Techstrong TV January 21, 2026

Hey, everyone. Welcome back here to Text Drug tv. I'm really happy to have Olivier Blanchard or Blanchard if you're from the US and you've wanted just low lowbrow it.

But Olivier is an analyst with, from, uh, with Futurum. He, I've interviewed him before and he, he writes a lot. com.

Covers a lot. He's gonna tell us about it. Olivier, welcome to Text Drunk TV.

Again, it's great to have you on. Hey, thanks for having me. It's always good to be on.

Thank you, Olivier. For those folks who may not be familiar with your area of expertise and kind of, you know, your beat that you cover, give them a little insight. Yeah, so I'm a research director with, uh, the Futurum Group, uh, as you mentioned.

And my primary focus is AI devices. So basically any kind of physical ai, any manifestation of AI that's not in the cloud. So a smart ring, a smartwatch, a PC that has AI built in, uh, AI enabled phones, all the way up to robots and smart speakers, smart TVs, smart cars.

Um, anything that's not bolted down, uh, in a data center that has a, some kind of AI capability is my focus. So it's a very wide and broad portfolio, uh, very interesting one, uh, but one that also sort of like, uh, helps map how some of the same players keep turning up in the, in, in different places, uh, especially on the semiconductor Omicron. Got it.

Um, let's, well, first of all, I, I mentioned the Futurum group is where people can follow you and your research and notes and so forth, and, um, I wanna make sure people get that before we even go any further. But Olivier, I wanted to speak to you today about a company called, I believe it's Media Tech, right? That's M-E-D-I-A-T-E-K.

So I, I have to admit, this was a, a company that was new to me. That's Fair. That doesn't mean anything, right?

'cause God knows, I don't know everything. But for our audience out here that may not be familiar with Media Tech, how would, how would you describe it to them? Media tech's a really interesting company.

Uh, they're definitely not a household name. They're a little bit the way Qualcomm used to be 10 years ago, uh, where everybody get, might've heard the name or a lot of people might've heard the name, but it didn't quite know what it did. Um, media Tech is, doesn't even have that kind of level of, of name recognition, at least not in the United States.

I think overseas, uh, on global markets, it's, it's definitely more, more visible. Um, but essentially it's, it's a company that, uh, is that kind of started in, in the, or made its bones in the, the mobile world market. Um mm-hmm.

Essentially it's, it's the world's largest provider of smartphone chip sets, which you might not have known, uh, at least by volume and, and definitely a, a global leader in, in cross platform semiconductors. So they can be found, found in a lot of places. Um, I would say that, that the majority of their revenue, um, is, is from mobile.

I think it's just over 50%, 52%, 53% of revenue is mobile. Uh, and then Smart Edge platform. So basically everything else is about 43% of their revenue.

Um, but they have one particularity, um, which is that at least up until recently, they were essentially a, a sort of like, how do you, how, what's a a polite way of saying it, they were sort of like the low to mid range semiconductor d uh, um, provider for mobile handsets, um, in terms of price points, right? They were sort of like, your, your budget phones, your Mass market, Your mass market phones, right? Um, two Qualcomms sort of like super premium stuff.

And in recent years, what we've seen them is actually get out, well, they're not getting out of it, but they've expanded into that premium segment, but they're market Coming up market. Yep. Yeah.

Yeah. They're coming up with some really good stuff. Um, again, not a huge footprint in the United States, but overseas.

Internationally, uh, they definitely make their mark and they're, they're a company to watch. You know, that's a very common sort of strategy. You see it in a lot of maturing markets.

Olivier, you have, you have folks who come, let's say, from a bottom level up that are aimed at an S-M-B-S-M-E kind of markets, if you will. And then you have the big enterprise guys who want to come down market. You have the down market guys who wanna come up market and somewhere they meet in the middle.

Right? And, and, uh, and that's where you determine market shares. So it sounds like media tech though, is ascending in, into upmarket, and, and look, it's, a lot of people tell you it's a lot harder going up than it is going down too sometimes, right?

It, it can be, especially when those markets are obviously a premium is smaller than mass market. Yeah. So they can be the number one, you know, global, uh, smartphone, SOC shipments company.

They can be 10th largest global semiconductor company by revenue in the world, but still struggle to, uh, to penetrate those markets that are very mature, uh, and, and very well protected. I'm reminded of a conversation I had with a very good friend of mine, uh, Byron Nicoletti. Byron is the CEO founder of a company, company called People Cert, one of the largest training providers in the world.

They own and operate the IL uh, it, you know, service management language. They also have DevOps Institute and some others. But Byron always told me in a business, you like a business that has a very wide base Yeah.

Rather than just the top of the pyramid. He said, I'll always take the base of the pyramid versus the top of the pyramid. And there's, you know, that sounds sort of like what we're dealing with here.

Perhaps there is a base of the pyramid versus the top of the pyramid. Um, it's nice to be up and down the whole pyramid though, isn't it? It It is.

If you can Yeah. Ask Apple if you, right. Yeah, yeah, exactly.

Exactly. Who by the way, announced they're partnering with Google for, for Siri now, so it's gonna Be, yes. That's a good thing.

Gemini, under the hood, under the covers. I think I, I think a a a Google powered or Gemini powered Siri is, is good for everyone. Absolutely.

