mine, a lot of the customer data, and then being able to provide rich valuable insights, there may be certain insights that only the executives should be able to see, and there may be certain insights where a marketing person or a customer success person can see. So to be able to ensure that the right person with the right role has the right access to the right AI application becomes a key part, uh, of the security, uh, ask as well.

And lastly, every region, every industry is, uh, you know, evolving, uh, as, uh, you know, the use of ai. And so a lot of regulations are coming up, uh, on the use of AI and, and the use of AI applications in the industries. So to be able to understand the compliance and regulatory challenges, uh, becomes, uh, an interesting challenge as well.

So why do I mention all of that? Because these are all the big challenges that an AI leader within an organization needs to think about. A, he or she needs to think about what is the business need of that organization and, and how can ai, uh, how can they bring in the power of AI into the organization so that they can bridge the gap between, uh, the power of technology and power of what, what the organization really needs.

So we have to make some IT decisions, as in when we build these AI applications, what LLM models are we going to use underneath the covers? Are we going to use a pass service host an AWS or Azure or GCP or OCI, or are we going to run this in-house? Uh, which means that's gonna be capital intensive, uh, but it's much cheaper to run it on an operational basis.

So then they'll have to make these decisions on security. What kind of security controls do they need to ensure that, uh, data doesn't, uh, get exfiltrated the right person has the right access and ensure that we manage through the regulatory and compliance risk? So really the, the significance of the AI leader then becomes defining the AI strategy and roadmap, which includes, of course, security and then ensuring that the organization is well positioned to take advantage of the power of AI for their business needs.

So once you have a good AI leader in place, do you have any advice or tips to ensure that there is good communication between that AI leader across the organization and be and between all important departments? A Hundred percent. And I think that is one of the key responsibilities of an AI leader, is to drive what we call strategic alignment, which includes cross-functional collaboration as well.

So what I mean by strategic alignment is if the AI leader does not define a clear strategy for the organization, it can very quickly lead to disjointed efforts across the organization. You'll have different business groups leveraging, as an example, different LLM models for meeting their own needs, their, or leveraging different SaaS applications to meet their own needs. So the, so one of the key goals of the AI leader is to ensure that all AI investments within the organization, they're aligned with the company's strategic priorities.

And the, the important thing to do here is because most of the time these AI initiatives, they span multiple departments. So the AI leader then acts as the unifying force, you know, ensuring collaboration across technical teams, business units, and also of course, the executive leadership so that they can achieve these cohesive results. And then how do they track the end results?

Um, you know, as, as we look to integrate ai, a lot of company leaders, I've been reading some articles, they, they don't see that end value result that they expected. Uh, so what advice do you have for how do they achieve that end goal and track, uh, the value from it? Yeah, I think this is a very good question because this has come up in so many of our customer discussions about as they're double, you know, adopting AI within their organization, how do you find the right return on investment?

I think it all starts by setting the right measurable goals, uh, by the AI leader. So for each ai, I mean, where we have seen this work well is where for the AI projects, the AI leader, uh, you know, defines the success metrics for each of that project. And it depends upon what that initiative is.

So as an example, um, one of the com uh, one of our customers, they were leveraging the power of AI to mine through, uh, customer documentation. And the reason why they wanted to implement that was so that their end customers have a two things. It, they can find the right information faster, and number two, they don't have to spend a whole bunch of, uh, time in navigating through their documentation site to figure out the information.

So again, kind of related to the first point is they wanted them to find the information faster. So then if the, in, if that is the overarching business goal that defined the AI leader will say, if I'm going to be leveraging the power of ai, first of all, how does the experience then get better? And now as the end user comes and searches for information, then how do we make sure the information that they're looking for is indeed the right information and the customer's satisfaction is still very high.

So those are the two key measurable goals that they will track. And if they see that the changes that they have done on the AI project are already leading to better measurable outcomes, that is indeed the success from the initiative standpoint. Wonderful.

And then another question is AI is advancing so quickly and rapidly. Um, how do business leaders keep up and, and keep the most current, um, aspects of AI as it advances so quickly by the time they get one thing done, it it might be already obsolete? Yeah, yeah.

This is, uh, you know, it's indeed true. Um, this, this space is evolving very, very fast. So there are multiple, uh, way things that are evolving.

I think the first piece of the, the technology itself is evolving, right? There's no questions about that. Uh, every day, um, you hear about new AI SaaS applications, uh, coming up, which solve a specific problem for the business.

Every day you hear about new LLM models coming up or existing LLM models getting updated. 0. You know, this just keeps on enhancing as things go along.

So that's the technology being up to date on technology. The other aspect of it is that the security aspects of it are also evolved. Uh, what, what I mean by that is you, you know, you, you need to ensure that you are on top of what, what are the security considerations I need to have for AI applications?

What we used to be doing for other SaaS applications may not be necessarily true that I need to do for AI applications. As an example, previously you were probably happy just saying, I'm going to allow or block a certain application, or I'm going to not allow sensitive data from getting uploaded into that application. But in today's world, for you need to look at one level below, what is the LLM underneath the covers, what is the risk of the LLM model being used?

And so as the AI leader, you need to think about and keep evolving your understanding of security as well. The, the next awareness that they, they always have to be is on regulations, uh, governance and regulations and compliance, right? What, what, because every day you hear about different countries or different regions coming up with new AI regulations.

So if you are a multinational, if you are operating in, in different geographies, then and, and you are dealing with a whole bunch of AI application with sensitive data, uh, then you need to be on top of what are my, uh, you know, how do I make sure that I'm, I'm compliant with the regulations of those regions? Because anytime you're non-compliant, then all the then, and then the, you know, the impact of not being compliant would be fines and that PR and all that, which is all going to be a negative impact. And it'll, it'll wash away all the great work that he or she might be doing with the use of AI within the company.

Absolutely. So if there was one key takeaway you could leave our audience with today, what would that be? Yeah, I mean, the one key takeaway would be embrace the power of ai.

It is once in a lifetime technology and it is going to make your life yours as an organization's life much, much easier, making them super more productive. But as you do that, think about definitely the security implications of that. Security needs to be bolted in right from the very beginning, making sure that sensitive data is well protected, making sure that you have zero trust access to these applications.

And of course, last but not the least, always be on top of governance and compliance as you build these AI applications and then deploy them within the organization. Wonderful. Well, thank you so much for coming on our show and sharing your insights with us today.

Thank you, Amanda, for having me. And thank you to our audience. Stay tuned.

There's more. Hey everybody. Mitch Ashley here with DevOps Dialogues.

I'm v VP and practice lead of DevOps and application development with the futureum Group. DevOps dialogue is all about conversations about creating software in the era of ai, cloud security, all kinds of aspects of the we have to deal with of creating the best kinds of applications that deliver the results that our customers, our business, our partners, are gonna be most fulfilled from and receive the greatest outcomes. So we're gonna be talking about Gen AI and doing AI projects and, uh, some tips and information that will help you all in doing and, uh, taking on those, or maybe you've already started a, a project and looking for some good advice or information, some insights.

We're gonna do that by the two folks that are joining me on the podcast today. First is Ed Eduardo from, uh, do It. He's Senior cloud architect and Joby George, who's global head of partnerships at Wvi eight.

Welcome gentlemen. Eduardo, would you introduce yourself, tell us a bit more about you and also, uh, tell us about Do It? Absolutely.

Thank you. Well, um, I'm in Canada, so the weather over here is very cold. Sorry to get called.

I'm a senior cloud data architect, and I've been working with AI mls, uh, for quite a few years now, uh, before Gene ai and at Do It, what we do is we'll help our customers really unlock the true value of the cloud. And so we help them strategize, create architectures, and really become efficient when using the cloud. Fantastic.

Joby, I'm Joby George. I run the global partnership at VVI eight and manage all our cloud technology and system integrator partners. VVI eight is an open source vector database, uh, for building, uh, AI native applications.

Without developer first approach, developers are able to go and use LLMs and embedding providers and frameworks to build their AI native applications. And we, and they're using it to build applications like hybrid search, semantic search rag applications, or some new things like recommender systems or generative feedback loops. And now the agent cracks.

We have around thousand plus customers like Cisco, Morningstar Bunk, who are building all kind of gen AI and search applications. I'm glad to be here. Thank you for inviting me.

Excellent. Thank you both for being here. Thank you to your companies for being here as well.

You know, le let's start out about talking first about, uh, kind of taking on gen AI projects. Uh, it's an exciting time to be in the industry. There's, you know, technology's changing every day.

There's announcements every seems like every week, every day, whether it's Microsoft or AWS or you name it, you know, new model coming out. There's so many, uh, kind of technologies to learn. I know from my own experience leading software teams, you don't always want to venture into this alone, um, for fear of it becoming too much of a science project rather than really getting value out of a project.

I'd love to hear your thoughts. Um, maybe Eduardo, if you wanna start out first, since you work with so many customers building these kind of applications, um, what, what are some of the best ideas about how to get started and how to decide what kinds of applications are best to take on with generative ai? Yeah, I mean, we, we are, there is so much going on.

There is just so many things as you were mentioning. Um, and we take an example of AWS, there is so many services available to our customers. So a lot of the times we hear like, Hey, I wanna use GN ai.

I really wanna leverage what is possible with the technology, but I don't know where to start. And really it's starting from the place where, where is a good use case for personalizing the customer's journey? How can you really hyper-personalized that experience to get the most value?