Well, my security friends may have something to say, but it's another story. Olivier, let's go back to Media Tech though. Yeah.

Um, they recently had some news. I know you're working on some new research notes and so forth, but, um, without letting the cat out of the bag of anything we're not supposed to talk about, what can you tell us about new news over there? Right.

Yeah, so I'll, I'll leave the, the top secret stuff. Top secret. Uh, but one thing I, I would, um, um, I would like to focus on is Media Tech's partnership, increasing growing sort of connective tissue with Nvidia.

Um, and I wanted to dispel any rumors because they've been working on so many things together that, Hey, is Nvidia gonna acquire media Tech? Is is like, is there something going on? As far as I know, no, and I've asked the the question repeatedly.

Uh, the answer is always categorically no. And it's not no wink wink. It's like, no, no.

It's like the, the two companies, uh, are very happy to be independent and doing their own thing, but there are affinities there, there, there are things that Media Tech does very well that Nvidia doesn't, and there are things that Nvidia does very well that Media Tech doesn't. And so we've seen them partner on a number of, uh, of projects, uh, lately. And so the, the wine that's especially dear to my heart, because I spent the last year and a half focusing so much of my energy on the A IPC, um, uh, segments, and how, uh, even though it's been a little bit disappointing in terms of use cases, the, the PCs being, uh, empowered by AI capabilities, that's, that's definitely a big inflection point for the PC and personal computing.

Um, even though it, it might take a few years to get us to a point where PCs truly operates like AI enabled machines. But anyway, um, media Tech and Nvidia have been, have been working together on this. And one of the things that that came out last year that I think was a bit of a game changer for the A IPC segments, um, was NVIDIA's DJX Spark, uh, platform, which is basically a, um, a Blackwell super chipp powered desktop that looks like a Mac mini.

It just looks like a, a little pallet, right? Um, you can put it in a bag, it's small enough, not fit in your pocket, but fit in a bag. You put it on your, on your desktop, and, and you have this little AI supercomputer that has a GB 10, um, NVIDIA chip in it.

But the, I would say at least 50% of the board looks like media tech ip. And so this, this combination of NVIDIA and Media Tech shows how these two companies can, uh, collaborate together to bring something very unique and very important to that space. And I'm already seeing them also partnering, uh, in the automotive market, which isn't, um, as big for semiconductors as you would think it should be, especially since intelligence is like getting into all these vehicles all the time.

And they could do more things, whether it's automatic or self-driving, or just all of the sensors and sensor intelligence and cockpit intelligence with, um, assistance and agents making their way to the cockpit where you can talk to your car and it talks back. Um, it's, it's still a, a very nascent market, but it's growing very quickly. And, uh, media Tech has a platform for automotive called Immensity Auto, uh, that they're working with in conjunction with Nvidia.

And so, again, you see the power and the market power of Nvidia, uh, in its name recognition, sort of attaching itself to media tech, which is kind of not very popular, but this workhorse that can scale really well across all of these different areas. Um, and one of the particularities of, of media tech and where it matters with Nvidia is Media Tech is primarily just an ARM-based architecture semiconductor company. They use arm, uh, and so does Nvidia.

And so there's this natural sort of architectural affinity there where, um, arm on arm arm with arm, uh, low power, high performance works really well for these types of Edge AI applications like automotive, pc, smartphones, uh, IOT and, and even, um, as, as it's becoming like the, the theme of the year after cs, uh, robotics, robotics is gonna beat this year. So there, I haven't seen any major Announce Physical ai. Physical ai.

Yeah. I haven't seen a, a lot of major announcements from media tech regarding physical AI robots. Uh, but I'm, I'm sure that will come, uh, very shortly.

There's, there's no way it doesn't. Excellent. Olivier, thanks for giving us this sneak peek behind the curtain, if you will, on Media Tech and bringing it to our audience's attention.

We appreciate it. Keep up the great work. As I mentioned, if you wanna read Olivier's coverage of media tech as well as all the other great, I mean, his whole AI empowered device thing, what, what a great time to be in that space, right?

com, check it out there, Olivier. Thank you. Hope to see you back here soon on Techstrong tv.

Thank You. Thank you. That complexity takes us to, okay, you've got some great ideas about where you want to take the network.

Um, you made some decisions about some products. What did you, what did you select going forward, and what did, what did this do for you in light of the requirements that you all saw? Okay, so, so our requirements was to have a modern design implementation operation cycle, you know?

Mm-hmm. So we, we need, we use NetOps techniques mm-hmm. Where we, where we're treating our network as code, right.

And, and when treating the network really as code, um, I mean network as code, the, the concept itself has been there for a long while. Sure. But it's been always, like somebody will tell you, write your network as code, and let me write a translator in the middle to enable this network as code to be understood by, by by my tool.

And then, and then you have the tool understanding this network as code, but it never tells you if it really implemented your, your code or not. Sure. It says it's accepted, says, so we had this idea of that our network, uh, our network as code should, if it's really network as code, then let's treat it as really code, let's treat it and, and put it through a software development cycle.

Mm-hmm. Like, we're, we're gonna be more like software engineers treating our network, uh, as really software where we have a production environment. We're, we're taking, we're we're putting it through a virgining system like Git, uh, taking branches, uh, implementing changes in the branch, testing them, feeding back into, into production.

So really this was our vision to have, you know, a NetOps a a process or, or, or architecture that treats network as code. But we really wanted to, to have a tool, a tool that really understands the code by as code and not translated onto something else. Sure.

Why we wanted to feed, we wanted the tool to feed us back information about our real code. Our real code is our real code. What is implemented?

Are you tracking it? We, to have this feedback loop, we didn't want any translators in the middle that will lose information, that will information that will lose this, this feedback, pure feedback loop. So, and, and that's where we found our, uh, really this, our, our new tools have enabled us to do this.

Our new configuration management platform enables us to do this, treat our net network as code, keep it as code, and feed us back to information about our code, how it's implemented, any deviations, any, any, any drifts, things like that. And, and just to be really clear, you know, you, you went down the path of going with Nokia's Sr. Linux as a switch in router operating system and event driven automation, or EDA as the, the management and automation platform.

And there's some really interesting connections there from a Kubernetes perspective, from getting away from having to go testing with physical equipment in the lab. Um, I know you can speak to at least both those issues. What, what did you get out of that, you know, out of, um, Sr.

Linux with EDA to drive that forward? Okay, so, so IDA enabled us to, to build, I was talking about the, the, you know, the software development cycle mm-hmm. Where you have a production environment and you have a development environment.

Uh, we really wanted our, our development environment to be a copy of our production, but we don't have to, we don't want to build a lab mm-hmm. That is a development environment. I mean, we've got data centers with hundreds of, of switches in there.

Sure. And we didn't want to go and build a lab or build a scale down of the lab. Sure.

A scale down can, can sometimes work, but sometimes in cases where we do migrations, for example, right. We, we really need every, no, we need to understand every node. So, so either provide us with this idea of a digital twin mm-hmm.

A true digital twin running the same code, same everything. And the nice thing about it is the intent, the intent, the, the network intent that I'm feeding into the production environment. Mm-hmm.

I can feed the same network intent to my digital twin. No, no changes at all in anything. I don't have to, to mess it around or tell it to, or massage it to fit with this digital twin.

Sure. Just the same code. Put it in the digital digital twin, and then I can play with that code.

I can, I can make changes in that code. I can experiment, I can run traffic on, on this digital environment that is a true replica of production. And once I'm happy, then I can merge into production knowing that my design works well.

So I can, I can test things that, you know, routing to low balancers to firewalls, uh, different server configurations. I can do that in the digital environment Yep. And, and feed it back into production.

So, so, so either this idea of a digital twin was very powerful, very, very powerful. And, and especially in migrations, it was, Yeah. You're using the same control plane that you're using on the physical switches and you're feeding it the same configs and to be able to play what if in the digital twin.

And that's a, that's a huge enabler, right? And, uh, And the nodes are running the same code as the production node, as the real SR Linux nodes. Yep.

And you could even change that rev of code. Right. And still see those changes reflected in the digital twin.

Yes. Yep. Hey guys, thanks for the throw.

We're here with Phil Menez, who is vice president of Go to Market Execution for Vast Data. And we're talking about this flash memory crunch that we're all starting to see. And one of the forces behind all that, Phil, welcome to show.

Awesome. Thanks for having me. So what is going on here?

I mean, I think we all kind of generally understand that AI is somehow at the core of this whole thing, but walk us through how does this crunch occur and how is it manifesting itself? And more importantly, is it ever gonna go away? Yeah.

Some great questions. So I think there's, there's obviously a handful of things driving this AI consumption is, is a big one. But there's also the fact that we had, uh, an HDD shortage as well.

So we saw a lot of customers looking to high capacity flash drives to address that shortage. And I think that drove a lot of great modernization. Customers are getting savvier to the idea that they want to be able to have that data on fast access.

So that's a piece of it. Uh, and now we're seeing more and more things in the AI world just driving consumption, right? We just had, uh, jenssen's big announcement around the idea that we need to start storing more of this context on flash.

That's driving a ton of consumption as well. And they do have the traditional drivers of growth that media is getting much more rich. And we are seeing customers get more savvy, savvy to the idea that there is more value in data and they need to start being more creative about what they capture.

So it's really coming at the industry from a lot of different angles. And then the final question is, how long is this going to last? And we think it's going to be something that hangs around for the next 12 to 18 months that customers are gonna have to deal with.

Just, it's gonna be difficult to get their hands on SSDs and disks in general. Do we need just more manufacturing capacity? Is that what we're waiting for?

Or is some sort of part of the demand equation gonna change? I think we do need more manufacturing capacity. Ultimately, we're gonna get into a, a place of the world where, I don't expect this to slow, but I also think that there's a lot of opportunities to drive more modern technologies, right?

There's still a lot of investment in technical debt where customers are bringing capacity into their environments and just not getting the full value of that capacity in the way they could if they started leveraging more modern platforms like Vast. Mm-hmm. You kind of alluded to what Jensen Wong was talking about with context.

Explain how that manifests itself. Am I just really caching the prompts more aggressively on my SSDs? Or what kind of data is going into that motion to give people the, at least something that feels like a real time AI experience?

Right, Right. So what happens is I ask a question to a, a model, and it looks at what I've asked. Maybe I'm adding a document, right?

We're seeing more of that. I'm adding a document, maybe it's a video, right? An audio file.