And everybody's collecting all this data, it's just very powerful data that allows us to get into the nitty gritty of that personalization. Now with Gene ai, we are able to unlock that. Before we were creating personas, we were creating buckets that will put a customer in a certain category, but with Gene ai, you can really go dive into that custom personal experience.

And if we take a, like I was mentioning in AWS as an example, they have great service called Bedrock, where you don't have to create an infrastructure, you can start utilizing it with, uh, on demand and send text. You only pay for the amount of tokens or the amount of words that you send out and off you go. And it's a really great way to be able to start testing what is possible.

Now, I do have to, uh, whenever I talk with my customers, I do have to mention that HN AI is not a silver bullet. Like nothing in nothing in technology, right? So it is great at certain things.

And when you are doing this test with Bedrock or with any other cloud provider, make sure that you test that limit. Where does it start breaking? Because that's the iteration where you are gonna be able to start making progress and start adjusting things.

So it fits your use case. But, uh, to start with is think enough about that hyper personalized customer experience you wanna deliver and then start testing with the, the managed services available to you in the cloud. So that's kind of like where I'll start.

Um, That's a, that's a great, great point. And um, I'll, I'll just add to that, uh, that itself like, uh, uh, so for example, VV eight is one of the companies which integrate with Bedrock. I think, uh, just to roll back a bit, right, uh, generative ai, this is a totally new way.

It almost feels like, uh, almost like the internet browser coming into the market type of a landscape. And everybody wants to build all of these application, everybody wants to build an application which looks like a chat GPT or equivalent of those applications. But, uh, they, they might not have the skill gap, they might not have the real understanding of all the use cases which they can go and tackle.

So kind of taking a more selective approach to how and what they can build and what is the platform kind of fits in for them as they're building these applications become super, super critical. But the possibilities are endless. Uh, as Eduardo said, there are all kind of applications paper are building, starting from simple search type of applications, rag applications.

We always had a search or a database application architectures to build these applications. But machine learning and the generated AI has totally changed what the information retrieval and the quality of the information which is being retrieved from dramatically. So now we are able to get to relevant information.

Eduardo talked about personalization, so kind of being able to do that, personalized apps, uh, so we have an approach where we call it as a generative feedback loop, which allows you to build these personalized application at scale. And, and that's where the motion is. So some of the things I would kind of like highlight is that be very careful on what the use cases you're trying to, uh, approach.

Do you have the skills to kind of do those and kind of quantify and uh, approach it in a way that you can de uh, deploy and get ROI on these applications quickly? Andrew, Go ahead. Sorry.

No, go, go ahead. I was gonna, you know, you mentioned a lot of great things and you start getting a sense of it, uh, when you are starting to test, 'cause you get a sense of where the gap is on the skills and where the gap is on the data. 'cause the data is a great like motor and the oil that will get all these going, right?

And for, for all, for every company out there, you have access to the same models. You have access to better, you have access to all these other models for other cloud providers, but the differentiator becomes the data. And so when you start testing, you'll be able to start identifying, oh, I can I start using this other data this way or, or this other way.

So yeah, I think those are great points. Yeah, I was just gonna bring up the data, right? It's really the center of the universe is also oftentimes is deciding where do I put my turn of AI application?

Where, where in the cloud, et cetera. Where, where do we need that data? Uh, you know, one of the w with so many things happening in, in the a AI fields, particularly generative ai, you know, we heard a lot about rag augmented generation.

Let's talk a little bit about what that is and why, why we use that with the importance of it. And then also, of course, we hear a lot about agents, right? Being able to, uh, create your own agents and a lot of different capabilities that are being launched in the market.

I suspect we'll see a lot more over time. Um, anybody wanna take on rag? I think maybe, um, Joby, you were kind of mentioning that earlier.

Yes. Um, so RAG is a very logical, uh, evolution from a search based application to kind of building a retrieval based approach to building application. So think of a native rag pipeline consists of two parts.

One is a retrieval component, typically, basically composed of an embedding model and a vector database, which is V vvi eight is one of the providers in that. And then there is a generative component, which is the LLM or the models, which you can use. You can use foundation models, you can use your own custom models, whatever you want.

So at the inference time, or basically what you're trying to kind, kind of, when you do a query, what you run try to do is do a similarity search, try to find an index of documents, which is a subset of the documents which are more closer to the kind of curry are you are trying to put in there, which allows you to retrieve the most similar document and provide those as a context to the LMS to build, uh, to build their response. A generative response back to you. This is fundamentally different than just doing a searching or a sickle query type of an approach.

Now you are able to build some interesting applications on top of it. We are able to take the plain old chat bots, think of it as like four or five years ago. And now they are really looking like they're really answering your questions in a more relevant way.

You're also hitting information, which is very relevant to the context because you have actually done that similarity search, and that has provided that context, which allows you to now provide a response back, which is very relevant to the information you're trying to retrieve it. So that's a rag approach, and I see that the RAG is now kind of transforming to what we call as an agent rag, where you are taking the, both the LLM and the database and now adding some kind of a memory and a workflow tools, uh, as an elements to it. So it's a very logical, um, evolution of the generative AI applications from rack to agent racks and to eventual, uh, more complex workflow based applications.

And to add to, and to add to this is that there is a race to be able to identify the best content for the question or the task at hand. And we need to, like, organizations are looking for ways to optimize that search. And so databases like Aviate, uh, help allow the search to be more efficient.

The longer the text that you send to the, to the model, the longer it will take, the higher the cost will be, and the more noise you will introduce into, into the inference. And so there is a lot of repercussions about that. Um, and so we are trying to identify what is the right amount of data, the minimum amount of data required to answer this task.

And there is a huge field that is going into that. Now, on the agent side, there is also great, uh, work being done around that. And in a way I'm like, I don't like the work agent too much.

I like the word tools better than agent just because it is more like a tool that LLM can use to retrieve data or to do other actions. So there is this combination of like vectors and embeddings that you have in a vector database, but also a lot of organizations have data, data in relational database like my SQL or Postgres. And so they wanna access these data as well, right?

So it's another way of being able to use tools to access data, get the specific points that they want, and enhance the, the prompt that is being sent to the model and make it as short as possible. So that's kinda like the sweet spot that everybody's looking at, how can I get there? And these databases, vector databases are making a big difference when, when doing that.

You know, a couple other points too, uh, rag is a way of, uh, leveraging data as part of kind of the front end to the model without exposing the model to your data or vice versa of exposing your, your data to the model and how you can augment or add to that additional content. And to your point, SQL databases, it could be documents. There's all kinds of different, um, uh, points of which we can pull data from.

And you mentioned vector databases, uh, Joby, one of the ways we can kinda do high performance retrieval of information out of, out of, uh, generative AI models, correct? Yeah, No, that, that's absolutely right. And um, like one of the point I want to kind of come back to is like around the data silos and the data we are talking about.

Like I come from the data space. I, I live through the big data wave and kind of see this kind of a wave come in. So, um, and I think we are in a very early ginning of the gen ai, uh, geni landscape from that point of view.

There also was a promise of going and taking the unstructured data and making it available relevant for you to kind of go and build applications, insights, and analytics on top of it. I think we are kind of doing the same iterative loop in that that way. But now the game has changed a lot, uh, from the point of view of like earlier it was a text-based, it was structured data, which you can actually go out now by taking each individual data element.

So be it data sets tables have the metadata and the schema and some context which you can capture in a structured data. Taking PDFs, taking images, being able to take all of those multimodal data sets and building, bringing it all into the same vector space, and then kind of doing a relevant data search makes it super, um, super intuitive and super useful for creating some very natural looking applications, uh, which are very different than a typical database type of applications. I think that is the big jump which we are seeing.

And again, it's a journey. Like if you look at the progress around, like the new things which have come around, covert co poly and some of the stuff implementations we have done around acon, this is a journey, and I think we are just in the early evening of it that where the lot of innovations will come in, which will make it super important, uh, for a Vector database to be a core component to building some of those applications. And that's how we tell our story, which is like we talk about AI native stack, and when we talk about AI native stack, basically LLM is the core foundation and a vector database so that you can actually build the applications on top of it.

And then the underlying layer, the architecture has to be right, like it has to be containerized, it has to be scalable in the cloud. Like AWS being able to kind of get that scaling is critical part as your data sets grow and you bring in all the data into a vector space point of view. Very good.

You know, one of the things with new, working with the new technology is kind of keeping costs in mind, right? And these are new services that we might be using from a cloud hyperscaler like AWS, um, but we don't have experience. I mean, we had that happen early in the cloud, right?

What, what does our cost profile look like as we start to consume these resources? Um, one of the things to cons, what are some of the things to consider, uh, in terms of costs that either anticipate learning from from experts like yourself or that you'll experience once you start to develop an application and see what its performance and consumption profile looks like? Um, Eduardo, I also like test a lot, iterate fast and fail fast.

I mean, it seems like, uh, there is a lot of entrepreneurs out there that are doing this to be able to get business up and running. Uh, when it comes to gene AI and these workloads is the same thing. There is no one solution fits all.

And so you need to iterate fast and understand what are the limits of the technology, right? Sizing these workloads is very, very important to reduce costs. And that's true across entire cloud, not only on gene ai, but every workload, uh, is not simply by going to the cloud means that your bill is gonna be cheaper.