And I wanna be able to interact with AI around that document. There's a lot of context that gets stored there. And ultimately, you either have to store that context on memory.

I have to potentially recalculate a lot, which is really expensive in GPU cycles. But to your point, also creates this lag in user experience that can be very annoying. So now the idea is, can we be more intelligent about storing that context on flash to find the best of both worlds, right?

Where I have that context, I can have these long conversations, I can, uh, store context on things like longer documents, and at the same time, I don't have to recalculate very, very often, right? So that improves both user experience and it reduces the cost of ai, right? Customers can't just keep, just keep throwing at this problem in a way when it's really to your point, a memory or storage problem.

Mm-hmm. Well, some organizations start to hoard some of these types of drives because they'll see this, um, situation evolving and they'll exacerbate it even further by going out and buying more stuff earlier than they need it, and therefore making it more difficult for everybody else. Absolutely.

We are seeing it happen in real time was vast. Uh, right now, I think what we saw was that the, the really big buyers, right? You think about the AI labs, the cloud, some of these, uh, companies that probably have more of a direct line relationship to the actual suppliers and manufacturers than maybe a traditional enterprise customer, have seen this problem coming for a, a few months now and have been bracing for it.

Now we're seeing more and more customers become savvy to the idea that they are gonna run outta capacity that prices are growing. So we're already seeing that boon, right? I think what's available is going to be chewed up really quickly.

Mm-hmm. And is the memory that's used to drive these things are also in short supply as well? It seems like, you know, I'm hearing reports where, uh, out of consumer technologies 'cause they're just making too much money and there's too much demand on the server side from the enterprise and the AI folks.

So is the whole supply chain kind of changing A hundred percent, right? The raw materials are, are in limited supply, right? I think we're seeing now, um, you know, there's different ways, right?

If I'm using TLC flash for the same raw materials, I'm storing less data than I am at QLC flash, right? And then obviously that business problem comes into play. So it's a matter of how these technologies are being used, legacy technologies, kind of getting less for the raw materials, legacy technologies not being as efficient.

So it's really across the board. And I think there's, there's going to be a lag in how we can pump supply back into the market to catch up. Hmm.

Are you at all concerned maybe that we'll get to the point where somebody will build an application and then somebody in it will come to them and say, we have to postpone the deployment of that 'cause we don't have enough infrastructure to support it and we'll put you on the list and there'll be this bigger backlog? Absolutely. I think customers are gonna have to make tough choices for the next 12 to 18 months about where they invest.

Uh, I think depending on the technologies, customers are gonna have those tough conversations around what data do we really need to keep? Can we delete stuff? Can we park it somewhere else?

Uh, what's active? I think customers are really gonna have to be creative. Um, and, and just looking at what they have available, what their options are for what they've got on hand.

And to your point, what projects are priorities? Is this therefore gonna force some, uh, better shall we say good housekeeping around the whole data management motion? Because I think part of our problem is, you know, we have a lot of data.

We store a lot of data, but we don't always manage it so well. I think so in general, there's going to be more scrutiny, right? On policies and keeping data.

But I think in the world that we're living in, and I've been in storage for a while, right? We've all kind of known eventually there's a lot of data. I don't wanna let it go because there's value in it.

We haven't been able to tap into that value. Now that opportunity's here with ai and then it's kind of brutal that as AI is really becoming something that feels production ready, that now we don't have the capacity to drive it. That, you know, our opinion is we would really like customers to take a first step at can we more effectively use what we've got and be more careful about what we're doing with the capacity that we bring in with that precious amount of capacity customers will get their hands on.

Can we be more intelligent around how we're using that as opposed to deleting data that's gonna have value in a year? 'cause that's gonna feel really tough for a lot of customers if, if that ultimately becomes the case. And even some of the tools they might need to understand what data has value, kind of needs access to that data before we can make those decisions, right?

So it, it's a very tricky time that this is all converging. At the same time, When we need to get better at utilizing our storage systems because of this issue, maybe we'll need to figure out how to make it easier to share those SSD drives across multiple applications. 'cause sometimes I feel like when it comes to IT infrastructure, you know, we're all out that day when we were taught that learning the share is a good thing.

Uh, I think absolutely right. That is, uh, something that's near and dear to us at Vast is that when you look at, you know, a traditional enterprise, like say like a global bank that has some of everything, they just have so many different products in even an enterprise data storage, a lot of times multiple products in backup multiple products and data analytics. Now, ai, you're building kind of new islands, existing islands in HPC, all those islands create a lot of waste.

And you are gonna see customers looking at their environment and saying, I've got capacity over here, but I can't really use it well for this application over here. So I think it is gonna drive a lot of just focus and rethinking on how I want to plan, because now you're looking at it and having to plan at each individual layer of like every single application, how that application is going to grow and trying to get it exactly right is impossible. So the only way to really get past that going forward is to have platforms that can support a variety of applications.

And then I only have to get really the macro number of my capacity demand, right? As opposed to getting it right 20, 30, 50 times, whatever it is for these individual systems that customers have. What is that thing you see people doing today that just makes you shake your head a little bit and go, folks, we need to be a little bit smarter about how we're using these systems.

I think, and, and, you know, I don't think we can, you know, it's not like, oh, they were wrong, but when we look at environments, there's a lot of systems in, you know, an enterprise environment where you're gonna find data triplicated or you think about, you know, kind of legacy data analytics, HDFS, even things like, you know, log analytics, Splunk, where you're gonna have a lot of triplicated data and thinking about the idea that, you know, I can buy 10 petabytes of data and get, you know, store three petabytes of capacity on it. I don't think it's like that's, I wouldn't blame the customer for it, but I think now you're realizing that some of that technical debt is just such a big drag on capacity, and I think there's a lot of low hanging fruit that, you know, you just have to look at the calculus of, of what you're buying and what you can store on it. And those technologies have been, are and have been a really big drag on that capacity utilization.

Do you also think there's a certain amount of data hoarding going on? I mean, people are just kinda, uh, storing everything in anything without much regards to whether they actually need it? I think So, uh, but I also think it's, I think across the industry, you find a lot of customers do struggle with the idea of what do they actually have, right?

And I think there's now a lot of uncertainty around what has value, what doesn't. Um, so in our opinion, right? We, we love capacity in the world that, um, you know, we would like to see more intelligence put into that process, right?

Of understanding what data you have, understanding where there's value, and I just think it's very difficult for customers to make those decisions. So I don't know that it's hoarding. I think it's almost, uh, you're kind of paralyzed by your capability to actually get insights into what's out there.

Mm-hmm. One of the things I also hear is that people realize that they need somebody who functions more like a data engineer rather than say a storage administrator, but they don't seem to be able to find enough of these data engineers out there. So as part of our issue is that we just need more folks who are savvy about how to manage data programmatically versus just kind of storing data and, you know, making sure that there's enough capacity on a drive.

I agree. I think a lot of, a lot of environments are very much anchored to the infrastructure as opposed to looking at the data, right? So we spend a lot of time talking about data pipelines and how data flows through organizations, and there's just, you know, there's a lot of muscle memory that, you know, I need a system over here that captures data, right?

Then I'm gonna move it over here to do some data prep if I wanna analyze it, or, you know, do faster queries. I've gotta have it on a separate platform. So there's a lot of muscle memory.

There's also a lot of organizational challenges in that you have so many different groups touching these things that to try to make a wholesale change to understand how data can flow through our organization more effectively, you need a lot of people on the same page, Right? Who's leading that conversation? Because, you know, we've always seen CIOs as core to this, but recently we saw the rise of chief data officers and chief AI officers, and there's all these lines of business folks, and sometimes I wonder if there's too many chiefs looking at all this stuff, but who's taking the lead to help resolve this stuff?

Yeah, I, I do think the rise of the chief data officer and chief AI officer, and we engage with customers where those are merging, and we still see worlds where those are different roles, right? Uh, but ultimately what we've seen is, in my conversations, even the infrastructure, people are getting savvier to the idea that the demands coming down from these folks, right? The chief data officer, chief AI officer, who are getting more funding than infrastructure teams are getting influence.

They are, you know, really very much tied to these critical business issues, are challenging the infrastructure folks to realize that they probably can't build things the way they've been building them and meet those needs. I think that's really something that's coming to bear. And, and we're seeing, you know, even more than it has been these data teams driving more of that strategy.

And I think we are seeing customers get savvier to the idea that it's not about data storage, it's about the actual data itself and what we're trying to do with it. Mm-hmm. All right.

It's early in 2026. Get out your crystal ball. What's your prediction for the coming year?

Uh, I think this, even with the challenges, this is the year where you really start to see inference at scale in the enterprise. And I think it's gonna take us about a year to get there, but, uh, AI has very much been something where when we've, we've seen a lot of the spend over the first kind of few years of this AI boom, a lot on training. Now we're seeing some more inference at scale, I would say, in, you know, the re tech savvy companies, the leaders.

And I think over the next year you're gonna see more of that bleed into the enterprise. I think, you know, in the mid-market, that's still gonna feel out of reach in terms of some of the areas they're gonna lean on, um, SaaS solutions to really achieve ai. But more and more, I think by the end of the year, you're gonna see more inference at scale in the enterprise, um, kind of realizing some of the potential that we've been talking about for quite some time.

All, Hey, folks here, heard it here. No matter how advanced it gets, it seems to keep coming back to one thing. It's all about the data.

Hey, Phil, thanks for being on the show. Awesome. Thanks for having me.

All right. And back to you guys in the studio. Enterprise AI applications need a solid data foundation bringing together disparate data sets in a secure and flexible manner.

But despite years of effort, most businesses still have a diverse data environment. Before we will see the value of AI in enterprise applications, we have to solve the challenge of data access. And that's what we're discussing today with Ken Jagen of cdata.

Welcome to utilizing ai, the podcast focused on practical applications of artificial intelligence from the Futurum group. Each episode brings together diverse perspectives to explore news and use cases in the ways in which AI is transforming enterprise IT and the industries it serves. I'm your host, Stephen FoST, president of the Tech Field Day business unit here at the Futurum Group.

Before we dive into the discussion, let's meet who's on the panel today. Hi everyone. Brad Shiman.

Um, good to be back with you. I am the VP and practice lead for data integration, excuse me, data intelligence. I, I'm already thinking about chatting, chatting with Ken today, uh, of data intelligence, analytics, and infrastructure here at futurum.