Uh, rather you need to understand what the architecture is, make important choices, right? So for example, on the data side that we've been talking about, we can easily put a lot of data in S3, but if you don't have the right format and the strategy there, you can be paying 90% more or a hundred percent more to access that data that if you have the right strategy data, you have the right format and know how to access these data, right? And that has an implication not only on the cost, but on the latency of the model, which ultimately is your paying for how long that model is running.

So all of this is, has a effect if you do not understand the building blocks to optimize every layer of the workload. Um, uh, and that is goes with tools as well. Like we were talking about rack and agents, you can create a lot of tools for the model to access many different things.

Great, fantastic. But it doesn't have to, it needed in order to do that. And just a quick tip, for those that are listening and you're working with agents, when you start adding four or more agents to the model, it is gonna be a diminishing return on the quality of which tool is being utilized.

And there is a tendency to use the first tool presented to the model. So if you have four more, the fifth tool is not being utilized, and you are having this code there for nothing or paying for it for nothing. So all these architectures we need to revise.

That's something I love to do. I love to drive it with my customers and say, okay, what is it that you're trying to accomplish? How do we architect this to fit your current estate, reduce your bill, and be able to get you in a path where you are able to build for the future?

So, I mean, it's a long answer because there is no really super bullet to all of this, but I mean, Jovi, you, you, you, you experience that. I mean, you're in the database space. Yeah, right?

No, I, I can certainly add add to that, right? And I kind of like break it down into two parts. Uh, one is on the, like development and developers and others on the deployment side, right?

So if you look at from the developer and the a and the development side of the things, everybody wants to build charge p everybody wants to build the most complex generative AI applications. And hey, choosing the tool, being able to look at the use cases and quantifying the use cases to the segment so that, what is the ROI going to look like? Uh, because nothing comes for free, as Eduardo said.

Uh, you can do as fancy applications as you want. Uh, if you're doing a e-commerce application and you're trying to put out a personalized shopping cart and you have certain SLA on QPS for you to do that serving, you cannot do all the aspects of the generated AI to kind of bring that applications to life. So being able to kind of do that trade-offs, like very standard practices across any workloads, those are all the standard practices which apply costs do add up very fast.

And in the sense that this technology is evolving, the, it's in that stage of the eco, uh, stage of maturation where a lot of innovation happening. Optimization is still kind of coming in as part of the logical maturation curve. We have started focusing a lot around cost versus performance.

So a lot of the features we have introduced things like multi-tenancy, where if you are not you, you can break your workload into multiple tenants, then you can offload some of those tenants down, you can offload them all the way to the disk and save cost. So be being aware of the cost consciousness about these deployment models is super critical. Coming back to the developer side, we kind of look at it like we built it on Kubernetes.

We built it on AWS uh, basically to provide that choice where customers can start on a multi-tenant SaaS type of an offering with like $25 a month type of an offering and build an experiment. But as they go bigger, they can actually go and get to more of a managed hosting. So we basically, we are able to take the same, same environment, move them into a single tenant offering from our end, and or they could start with a bring your own cloud kind of an offering and experiment with that.

Most funny thing is that as people are deploying this, uh, use cases, what you're seeing is that their cost and ability to manage these deployments is, is not there because, again, evolving ecosystem that they're coming back to, uh, us in some form to go and host it for them and run it, because that is a lot more cost efficient than them trying to figure out and going and running it. So there are a lot of these pieces coming in because of the state of the market. It is, It's really good point, because oftentimes the architectural decisions you make the service, not just the services you choose to make.

You can create very high consumption, you know, types of applications, particularly in AI and not realize you're racking up a lot of costs. And that's, of course the fastest way to get your second projects, uh, canceled is have the first one go way, way out out of control. So you wanna think about those costs upfront, right?

Even if you don't know what they are yet is, is understanding what's driving costs. And maybe you can make some adjustments in your design, your architecture along the way to better utilize the money that you can be spending to operate these kind of applications. And I think that's where the cloud really also gives another benefit.

All these managed services where you don't have to spend human hours setting up and infrastructure, but rather start getting a sense of how much things are gonna cost at a smaller scale. And then you can start seeing, okay, if I send this product, if I get this output, this is how much it's gonna cost me. And start to extrapolate on putting thresholds to be able to get there.

Let's talk a little bit about, um, I mentioned earlier some of my experiences when working with new technologies is it's fun to learn it yourself, but that isn't always the most effective way to get it done. I mean, you make, make a lot of the same mistakes that other people have already made. Some of the learnings you can gain from working with others.

Let's talk about the value of working with, uh, organizations like, uh, yourselves, people that have been down this path multiple times, maybe worked in different, uh, different scenarios, different kinds of applications, and how that can help accelerate, uh, maybe a new customer that you're working with to get not only their, uh, their project delivered on time or successfully, but accelerate their own learning. Absolutely. I mean, for, for me, I've been in the trenches, right?

I was a DevOps engineer before trying to figure out AWS and trying to be the most optimized way to it. Spend hours going through documentation, trying to figure out, and at the end of the day, I'm like, am I doing this right? And that's what I love about working with DOT and myself here, because we are a group of 300 engineers across glove that have expertise in everything around AWS.

And so we collaborate with one another to be able to get the right answer to the customer, understand the requirement and what the customer is trying to solve or trying to build for, and then being able to cut all the noise of what is not necessary and be able to hyper focus on like, this is the best architecture that you can build today that will grow with you, with your business and will keep the cost imbalance. And then that will just help the, the customer reinvest, reinvest all those savings back into the cloud and growing the business. So for us, do it, uh, is an extension of every team, every engineer, team, and every organization.

That's true. And I can add to add to that, like, um, I think, uh, being able to, like we, we are creating a distributed new database and, and we are an early stage where a lot of experimentation going on and now a lot of them are now going into production. So lot of like moving parts at this point.

And being able to have the help across from a WS and being kind of the up the database in the right way and eek out wherever we can, the cost and optimize the performance and cost for the customer is super critical. Because as, as we were talking before, the cost starts adding up pretty fast. And being able to go there and go under the hoods and find out like how you can go and do this particular optimization at the Kubernetes level four S3 would help you to get some of the cost advantages is critical.

We also do things like, uh, based on your QPS, like what is your expected query speeds and things like that, we can then go and find the cheapest way for you to go and run a certain machine configuration. So for example, we run a lot of our production workload for our customers on graviton and, and, uh, a lot of, when, when people talk about generic ai, they talk about cheap use. But I think we are seeing a lot of our actions on the low cost side of things because cost is a big factor.

And as more and more deployment starts happening, it'll become a bigger and bigger factor. What is the performance and cost goals are going to look like and how you deploy things in production, uh, becomes very, very important. Very good.

I would just add to that my, my general advice in this particular area, things are changing so fast. So many things are being introduced seems like on a daily basis, it's, it's tough to say what's the best technology you use? 'cause it's changing that rapidly.

I think you're more, more, uh, on, on the good track by picking the right partners to work with because the technology will change, our understanding will evolve. Our learning will certainly increase, um, as the technology changes too. So you wanna kind of pick the right horses to, to be a part of, to ride if you will, not just necessarily the best technology.

'cause they're gonna be a lot of really good things. Not just available today, but coming out real soon. Just, uh, just open up your browser the next day.

I think you'll probably see a new announcement. Well, let's, let's, uh, wrap up. Uh, I'd love to hear a little bit more about, so folks that are interested, wanting to find out more.

Like how can you, how can you help me on the journey that I'm, maybe I'm already down the, down the path on an app or I'm looking to get started and wanting to, uh, kind of accelerate my own learning. Um, what, what are some, what, what's a good way folks can engage you to find out more? Sure.

com. We have a wide range of services available. And just to point out, g accelerators my favorite program that we offer.

So hope you can check it out to get you started in the gen AI journey. io where we, there are a lot of gateway to a lot of ways. You can get a lot of information on documentation, slack forums.

We are open source. You can go to GitHub, play with the code. If you're of that type or you want to go and basically just get started, go to our VVI cloud.

Uh, you can get started and get 15 days, uh, for a free trial. You can, you can run your sandboxes there and provision your clusters and get started in a few minutes. Uh, so test it out, learn, Get started, right?

Jump in. It's a con. AI is a contact sport.

Yeah, get engaged. Well, thanks to you both, uh, Eduardo and, uh, Jovi's, been a pleasure talking with you today. And please folks, check out other respective websites, uh, thanks to both your companies to, uh, do it and, uh, ate.

We appreciate having you both on DevOps dialogues. And thanks to our listeners, we wish you the best on your journey and to generative AI and applications and beyond. Take care everybody.

Welcome back to Textron Unplugged. My name is Cassandra Jochen, and today we have Al Correct. Hey Cassandra, how are you doing?

Doing Well. Can I introduce yourself? Yes.

Uh, my name is Liron. I'm a developer advocate at sncc, which basically means I enjoy teaching developers about security, so application security topics and stuff like that, how to write secure code. Can you talk a little bit about how you do security education?

Uh, yes. So there's like several ways of doing that where you're like teaching developers at conferences or events or just having, you know, fun workshops. Um, I think specifically having fun is like a really good trade of doing it.

So for example, I would say if we are making it like a, like a fun, like giving them like a puzzle to solve to developers related to security, they actually like really connect to it. And so in security there's this, uh, game called, uh, like capture the flag, which comes obviously from like the gaming aspect of it. Uh, so you essentially try to like maybe hack a system and get the flag, get something in the system.

So I find that developers and like probably a lot of people as well, just like find out like a fun activity, like an escape room, trying to figure out to bunch of activities and trying to like access the system in different ways. So like that's one way of us doing security. Oh, you mentioned hacking.

Like normally we think of security as a good thing and hacking as a bad thing. So how are the two related? Um, a good thing or, and a bad thing For security?

I would say good security is often invisible. That's kinda like the curse of security because like, until you get hacked until like something bad happens, you really like, don't know, right? It's, it's kind of like invisible.

So until you get hacked, until there's like a data breach that does something goes wrong, like security is invisible, no one can like pay attention to it. So that aspect is maybe like the bad part of it. Uh, Like why do we wanna teach developers how to hack?

How to hack? I think putting them on, uh, on a space where they, where they know how to hack, like they understand how an attacker mindset works, uh, in those kind like CTF games, we can like give them an appreciation to why writing secure code. Why like using, you know, secure practices when they, when they write code, when they ship production apps is important because they can see how sometimes easy it is or how sometimes the mistakes are made in a simpler way, um, to like that they shouldn't have done that before.

So you really understand why security is important when you hack the code yourself. I think it gives you one perspective. Yes.

Are there any other ways that you teach security, which you find interesting? Um, well I think it, they're all somewhat related to gaming. So for example, um, there's like a cards game, so if you like, uh, I, I've been to one recently actually, uh, in Vegas in Defcon, uh, there was, uh, Git Guardian was a company was like sponsoring one of the booster.

And what they had is this, um, uh, secrets game. So you get a card, like a physical deck of cards, they all have secrets in them. You don't know what is an actual true secret and what is not.

And you have to like filter it pretty fast, like, you know, also like win and like get on the leaderboard. Um, and you can like sometimes make a mistake and you think something is a secret and really it's not or your way around. And so I think like those kind of like activities around like having a game, having, you know, something fun to do, it is, uh, is a, is a fun way of teaching it.

Um, for these games, like are they targeted at a certain age of developers? Um, no, definitely not. I think some may require some kind of like pre-experience, like understand the concept, but like secrets is like very shallow, like understanding, like I think any, any, any age, any experience level is, is relevant.

So do you think that even older developers can benefit from learning security? Oh yeah. I think everyone can benefit from knowing security, application security and information security.

Everything in and around it is, is just sometimes so far away from reach because like focused as developers just like, you know, get on the backlog, get back fixes, get features done, and all the other cross crosscutting concerns like security, like sometimes, uh, performance and testing and those kind of things get kinda like, you know, trickle down because they're not as important, but it is and everyone can win from it. Uh, can you think of any like specific security incidents where like, Uh, so many, uh, I dunno. The recent one was, uh, I think much in the news, uh, was the exit details.

That was an interesting, uh, kinda like crossroad of several things happening. So potentially like nation state actors, like a very major, uh, uh, incident in which, uh, an open source package, a library that is installed in like, you know, regular computer systems, uh, gets, uh, you know, gets someone else malicious actors, uh, to kinda like pay a key, a key role in there and potentially like slip in back doors and Trojans and that kinda like integrates a lot of things. Uh, both like how open source works, uh, the importance of, you know, libraries, uh, and like ownership for that.

Uh, you know, CICD practices, supply chain security, there's like a lot that goes into it. And that story alone could like, you know, pivot into different, uh, uh, education parts that we could better empower developers, like understand how to kind like practice, uh, in a, in a more safe way for, for the ecosystem. And have you always been in the security space?

Like you feel? I'm actually a, a developer gone into teaching developers about security because I kinda like care about security, but, uh, my, my background is totally not security. It is, um, has been being a developer myself.

So kinda like transitioning into, uh, enjoying security to a lot of, uh, to a good extent, uh, writing about it, you know, being able to like, you know, talk to my colleagues about security aspects and practices when you are building our apps. And, uh, I think like anyone can transition into a security space. So do you think things have been more fun since you transitioned to security?

Um, this job has been more fun in general doing that. Yes. Um, going back a little further, like how did, how did you get into technology?

Um, it's, I don't know if it's a, it's a regular path, but, you know, I've been, uh, my, my dad was, uh, was like in and around computers back then when I was a kid, um, which is his like 94 or five or so, um, to, he was really, uh, kind of like, you know, building app apps in like what would be an excel back then, uh, for his business. And I was just spending a lot of time, you know, in front of the computer doing a lot of things. And I think my, my surroundings were basically around that.

So I kinda like, got into computers and the moment I was kind of like learning, uh, uh, basic cubase editor thing, like building my own programs was like very satisfying that I could actually do that. So, um, that really got me hooked onto computers. And from there, uh, you know, I kinda like knew that all I wanted to do was basically work on, you know, software development all day.

So whatever the, uh, kind like the, the journey it took, um, school work experience, whatever that was just turning into, into tech has been kind of what I was, uh, envisioning it from childhood. I think it's a lot of fun. Like, computers let you create things.

Yes. I, I think that's the key aspect of it, being able to create something sometimes out of nothing. And at this conference, are you giving a talk?

Uh, yeah, just in and hour actually. So it's gonna be fun. I hope.

So. Do you wanna tell me a little bit about what your talk is about? What is this?

Um, it's, uh, it's much related to like, obviously security, fun games. Um, I am basically going to tell developers how even though these days there's a lot of hype around AI in, you know, generative AI chat, GPD, uh, coding assistance in their, uh, integrated development environments, then why having that is actually sometimes a security concern. Uh, so my talk is gonna have like live hacks and demos of the coding assistance, um, auto completing and suggesting vulnerable code that we could exploit.

And I think until developers like see that in practice, they sometimes don't even think about it. So that's gonna be, uh, that's gonna be my ambition to show them how bad things can happen as well. So they have like the mindsets of, you know, more responsible person when they work with ai.

I think that's an interesting topic 'cause AI is really popular and we're like training developers to use copilot. Yes, I would say, I dunno, training developers is one thing. I think we're kind of like going into a de facto state where we're having, so, so having a gen AI tool, like a coding assistant is not, I think, inherently bad.

We use them and that's, that's gonna be, I think, the future. That's, there's no denial for that, but I think the way that we make use of that technology and the amount of responsibility we apply to it is going to be a decisive factor in terms of what is the quality of the end products that we produce as developers at the end of the day. And I think that is something that needs a bit more kind of awareness and education onto what could potentially go wrong.

I think that's really important. I think so too. I think we've had a really good talk today, so thank you.

Thank you so much. 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.

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Hey everyone, it's Alan Shimmel for Text Drug, and you are watching the last great cloud transformation. This is a, for those who you are not familiar, this is a biweekly show that we do in partnership with our friends from CloudFlare. And we're talking about what's going on.

You know, the cloud has been around, well, it burst on the scene around 2005, 2006 actually, so it's been almost 20 years. And you know what, over that 20 years, a lot of organizations have moved at least some of their infrastructure to the cloud. There's a good chunk of people who haven't yet, and they may never, there's a good chunk of people who are still planning to, but you know, the initial move to the cloud was sort of these hyperscale kind of core cloud data centers, if you will.

And they're great and they're big and they take a lot of energy and they throw off a lot of heat. And we're hearing all these things about them, you know, now, especially with AI and GPUs. But we're also seeing gen two, gen three, even gen four migrations in the cloud, where, hey, it's not just putting it in the, in the one big hyperscaler data center or even on their network.

It's hybrid cloud, it's multi-cloud, it's cloud on the edge data located on endpoints in space, underwater in ice, you know, and everywhere in between. So that presents its own series of challenges, and that's the kind, that's what we explore here on this show. Um, I'm gonna go into today's topic in a second, but I first wanna introduce you to our panel for today.

First of all, joining us, I, he's actually up in Canada looking at his background today. Um, he is pretty well known in the security space, has a lot of experience in, in the kind of things I've just been talking about as well as currently serving on, uh, some csa uh, panels on SBO M and Dbo and other security related frontiers. Our friend Chris Blak.

Chris, how are you? Loving life. Good to see you, Alan.

Good to see everyone. Nice to have you here, um, joining Chris and I, uh, she's with, been with us before, if you've been watching this series, Emily Hancock of CloudFlare and Emily, if you, I I couldn't do the whole bio I gave there for Chris, so why not, if you know, why, why don't you introduce yourself to the audience? Sure, yeah.

Uh, it's great to be here again. I'm the Chief privacy Officer at CloudFlare. I also manage our legal product privacy and IP team and our privacy operations team.

And I've been with CloudFlare a little over six years now. Fantastic. And and that's a job.

Yeah. Um, God bless you. Uh, next up my co-host and he's the CTO here at, uh, tech Strong as well as CTA at FU of our sister company that we're merging with my friend Mitchell.

Ashley. Hey, Mitch, great to have you here. Always good to be talking about security with friends and let's have a great time.

Alrighty, so let's jump into it today, guys. Let me, I'm gonna read you from the abstract and, uh, we will, we'll jump from there, but, you know, how can organizations keep moving forward in the cloud while at the same time adhering to numerous, and I do underline the word numerous data sovereignty and data localization laws. They need a new kind of cloud, one that transforms the network, restoring visibility and control for their complex environments.

Um, beyond that though, let's dive into this, right? People, we are so concerned about supply chains, we're so concerned about where our data is being stored. Oh, we're starting to be really concerned.

And there are, and when I say there are numerous data sovereignty and local data localization laws, we're dealing at the federal level of the US state level, EU level nation states throughout. I mean, what's a poor company to do? I mean, you know, a single entity.