And, uh, it's, it's my pleasure to join you guys. And we have a, an exciting guest on, I'm going to introduce him now. His name is Ken Jagen with cdata.

Hi, Ken. Hi, Brad. Brad, Steven, thank you very much for having me.

I'm the Chief Product Officer at cdata, Ken Yagen, and, uh, CDATA is a leader in enterprise data connectivity and integration. And, and we have one of the first managed AI connectivity platforms on the market. So excited to talk today about ai.

Uh, as Chief Product officer at cdata, my focus is really on how our customers turn data connectivity into governed scalable foundation for enterprise ai. And that's why we're happy to talk to you about this, because again, this is utilizing ai, we're all about figuring out practical, useful solutions based on ai. And I, uh, learned about cdata last year.

And boy, it, it is such a great idea because essentially one of the ways in which AI is going to become useful is when it can ingest and act on the various types of corporate data that enterprises have, and really, um, bring that data together and give us, you know, help us to, uh, build applications that use it. The problem is that that data exists all over the place in all sorts of different formats and so on. And from the initial discussions with cdata, it seems like y'all are, are really focused on solving that problem.

So, Ken, let's start with just a little bit of an understanding. What's the problem when it comes to data and ai? Yeah, Steven, uh, the, the problem around data and AI is really, data is an AI problem.

Um, the strongest predictor of AI success really is that maturity of your underlying data infrastructure that takes and delivers the enterprise context to these powerful models that companies are investing in. And so the companies have to act on that data. They need to be able to understand it, they need to be able to access it securely and correctly, and then they need to be able to take action on it, which is sometimes requires writing back into the systems as well.

That is a data integration problem, and that requires a lot of sophistication and understanding the semantics of the data and how to access those systems. There's some, you know, new, new technologies and protocols and techniques that are greatly unlocking that, but you also need, uh, that understanding and governance layer as well. And that's what we try to do at cdata.

Yeah, and I, I would, I would argue that there is no AI without data. Um, I, I think that they too go hand in hand, peanut butter and jelly all day long. And, um, like Ken, you said it is a bit of a challenge for enterprises because they have been working hard for decades now to try to modernize and streamline and democratize access to their data estates.

But that is not easy. And it's certainly, you know, if you take 10 enterprises and sit down with them and say, okay, guys, you know, where are you at in terms of, you know, trusting your data, having quality data available to your business users, uh, as well as your agents that you're building right now. And I think most of them would tell you that, you know, it is very much a hit and miss sort of affair right now that they don't have full trust.

They don't have full access. So we ran a survey this summer, um, uh, actually autumn, um, asking data professionals, you know, are you investing in, in, in AI and are using ai? And, you know, as we see everywhere, you know, by and large, over 52% said, we are already using it.

We are building on it. That is our top, top priority is ai. Uh, and yet when you ask them, you know, what are your biggest obstacles?

Guess, guess what the biggest one is? It's, it's not security, it's not integration, it's not money, it is data quality, trust and governance. Uh, agreed.

And, uh, Brad, it's interesting 'cause we actually ran a very similar survey, uh, on our side, the state of AI data connectivity, and we spoke to over 200 leaders in both the enterprise and in software technology companies. And our findings were very, very similar to yours. So we might've been talking to the same people.

Um, we found this Probably were Maybe, yes, uh, 60% of companies had had the highest level AI maturity also had the most mature data infrastructure. Yeah. And the inverse of that, 53% of companies that had immature AI also had immature data systems.

So there was a strong correlation between the maturity of their data systems and the maturity of their AI initiatives. Yeah, because the, the biggest, oh, sorry, go ahead Steven. Yeah, Lemme just jump in there.

Okay. Uh, hey, here's an, here's an edit point. We're demonstrating how to do this.

All right? I wonder if that is a cause or an effect or both. Uh, you know, I mean, if a company has a mature, uh, data foundation, if they really understand their data and they've, they've spent the time and energy and effort to, uh, bring it together in, in some way, uh, they may be better, uh, prepared to develop AI applications right from the start.

But at the same time, as you're pointing out, if they haven't done that well, then they're just not going to be able to get the benefit of AI applications, even if they do invest in them. What, what do you think of the chicken and the egg here, Ken? Is is this a, uh, um, a, a requirement or is this a symptom?

I, yeah, I, I, I under, I understand, and I think I agree with you with some of this, with the, the dual nature of this. Um, and, um, I would say that there is a, if a company has a culture of stewarding their data and having good data infrastructure, they already have a culture that's gonna allow them to move quickly and adopt ai. And, but on top of which, they're gonna have that good infrastructure in which to, to do it.

Those that haven't made that investment, they're trying to play catch up, they're trying to, um, swap the engine in flight by plugging in better data while also trying to plug in ai. Um, there's some ways to accelerate that, but you're gonna have to do a little bit of the work required along the way. Um, and you know, what we try to do at C day is we try to help them sort of accelerate that, take advantage of what they have.

Um, the good news is, you know, often when you talk about data infrastructure, you think about, let's get everything into a data warehouse or a data lake, and let's stage it all there and everything. And that's important, especially when it comes to understanding your business and analytics. And you can apply AI to that.

But there's also sort of the need, and we saw this in our, our survey and our study as well for realtime data. And yeah, realtime data is not data that's staged in a warehouse, but the data that's sitting inside your operational systems data that you're gonna act on directly and orchestrate your workflows and business processes around. And this is where agents in the world of AI are really starting to come into their own and their ability to sort of do that.

And again, they require good understanding of that underlying data. What is, what is the semantics of that data being stored in that underlying system? You know, how do you operate on it?

What are the correct ways to work with that data? And combining the semantics with that data access is what will is that sort of accelerant that will allow you to take advantage of it and mature your AI projects much more quickly. Yeah, I, I agree, Ken.

And, um, it's, it's funny because access and understanding, you know, have to go hand in hand. And I, I feel like right now in the enterprise, we have unprecedented access comparatively to where we were just, you know, uh, even a few months ago, uh, at the hands, for example, of the model context protocol that Anthropic came out with about a year and a half ago, and how, you know, surprisingly, you know, uh, uh, uh, dominant, that has become as a means of helping models in particular access data, but understanding what the data means is, uh, I think a greater challenge and one that not a lot of companies are, are really, you know, uh, able, able to chat about. Um, I mean, I would love to come back and actually talk about, you know, how the easy button of model context protocol and how dangerous that is, because I think you guys, uh, are are definitely seeing that in your customer base.

But before that, can we talk about the semantic layer? Can we talk about, you know, what companies really need to do to build that understanding? Do you feel, Ken, that we're getting to a point now where we have the ability to not go the data warehouse data mart route, but instead have this open composable data lake house, let's say that, you know, totally separates storage and compute and lets me use whatever query engine I want depending on who I am in the company and always have, you know, access to and knowledge of when I say quarter close for accounting and sales, that it means this and not that.

Yeah. Well, uh, you know, the, the, the common phrase is garbage and garbage out. So you have to make sure that you're putting good data into, into that data lake, um, in order to apply that semantic understanding of it.

So a absolutely, I think we are approaching that. And I think AI is, again, as an accelerant of that and the ability to, um, have deeper understanding of the structure of the data and the meaning of the data. And both of those are important.

Lemme lemme tell you what I mean by that. So, structure of the data is, is how's the data stored? What is, where do you find and how do you connect the different fields, uh, uh, the data together and, and, and then can interpret meaning out of that.

And then understanding that data, like what does it mean? What is this number? Is this a quarterly number?

Is it a monthly number? Uh, does it include us or world or, you know, north America, you know, whatever that might be. Understanding the context, what, you know, accounting principles, if you're talking about financial data applied to it.

So there's a lot of context in, in order to interpret that data. And AI is really good at sort of stitching that context together. And, um, at cdata we do is we take that we have some understanding of the underlying structure, semantics, and a bit of the understanding of what the actual data is, and we inject that into the context of the model.

And that semantic, semantic context is super critical, because without it, you, I like to say is you're left with a system that just burns tokens on ambiguity rather than delivering value to your user. And that's so, uh, difficult when you have such a diverse set of data sources. Um, you know, not every data sources created equal, and not everyone is going to be able to have that kind of context.

Uh, talk to us a little bit more about how you deal with diverse data sets. Sure. Well, there, I mean, enterprises, the, the typical enterprise uses hundreds of different systems with data port in all sorts of different, uh, data, uh, locations.

Uh, it could be internal databases on-prem software and systems, SaaS-based solutions, uh, partner systems and so on. So you have to be able to pull all that data together and connect it, uh, in the LLM in order for that context to be valuable. And, and, and that is what a connectivity platform really helps with.

We're actually able to go out and connect across systems and join data across systems, take a bit of the burden off the LLM, so you're not consuming all of your context and all of your tokens by having to bring all the data and do that processing in the LLM, we can push that down into these underlying systems across multiple locations, and then expediently bring that back to the LLM so we can do the final sort of reasoning or actions that it needs to perform. So being able to handle diversity is really, really critical, especially in some of these new agent workflows that businesses are building. We as humans, we deal with that sort of, you know, diversity day by day.

You move in between what you used to call swivel chair integration and moving between system to system, pulling data together, copying and pasting, pulling up analytics reports. We do that as part of our job. We're now asking AI and agents to do this.

And so it needs to be able to manage and handle that diversity. So you need to be able to, to have a system and underlying infrastructure that supports, uh, that diversity as well. Yeah, at scale, right, Ken, because, um, as we start to get into, you know, these, these very advanced ag agent pieces of software that we're building right now, getting data to the model is half of the challenge, half of that battle and, and not just understanding it.

And so you see a lot of investment right now in, in things like memory caching for being and being able to batch process and be able to, you know, not burn tokens, but still get the data to the models. And I think we need to also think about the fact that it's entirely new constituency, uh, not just for consuming data, but for producing data. 'cause these agentic processes create a lot of information as they go, and it's data that needs to be managed by the business because, you know, you, the, we, I was just actually talking to, uh, a number of, uh, companies who are building out commerce systems that are agentic.

And the biggest challenge they felt they had was, was being able to take the data that gets generated from each interaction with their customers and to, you know, have that available to the agent, not just today, but tomorrow and the day after tomorrow. Yeah. And that, that's, that's where these, uh, data management platforms, large data lake solutions can really valuable.