How, how the heck do you navigate this? What, you know, I I could see paralysis by analysis setting it. Emily, you, you know, your chief privacy officer at CloudFlare, CloudFlare carries a good chunk of the internet over, its over its wires, over its network.

How do you, how, what's the answer here? What, what comfort do you, can you give our audience? Yeah.

Well, I, I can give, I can give some comfort, um, but I will commiserate with the audience because it is an increasingly complex world out there. Um, we're seeing data sovereignty and localization, not just from data protection laws. And you mentioned like GDPR, um, there's laws in a bunch of countries, Japan, South Korea, to name a, a couple, um, that regulate the processing of personal data of their citizens outside of their countries.

And they don't always pro prohibit it, but a lot of times what they say is, if you're going to take that personal data outside of the com the country, you need to, you know, clear these hurdles or, or do these things or put these contractual provisions in place. So there's that. Then there's a whole slew of industry specific procurement requirements that may require things to be stored locally.

India banking regulations, for example, require some of that banking information to stay in India. And then we've got some certifications. Um, one of the big ones that's on the horizon, it's not there yet, but is the EUCS in Europe, which would say that if you're providing, uh, support, uh, critical infrastructure types of, um, services, or you're supporting critical infrastructure, some of that data has to be localized.

And, and then there's other countries, smattering of which, um, you know, Russia for example, have localization laws where they want data to stay local so their government can access that for whatever purposes they may want. Um, so yeah, there's a, a really complex, um, situation going on. And so what you have to look at when you're moving to the cloud, because there is this concern of, well, what is the cloud?

It's, you know, with, it's the cloud, it's, it's where is it? What, what does that mean? Um, you know, when it's on-prem, I know that it's kind of in my server in the basement or next door or wherever the servers are.

So when it's in the cloud, you need to be looking for providers that can help you store things in jurisdictions, if that's the requirement, or that can process data in certain jurisdictions, if that's the requirement. A lot of it is really dependent on what requirements you think you have to meet. And then you have to look for the cloud provider who can check those boxes based on the services they provide.

Yeah. Panel. Chris, Mitch, any, I, I have thoughts on this, but I'll let you guys go first.

Mitch, you gotta learn to take the mute off. Okay. Uh, oftentimes I'm, I'm both sides of this fence.

One, have as a provider, but also having been a provider, but also as a, uh, service or of data, if you will. And it seems like one of the things that you can look towards, and I know that, um, full disclosure tech strong is a customer of cloud flares. Uh, but there are, there are resources that, uh, working with companies can provide.

Like for example, the EU has their US data privacy framework. Now, security people were very used to frameworks, right? Process.

Here's how you document what you need to do and what you have to report on. Um, but that's, that's also I think a comforting factor is you don't have to go unwind all this stuff yourself. You have folks like Emily, providers that you can work with.

And I don't mean that as just a commercial for CloudFlare, but that's who, who we rely on one of the companies. And, uh, so that way you, you know, they're not there for legal advice, but they're there to help point you to the resources that you can use to try to unwind these things and figure out what you need to do in your situation. And I can think of, you know, two examples.

Let me see. You know, I mentioned on the show before, been public, uh, public knowledge working with, uh, Columbia in South Carolina, the, the nation of Columbia, you know, on long-term, uh, infrastructure plans and this level of, of visibility into cloud infrastructure. You know, saying that as you move forward at a certain point in ot, as we accept now in the operational technology world, people understand cloud is part of it.

But this was about six years ago. We didn't, you know, to have exactly these sit down conversations where, as you said, Emily, where you, you get the stakeholders together and say, you need to lead localize inside mind, jurisdiction, inside mind, country, this stuff, or, you know, cloud all you want, we're not doing it. And at that point they weren't.

And now they aren't. And y you know, and, and current, you know, this is how timely, this is it where we are right now. So in the Department of Homeland Security, there's a bunch of tiger teams working on supply chain issues, and we're just finishing a two month, uh, tiger team right now, uh, looking at ISACs as SBO M distributors.

So information sharing and analysis centers, you know, that, that share threat intelligence already and have the last, you know, 10, 20 years, uh, doing the same sort of things with SBOs and what some of the things both of you, uh, particularly Emily said, had me thinking about this as recently as today, working through how an ISAC can be there for the discovery of an SBO m but not actually for the access or transport. The other two phases of sharing because of localization and liability and all those sorts of things. And just today, I, as far as I know, having the best of the first conversations about how we, you know, a year and two from now, how we work on the conversations where maybe, you know, an ISAC specifically does want to hold some of those SBOs because of conditions anyways, without going into all that.

So, you know, my whole obsession with change over time leads to the kinds of things you said, Mitch, that about now I'd expect companies about like, like CloudFare to be offering about this sort of service. 'cause that's where we are. And if you need it, it's probably available.

And if it's not, you know, the VC world is waiting, start a company, now's the time. Yeah. I, I, I, I'll add something in here.

Emily, you, you, you touched on it, Chris and Mitch both, you know, there are some jurisdictions I think, um, you mentioned Russia, uh, that said, Hey, I want my data or data relating to my citizens kept in my sovereign cloud so that I can access it if I want to. There's the flip side of that, which is I want my citizens' data kept in my country's sovereign cloud, because if it's not on my sovereign cloud, I don't have access to it. And some other country will access my citizens' data.

And, and this is a market I I, I don't remember if CloudFlare was in, but I know some of the hyperscalers were certainly doing it, saying, Hey, we will, we won't crumble, right? From a government subpoena, right? I think it might have been Apple was involved in this too, right?

We're not gonna turn, we're not gonna give them access to your iPhone. We're not gonna give them access to your data. Now, you know, once you start getting into areas of localization like this, I don't know how a, a CloudFlare or an Apple or an Amazon or any of these folks are gonna withstand a full on government press here and, and the power of the courts and the full, you know, faith, full faith and credit of the United States of America, right?

Telling me, you gotta turn this over. I mean, what I mean, Emily, this is, this is kind of your, you know, you live this day to day, what do you do? Yeah.

Well, you're, you're now getting into the history of the cloud act a little bit, um, which is a lot. It's a, the, the very first big case on this was, uh, when Microsoft said, no, we're not going to turn over data because it's actually stored in Europe. And, um, long story short, that led to a cloud act.

And so governments are supposed to have these information sharing agreements put into place so they can share this national security information or law enforcement information. We're not there yet. The governments are, are still kind of fighting all that out.

In the meantime, um, this is a little bit of what we started seeing before the US EU data privacy framework was implemented. The courts in Europe, uh, the, the, uh, CJ EU specifically was saying, we're not sure that there can be any appropriate protections for EU data as long as it's held by a US company, even if it's held in the eu. We've started to see the tide turn on that a little bit.

There's been some smaller courts, and I think there was a smaller court in Germany, for example, that said, um, just because the US government might be able to get data does not necessarily mean they will. Because if you look to the agreements that these providers have in place, um, those agreements with their customers mean that they won't turn over data. For example, um, Kler signs a data processing addendum with all of our customers.

And in that data processing addendum, we specifically say that if there's a conflict of law that that exists, um, between what the US government might try to get from, uh, get, if they wanted to get data about one of our customers and our customer's data was related to people who are not US citizens. We specifically say that if there's that kind of conflict of law, we will push back. And we have a, um, transparency report that has warrant canaries that are kind of these promises of things that we've never done for, you know, in response to government requests.

Um, so we maintain those and, and that's the kind of thing that you would wanna look for from a cloud provider, is look at their record on those kinds of issues and what kind of government requests they've gotten, because all of the big companies have these transparency reports now, and you want to make sure that you're looking for that, and that's how you can balance this, this concern. The other thing that's really important is the data privacy framework. Part of the reason that it got implemented was because the Biden administration packed an executive order that really reframed how the US government surveillance agencies, um, and could, could exercise powers under the Foreign Intelligence Surveillance Act.

And those powers were what, um, have been an issue in all the rems, uh, jurist in the REMS debates. So, um, but, but I think going back to the providers, you wanna look at those that have the privacy guarantees, that have the commitments to push back on government requests and then maintain those commitments. Agreed.

And that sounds really reasonable to me. You know, think, you know, ba you know, as I said a minute ago, I would expect folks like you to be doing about those things about now. And I think you hit on a lot of the key points.

You know, this, you know, to be clear, the rules and legislation aren't done yet, you know, in any one country, you know, the US, Canada, or, uh, internationally, but we keep moving down in that direction. So I would agree that you want, you know, as your concerns justify this, having someone, you know, the, uh, the, what was the term policy canaries, I love those, right? Uh, the War and canaries, Warren Canaries, war And Canaries, right?

Yeah. You know, to somebody pay attention to this. 'cause if you follow down the path, you know, the Department of Energy and uh, and uh, DHS are pushing, uh, uh, cyber informed engineering.

And one of the key terms in there is radical transparency. I like those, those words. Exactly.

And I don't like the, the, the forces lining up behind that because we need to have this conversation. Radical transparency does not mean that everybody gets everything just like it did with vulnerabilities and threat intelligence. And now supply chain, it doesn't mean that everybody gets everything.

It means that you get the information you need in a time you can actually use it. So your time to transparency window doesn't collapse before you can do something with it. And today, yeah, I would like someone, uh, a corporation with all the motivations to stay in business and not get sued and so forth, to have taken those steps and have those, uh, those warrant canaries, you know, waiting on policy trips because no individual individuals and even well-resourced enterprises can't keep track of all that themselves yet.

You know, I, I think one of, one of the dual complexities of this, and I'm particularly interested in your perspective about this, Emily, is that there's the regulatory compliance side of this, which is always moving, you know, changing, evolving new things, popping up, things, taking a long time to get put in place. So there's a, a bit of uncertainty about it, but at the same time, we're deploying applications and data and infrastructure across the cloud, you know, at the edge in the core, wherever it might be. And, and that's, that's become more of a fluid thing.

It used to be that infrastructure was, I set it up and then we leave it and we run it for a while and I might change it. And there's a real specific procedure. Now you've got infrastructures code and APIs into the cloud and into, into your software infrastructure.

Um, I I, is there any guidance that we can provide people on as you create more of a dynamic environment, um, how to help you understand where you may need to reexamine your compliance when it comes to privacy and localization? Well, I mean, that's, that's a, that's a very big question, so I don't wanna give a lot of By that. You're, you had to ask you, I'm like, I need to add This.

Yeah, well, I mean, yeah, so like, that's one of the, you know, that's one of the advantages potentially of, um, of going with sort of a bigger company because for example, we've developed this global network, so we had to look at all the laws of all the countries where we have data centers where we're operating. And so we've done that work. Um, and if you don't have the bandwidth to, to do your own 200 country survey or, or whatever, um, to figure out what the applicable laws are, you know, we're global, so we're respecting the global laws and regulations, and we've developed these tools that help you figure out if you need to localize.

Um, and, and so, you know, the idea is that you shouldn't have to trade off your fears about not being able to comply when you move to the cloud, when you go from an on-prem solution to the cloud. Um, and, and so, you know, our whole goal is, and I imagine the other hyperscalers too, our whole goal though is to have the benefit of this global network with the improved performance, reduced latency, all these built in security features, all this kind of stuff, while you still can figure out how to comply with the regulations, we have a data localization service, for example, but allows you to restrict where data is inspected, um, where you keep your keys. So if you wanna keep your, your encryption keys out of a particular country, for example, uh, we can do that.

And then, um, we have some limited availability now, but we also have some ability to store logs in particular jurisdictions. And right now, that part is a little bit more limited to the EU and the us, but we're, we're building that capability out. And, um, you know, so, so I think if you are trying to expand and you're trying to move to the cloud, you wanna just, again, look for, uh, you know, a cloud provider that also has, has thought about these things, but also has done the certification work.

So for example, um, we've certified to ISO 2 7 7 0 1, which maps to the GDPR, um, and we have an EU cloud code of conduct certification and, and some others that show that we have looked at some of these particulars jurisdictions and have kind of checked the box to say, yes, we're, we're compliant in these areas. And, and I think that's the kind of thing you would wanna look for. Mm-hmm.

You can't do all that legal work yourself. Okay. Very good.

Very helpful. Um, I, I think another reason why you wanna, and this, this actually is a, I guess one of the advantages of going with a cloud flare, flare like thing is this is such a, a liquid situation. It's far from static.

And so portability, right? And that used to be one of the hallmarks, one of the foundational things of being in the cloud is elasticity and portability, right? I, today, it's here tomorrow, I wanna move it there.

It's fine. And you know, I I I think that may or may not be something CloudFlare can assist with. Emily, I'd love for you to weigh in, but we need, I mean, for organizations out there dealing with this issue, gotta recognize that whatever solution works for you today may not in fact work for you tomorrow or next week.

And we need Plan Bs and Cs on, on, in terms of portability to, to adapt to whatever the, you know, the latest and greatest are, uh, in terms in terms of regulations and compliance. So, uh, again, I'll throw it to em and Chris, Emily and Chris, I mean, Emily specifically CloudFlare, how, how do you guys help with that? And then Chris, what are you hearing on that issue, you know, through your contacts?

Yeah, sorry. So in terms of, I mean, if portability, if you wanna leave CloudFlare, which we hope you don't do, right? Um, we don't lock people in and there's not a lot that we store.

So right now there's, we have a couple storage products. Um, so it, you're able to move your data. We don't own it.

Chris, how do you facilitate people who have to move their data or around, Well, I'm just going to pile in here and praise of vendors, right? And Cloudflare's here, and you know, Emily has really good answers. So there's nothing I, I, I would, uh, see that would make me think that that's not a good choice.

But it reminds me of, uh, uh, 90 19 92, the first firewall, you know, sit standing there in a booth all by myself. And these two young folks came up with a great question, and it's like, Ooh, I know the answer to this one. I'm really proud of myself.

Then the second one, and they came up the third time and I said something that, that has lasted to this day, and it's true. Look, while you're really buying is a relationship with a bunch of people who should anything go wrong will live on anger and caffeine until it's fixed, right? So, what, you know, the value of working with, with good vendors who are reputable and manage to keep their customers is that by definition, they're worrying about this stuff all the time.

And I love, you know, geeking into the individual answer then how we do things and privacy and localization. But unless you wanna do that stuff for a living, at a certain point, you're gonna have to have appropriate trust in someone else, right? And there are, outside of the very large enterprises, very large enterprises, there's not a lot of this you're gonna be implementing yourself by definition, right?

Unless you are, you know, that cloud provider, you know. So you need to be appropriately aware of your policy environment, what legal risks do I have? If you don't have good lawyers, get them.

Um, get good technical people who can question the, the answers of, of vendors and providers and so forth. And, and ask the kind of questions that come up on shows like this. But, you know, this is, and this is where, where my radical transparency obsession comes in.

I, I think today you can do business with good vendors and, and suppliers and vice versa. But the contracts and the actual vehicles we use to protect IP, for example, are almost just a wave and a, uh, you know, we're just, we're hoping our employees understand them. I think we get better and better at, at automating these sort of things so we can get the kind of visibility.

So in this case, the vice president of it at a, at an enterprise can say to the, the, the C-suite and the, and the board. Yes. In fact, we've done our diligence and we got the right providers, and I can see what's going on.

Fair. You know, when it comes to portability too, Alan, is, there's portability within the cloud, within the service provider or providers that you use, right? And that's, it can be portability of the workload as well as portability of data.

Though data is much more difficult, consequential to move. We get into localization issues and things like that. But even within, you know, one network, one network provider, moving those workloads around to the edge, you know, edge workers that are doing different parts based on traffic that might be coming in, or security issues that are happening in the network.

I think more and more we're moving to you. It isn't set it on flexible and it'll allow automatically move all over by itself. But you're getting to a point where you could make parts of your application more portable, say to the edge or different locations at the edge, uh, versus things that need to run in a certain location or at the core.

And that's part of the architecture design of our applications. And that's where this portability cloud starts to overlap into software architecture, infrastructure design, things like that. They are, there aren't hard lines separating the two anymore because so much of The cloud is programmable.

That's exactly what I was getting at. But as part of that portability, do I run afoul of these sovereignty laws, right? Because you're going, I, I happen to know you're going to Barcelona next week for a conference.

What, what does that mean for my data? I mean, you know, yeah, I, I keep it stored here in the US right now as per our, you know, policy. But what are you going to take with you over there?

Or what are you, are you going to use, uh, let's say I'm on CloudFlare or some other network. Am I gonna temporarily have data on the edge over there and and what does that mean? Yeah, I mean, that, that's, that's kind of the, the benefit really of the cloud in some ways, right?

So your data is, if it, it's being processed at the edge, if you're using CloudFlare, your data is being processed at the edge of the network. And, um, you know, the sovereignty is, it's kind of funny. It can mean kind of a couple different things.

And, and one of the things is you want that data to be processed as close as possible to where the person is, whose data is being processed. Um, but the benefit of the global network is that if there's DDoS attack or some other kind of congestion on the network, you can route that data to where it needs to go. So what we've done with our data localization suite, for example, is, um, if you are a European citizen and you are traveling within Europe, um, your data will still only be inspected in Europe if that's turned on.

Now, if you go outside and you're still trying to access a customer and that customer's website is based in Germany, but you're a German person who now has gone to the United States for vacation, and you're trying to access your German bank account, and the German bank is behind CloudFlare and they have data localization suite turned on, you might experience a little latency because that bank has chosen that they still only want their data inspected in the EU as opposed to in the United States. Um, but as far as I can tell, the European data protection regulators aren't too worried about the scenario of their, their people going on vacation and, and accessing from outside. But it's really more when they're inside the EU and accessing the eu.

But that's, that's Europe and its own its own glory. Um, so different jurisdictions may have slightly different rules, but generally that traveling, what you really want is you wanna make sure that your customers, wherever they are in the world, can get their stuff or can access their stuff wherever it's stored as quickly as possible. And that's the beauty of, of processing, um, at the edge closest to where the individual is located.

My answer was gonna be, I was gonna ask to borrow your phone and your laptop when I go to Barcelona Allen. So Course it's your problem of, um, I think I left one there, but I mean, look, this is, this is real world and here's my other kind of fear and many ways. Our world today on the geopolitical front between wars and competitions and, and nationalist movements and sovereignty laws and all this stuff is getting more and war fragmented, more and more complex, more and more fraught with, with craziness, for lack of a better word.

There's gotta be a better way. How do, I mean, Chris, you work with the federal space at the federal level, and the US I know is big on alliances and you know, at least with their friends on, on trying to get these things done. I believe, you know, you represent CloudFlare here, chief Privacy Officer, is it pie in the sky?

And, and, you know, naivety to think that we can come to some sort of global kind of rules around this that'll make life easier. Chris, you're raising your head, Right? Yeah.

So take everything we've talked about right now, and this is the state of the world we're in, and we got here, you know, in, in many ways, you know, recent history last three years, five, seven, you, I, you talk about decades and for everything we talked about, I, I've got one word for you, starlink. Alright? You know now, so you're not inside that ju you're orbiting the planet rats.

What do I do there? Well, and, and as an interesting step beyond that, you know, Gwyn Shotwell, who's COO at, uh, SpaceX, um, is or was anyways, uh, also CEO of a company called Orbit's Edge. So imagine Starling satellites in tens of thousands that aren't just routers, they're servers.

So we actually get that, you know, everything we're dealing with, with data centers and clouds and soreen and jurisdictions. Now imagine those data centers are orbiting the earth in 17,000 miles an hour, and it's distributed across end space. And as with all things, either we get into this future and the, you know, society collapses and we all go back to, you know, eating, you know, deer or we work it out.

And I'm pretty sure, you know, the kinds of things, policy visibility and processing, the ability to say, my data is here, and I know this to a usable level of the word no and is subject to these policies that I don't have to hire a bunch of, you know, uh, interns to look up, but are are known all the time as that's accessed. I think that sort of thing is inevitable, and it is in the, in the, the working lifetimes of everyone here. You're an optimist, Emily, are you as optimistic?

Um, well, I don't think we're gonna get to a place where we're all, you know, back to, you know, hunting and gathering and, and shooting deer for dinner. We hope not. I think, I think we're, we're maybe away from that, hopefully.

Um, I mean, it is, it is tough though because we do see a lot of governments who are really pushing for keeping their data local because they view it as a national security concern, right? They, they want it, want it in a, in an on-prem data center. And yet we saw when Russia invaded Ukraine, that that actually is a really risky proposition too, because Ukraine had to suddenly put all its stuff on the cloud because they were worried that the Russians would take over the data center where the data was.

So there's real benefits to having things in the cloud. There's also real security and cyber intelligence benefits when, for CloudFlare, for example, we're looking at traffic, you know, we, we sit in front of about 20% of the internet and we're looking at global traffic, we're looking at cyber threats, DDoS attacks, you know, bots, all the things, all the bad things that can happen. And we are figuring out how to adapt and evolve to that and protect our customers.

And you can't do that if you're only looking at the risks that come from France or the risks that come from Germany or that come from Chile or, or whatever. So you have to have that global visibility, you have to have that interconnectedness. And I think we have seen a little movement, I let EUCS certification I was talking about, there was a storm contingent, um, led by France that was saying, no, no, no, this data has to be processed locally.

And we've actually seen the Europeans move away from that slightly. I don't think that means that countries are going to just drop these sovereignty requirements altogether, but I think there is a real recognition that as providers like us and others are developing, um, you know, ways to program how the network works for you as our customer, as we continue to evolve, that you're going to be able to fine tune, fine grained control what's happening to your data on our network while taking advantage of the global network and, and all the advantages, um, for cybersecurity that, that brings. So, and I, and I think the cybersecurity officials are really recognizing that some of the data protection officials are maybe a little slower to agree.

And then you've gotta deal with the, the geopolitics of, you know, countries trying to compete just economically and trying to boost their own economies. And one way to boost your own economy is to insist on a sovereign cloud that is, is gonna have data centers. So you've got a lot of competing tensions there, but I mean, we hope we can rise above that as, as the cloud that offers a lot of different, um, ways for our customers to kind of program what they need to meet their specific, specific legal obligations.

Yeah, I'm really glad Ggl, glad to hear you say that because, you know, in security we talk about compliance, but we also talk about guardrails, which is sort of the automation of adjusting those rules based on the context of the situation. Um, and that seems to be what we, what we need because that situ, 'cause we're in a fluid environment, it's gonna mean this, today it's gonna be have stronger or less, less strength and in its enforcement tomorrow. And so you need a way of being able to work in an environment where when those things change, I don't have to lift everything up and go decide how to re-architect it somewhere else.

I've got a way of, okay, that won't move there anymore because that's not meeting those conditions any longer. So those kind of guardrails sound extremely useful to me. Well, I mean, you think about it, you know, my, on the international Space Station right now, we have Americans and Russians and other nationalities creating and using data that I think is, is a, you know, a litmus or a little canary or something about the future.

We're moving into, you know, as we established spaces on the moon and more, more things in orbit. And you know, if you think starlink and Orbit's edge is hard to process in this context, figure it out. And we will have control of our data and there will be nation states and corporations with enormous stake in it and that that will know where their data is and who's touching it.

So, sovereignty, to your point, Emily, you know, may not always just mean literally on this two dimensional space inside my boundaries, but inside my sovereign space, and I know it at the nation state level, we will get there Always the optimist. Chris, I think let's try to end it on a high note though, Emily, first of all, thank you for coming on this edition of the last great Cloud transformation. My pleasure.

Thanks for having me. Yeah, no, and you know what, thank you for CloudFlare to CloudFlare convey our thanks, because quite frankly, there are only a handful of companies that have the scale to deal with these global, uh, regulations that we're all having to deal with now and, and, and increasingly slow. So, so it's good to see that someone's actually thinking about this and, and doing something.

Chris is always my friend, thank you for coming on. Keep up all that you do that a lot of it goes unsaid or unrecognized, but we'll recognize it here. Thank you.

And Mitchell, what, I'll give you the last word. You know, I was just thinking about, um, I remember reading the book about globalization and the concepts of, you know, everybody working in the global economies, and this is what it looks like, what globalization really looks like. There's a lot of lot to it as you get into the devils in the details.

And, um, you know, partnering with the right people can make all the difference for sure. As someone who's had to operate those things, it, it makes a meaningful difference. Great.

All right. We hope you've enjoyed this edition of our last great cloud transformation show here in partnership with, as I said, with our friends at CloudFlare. We'll be on in another, I guess, uh, two weeks after this with another one.

And we actually have another live event, which I really, if you like, talking about these kinds of things, these live events give you a chance to weigh in and ask questions, so please join us for that. But until then, this is Alan Shimel. Have a great day everyone.

Thanks for joining in. Hello, uh, my name's Mark Callahan and I am the CEO and founder of Cloud Canaries. 0 or two point x.

It kind of depends, but also what happens beyond that. And I think that's pretty important to bring up right now as we go through this, uh, evolution. Uh, also we're gonna touch upon what is driving obs observability as well.

So stay tuned. We're gonna go through, uh, some slides that I hope you find, um, very interesting and helpful and, and a little bit entertaining. So here we go.

0, predictive visibility, and that includes a, a list of, of, of items. It's continuous monitoring, SLA, uh, compliance actual and forecast data. So you can, you can work with, you can alarm notify on actual data or forecast data from API to workflow.

So observability, uh, it has to be more than just monitoring APIs. It really has to be monitoring the, the user experience, and that really includes workflows. Um, the second pillar is, uh, autonomous mitigation.

Now, what that really means is, you know, prevent fix, but do no harm without interventions of the op ops team. It also kind of means that you wanna be able to do this really fast, that something bad is happening, and so that's within 15 seconds, but it could be weeks or even months that, um, the system will basically self-heal. So you have to keep that in mind.

Um, when you have this type of mitigation, you want to be predictable in the outcome. Well, it, it, it has to, the way it has mitigated an issue, uh, or resolved it, uh, has to be kind of within the, I think, a set of, uh, domains that are, uh, are reasonable. Um, also kind of aligning actions with intent.

And this kind of goes back with predictable outcomes. Um, you can fix a problem with a hammer or, um, a sledgehammer and, and you wanna be able to, uh, do it. That kind of aligns with the intent.

0, we believe that cloud canaries is convergence of metrics, connecting the dots. The dots are from the cloud to your business. And once you've done that, you've opened the door for a set of very interesting opportunities.

Predict anything, forecast anything, add business insights. So connecting the dots between the business metrics, cloud metrics, convergence of those two, this is again, a really important thing. All these three pillars are, are, um, very important and will change the way DevOps functions and operates, and the requirements that are gonna be built upon, uh, um, that for, for the DevOps teams, and I think all really, really good.

3, but the idea is this will be set of increments that, um, that will, um, happen in observability. Um, I just didn't wanna confuse everyone. 0, as we mentioned, three pillars.

Uh, predictive visibility, autonomous mitigation, uh, convergence of the metrics, cloud metrics, business metrics. This will have an incredible impact on the business, very positive. And the cloud too and beyond.

At Cloud Canaries, we believe that AI models, and these are models built by machine, um, uh, learning tools that are being supplied by many different vendors that create, uh, models. Basically, for the most part, neural networks will become the solution. In other words, you're gonna have one platform and many different models is the models are gonna contain all the intelligence that you need to, to, um, solve that a particular problem.

And because, and because it's not a, you don't have as many platforms and, and models provide the solution, you're gonna have many solutions because they're gonna be much easier to, um, to deploy, to manage, and you're gonna be able to roll it out to your entire, uh, all of your infrastructure, which again, is going to be amazing. It's gonna be incredibly helpful for the cloud and the business. So once again, beyond, it's gonna be, you're gonna be buying or renting or subscribing to models, not platforms.

Think about that. It is gonna happen. It's happening right now in some areas.

0 and beyond? So let's take a look at that. Um, developer leaning and people say, moving to the left, I, I don't like to use that term, but it's gonna be, um, developer leaning.

DevOps is gonna be, um, leaning to developers to do a couple things. Building observability in code. There's some things that you just have to do.

You have to do it in the code, and you can't just add on a little library to do it. And, and the, the, the, the clearest example is workloads, um, and workflows. So, um, how does a, how does a workflow, um, work?

It, it's a, you know, it's a, a process of going through, uh, several different stages within, uh, kind of a user experience. And you create this, this workflow. And, um, you might, it might be a series of workloads, don't really care, but, uh, observability is gonna have to be built in to really capture that, um, that information.

Also leveraging CI and cd, continuous integration and continuous delivery with ai. So again, I think that's gonna work with observability to actually make a better, more, uh, uh, better applications, but also better models. And we'll talk about that a little bit later on.

Second pillar, cloud gatekeepers. It's not gonna be just an ops, it's going to expand and DevOps are going to be the gatekeepers. If there's something that's going to happen in the cloud, you're gonna be talking to DevOps, the DevOps organization.

I call 'em the guardians of the cloud. And that's really what it is. What, you know, DevOps will be focused on predictive outcomes.

Your reaction time is going to, you're not gonna have that much re uh, the re reaction time that you're gonna be investing is gonna be fairly small because it's working, everything's working. So predictive outcomes and DevOps is gonna own p and l, new profits and losses, it's gonna own the performance. And SLA targets, again, it's more of a gatekeeper, guardians of the cloud kind of role versus an operations or developer security.

It's all kind of all the above within an organization. The third PO pillar is, is cloud analyst. And, and maybe there's a better term for it, but it's really kind of making sure that, um, the cloud is aligning, uh, to the business intent and that the RS uh, is, uh, is understood.

So aligning how the cloud is being operated, what you're doing to the end in mind, which is making customers happy. Uh, measuring success, predicting success. It's important also on the downside is, you know, measuring, uh, when things go wrong are when there's a failure and learning from that.

Um, and those failures might have, might have nothing to do with, uh, the cloud. It's really driven by the business. 0 and what's gonna come beyond that.

And I think what's coming beyond that is gonna be very, very exciting too. So let's take a quick look at that. 0, developer leaning, it's in the code ban, uh, cloud gatekeeper, um, guardians of the cloud.

Uh, the scope of, uh, DevOps grows roles, grow cloud analysts, making sure that the business and and activities in the cloud are aligned and using the, the massive data through, um, understanding the flows of, of, uh, solutions or workloads of solutions, uh, uh, or creating models that can help the business, um, you know, take care of customers and have a fair, you know, profit. So beyond is, um, kind of extension of that more diverse specialized roles for DevOps. And we're seeing this to some extent today.

We see this in, uh, security. We see this in infrastructure, uh, solutions. This is gonna grow.

Uh, the key will be to always make sure that the DevOps continues as a community. So that's really what it has been. Um, and, and that's gonna be the key to its long-term success.

2. You can, you can decide when we, when we accomplish that. Let's take a look at, uh, the next slide.

So I've tried to map this into like, what's the average time that a DevOps individual will be spending working, and what will they be doing? Um, and, and I hope you, the, the big takeaway, I hope you see here and, and it's, you know, pretty obvious is rea the time that you have to spend on reaction, there's a disaster. There's two disasters, there's three disasters will be reduced.

It has to get reduced. That's really the only way to allocate the time to do the other tasks that will have immense value for the business and the customer. 0, uh, the time that you have to spend on reacting to some emergency is gonna go down.

And the reason for that are observability. Being building the code, um, we're able to mitigate, um, the problems before they even happen through forecasting. We're able to gen through our forecast due prevention, understand what the problems are, and, and deal with them in a very reasonable time.

And that will impact the amount of time that DevOps development, operations security will actually have to be reacting to something versus planning to, you know, solve problems that are, that are six months or two years, uh, away. Um, beyond that, i, I, I think it will, um, it will continue. There might be a little bit less development aspect because observability has been built into the code, and that's now a standard process.

There should be more economic to actually do. The big takeaway in the beyond is the addition of business insight. As DevOps teams continue to build their mod forecast models, um, you're gonna be able to generate business insight that, you know, the business side will we'll be very excited.

It'll be, it'll be, it'll be their secret sauce for the business, the how to take care of the take, how to take care of their customers. So you're gonna see a, a, a massive amount of that because DevOps are gonna be managing the, the models through AIOps or however you wanna define it to me, is still part of DevOps. Um, so that's the big takeaway with beyond the ability to really communicate value and insight to the business.

0, but on a smaller scale. So then the question is, well, what's driving OB observability? 0, right?

It boils down to limited ROI talk to anyone, well, at least, and at least I have in the DevOps world, and you, you get the end, the end in mind that businesses aren't getting their, the RROI that they expect it. Some of it has to do with solutions being overpriced, consumption based. So you can't, you know, do a full rollout as I mentioned here.

Um, these, the, the company can't afford it. Cloud costs, they really dropped when you moved from the, the, your data center to, to the cloud, but now they're going back up cost to support overlapping vendors. You don't necessarily need that.

If, if the, uh, if the model that you're generating is the solution, you don't need as many platforms. The number of vendors and, and consult consultants will go down at, you know, um, because you're just not gonna need them. Uh, regulatory rules that's continued, that will continue, uh, to, to increase.

Um, especially if you're, uh, a global company, whether you're in EU or you know, the US or, you know, wherever you, you, you're, you're positioned, um, the rules are gonna increase. We mentioned, um, organizations can afford to do a full rollout. And again, I, you know, it's, it's just amazing when, when I go and speak with, uh, DevOps, uh, you know, VPs of DevOps, uh, operations and, and they say, well, we only actually rolled our observability solution out to like 20% of our infrastructure because we really can't afford it.

Um, and the turbo insights not delivered fast enough. Ultimately, that is the, the big driver for, uh, a limited ROI. 0, AMV odd.

So let's go to the next, next slide here. So what are the drivers? Um, um, so there's a, there's software drivers.

Biggest one is Open Telemetry tool is tool slash vendor agnostic. High adoption, it's amazing how many of software vendors have adopted open, uh, telemetry. It's all standard standardized.

It might not be necessarily be perfect, but vendors are all supporting it. And again, if there is an issue, it can be resolved pretty, pretty quickly because it's standardized. Second pillar code first.

This is having a dramatic impact on the quality of software that's being generated. Um, you know, both as a feature, as features, but also as within a steady state, uh, where software is released and, and running. Um, you'll be able to provide better monitoring, observability, um, security, uh, in, in, you know, all the time, uh, with, uh, continuous, um, observability.

So building code built in code, not a add in, and it's standardized code first. The last one, the last pillar is, uh, can you continuous integration and continuous, um, deployment in code. That's pretty, pretty typical right now.

CICD, um, but also in the forecast models that you're creating. Um, there's no reason why we shouldn't be doing that. We should always be making our models, um, smarter.

Um, and we can do that now. We'll talk about that in a few, few, few moments. Better code and models, better user experience.

Ultimately that is our goal. Observability technology drivers model ready data at Cloud Canaries, since we collect the data, we know how to easily, uh, use that data in our models that we, we create no matter who, whose tools the customer is actually using. Pretty cool, um, machine learning AI tools, again, there's a lot of different flavors or shapes, I guess shapes.

Um, all of them have certain pros and cons. It's should be up to the, the user to actually determine what they want to use. And the last piece here is compute, because our theory of cloud canaries is that you don't necessarily need, uh, you know, a thousand data scientists.

What you need is a lot of compute and generalized, um, uh, uh, tools that, that generate, generate models. You'll get your best performance bang for the buck that way that's happening right now. With that, we can, we can take, um, we can allow continuous model, uh, validation.

And this is the key to, to, uh, one part of the puzzle because we're constantly collecting data, workload data telemetry. We're using that data to build a model. We're using the models to generate forecasts.

Eventually in time we generate actual data that can be compared to forecast data and we can say, Hey, it's right arm, uh, uh, uh, or No, it's not. And then tune that model, uh, and, and take the error out or as much as you can until you get, you know, more actual data or you get more forecast. So it's a continuous loop, and this is gonna have a massive impact on the whole idea of models.

Become the solution, not the platform. Think about that one platform. Many solutions 'cause of many models, what happens?

Solutions get smarter, better, faster. It's pretty simple. You know, collect, validate, build your model with, uh, models, with, uh, tools that you want to use, forecast, predict, add, insight, notify and act.

And act is obviously the, a critical piece here for our customers. Beyond solutions, as I, as I said, you're gonna have more models, fewer platforms. You know, our claim at Cloud Canaries is that the, the, the number of solutions are going to become very, very numerous because you can be able to build models, you'll be able to use the same infrastructure, same, uh, platforms to use different models for different problems.

And from digital experience, contract negotiation, you name it, we can have a model for that. Costs are gonna come down, support for those. That platform is gonna cut, is, is going to be low.

And you're gonna build a set of consultants who are focused on a couple platforms and, um, validating models. So it's, it's gonna be, it's, I think it's, it's uh, it's a paradigm shift and, uh, it's going to, uh, make, uh, better products, happier customers and, and, and more time for us to, uh, think of new, new ways to innovate. That concludes our little presentation.

I wanna thank you very, very much for listening. Uh, if you wanna learn more, you know, just go to Cloud Canaries. Thank you very much.

Have a great day.