You have a place you can go store that at scale and then go back through, through agents, uh, and access it and bring it into the context of, of your workflow. Um, I do wanna, if I can go back to Brad to this point, you've brought it up a couple, a couple times in the concept of the explosion of access. Uh, and, and, and what you were saying just there reminded me of it again.

And that is really important to think about because I, so I've, I've been around the data and application integration world for almost 20 years now, and saw the explosion of, of APIs and SaaS and, and integration and iPads, and now, uh, with, uh, AI and agents and MCP and data's getting easy and easier to access, requiring less and less sort of technical work to, to bring it to the point of use within the business. And access is really critical access, both in terms of scale, like you said, you know, you can bring back too much data, burn a lot of tokens, uh, bring back inappropriate data that maybe the user's not, or the agent is not allowed to operate on. Or maybe the agent might do something with it that a user would know not to do.

Uh, so you need to be able to sort of govern that. You need to be able to handle at scale. Uh, you mentioned model context protocol.

You can have a tool explosion model context protocol represents everything as tools, and you can only handle so many tools within the context of an LLM. So you need to be very efficient in the tools that you expose within an agent to the LLM and to how much data you bring back. So leveraging the power of these underlying systems, not overload the LLM with too much data.

So there's a lot of things around access that need to be thought of by someone architecting an ag agentic system. And, uh, again, those are areas that we, I spend a lot of time thinking about how do you actually scale this and do it effectively and efficiently. Uh, and it's something that we see our customers, um, really kind of struggling with when they come to us, but realizing that there is, there are better ways to do this.

There are definitely better ways. Um, and, and unfortunately the, the technology is moving so quickly that it, it's becoming a, as you mentioned, too easy to access data and to do so unwittingly, un responsibly. Um, and also it, it's, you know, performance wise, the, the tech, the tools that we have available to us are allowing us to build systems that we can't support our, our infrastructure just isn't ready for.

And I, I like to think, you know, when I, when I think about cdata and you know, vendors that are playing in the space, you are that at the end of the day, it's about, you know, helping customers see that they should not go the shadow IT route because that, that's dangerous. Um, but there are options to, you know, accelerate what they have and to meet those evolving capabilities as well as needs that we're seeing start to, to come into market. I mean, I saw a model come out just this week, uh, that has the ability to handle 400 tool calls in a single, you know, long running pass.

That's insane. Uh, that is, that's, that's a lot of processing, a lot that, a lot of power that you have in that, in that type of model. And that's the thing, we don't know what's gonna come out next week.

We don't know how these models are going to evolve and what capabilities they have. We know they're gonna do more than they do today. And so you have to kind of plan for this unknown future.

And so when you're thinking about how you design these systems, you do need to think about scale and governance. And you also need to think about what might be possible six months from now. Um, and the other thing is make sure that you're designing to, to update and refresh this, realizing that your architecture's gonna change.

There's gonna be innovations to take advantage of. So you have to be agile. And so you need to use underlying infrastructure that's also agile and gives you that flexibility.

Don't tie you down to, to one particular model or infrastructure vendor, uh, allow you to move around, consume new data sources that you didn't necessarily have that you weren't thinking about before. So that agility is really important in this type of fast moving world of ai. Well then that point that you're making about is, goes to the point of maintainability.

And that's been a, a key problem. Anytime you're building an application that integrates diverse data sets or tools, I, i is the inherent sort of fragility of those systems because, uh, you know, vendors can change the ways that their APIs work. Uh, they can change their, you know, they could abandon, uh, one API or another.

They could, uh, really upset the apple cart. And this is especially true. It gets multiplied when you have more and more and more disparate, you know, components in there.

So one of the points that I was gonna ask, and I think you've sort of just a answered it, is why not just rely on the vendors to make this accessible? Why not just work with, uh, you know, whatever happens to work? And I think that the, your answer might be, because even if it works now, it might not work later.

And also maybe, you know, different vendors may have different approaches. They may not wanna support this or that model, and you would perhaps allow them to, uh, integrate with, um, a broader set of data. Is that right?

Yeah, exactly. You might wanna switch vendor, you wanna wanna switch model vendors, uh, next year. You know, did you tie yourself to the, to the capabilities or the interface of that particular vendor?

Or do you have the ability to kind of switch, switch out, switch that out? Uh, this is particularly the case when going with sort of full stack solutions from some of the legacy players. You miss out on some of that innovation that's happening in the market.

'cause they're gonna be a little bit more, they're gonna be a little bit slower to bring that capability to market. And so you, you wanna have that agility. That's exactly right.

Yeah. And I, I think it's, um, you know, when I look at the marketplace for this year, uh, one of our biggest trends that we see evolving is this acceleration through integration. And that, you know, last year we probably would've talked about, you know, the format wars and is it gonna be Apache or, or you know, is, is it gonna be Delta?

And you know, that's done. It is, it is definitely, you know, Apache iceberg all day long. And that separation of storage and compute I mentioned, and what that does is place the burden on the vendors who are building these systems to, to provide that sort of interoperability.

And what I worry about honestly, is that we sometimes when we get a shiny new toy, we over rely on that toy to to scale with, you know, our needs in the marketplace. And I think MCP is one of those that's just been so over used. Uh, you know, right now that it's, it's becoming itself a, a sort of liability in terms of that.

That's, you know, like we talked about before, understanding the meaning of the data that it's accessing, accessing the right data. How do you secure that access point? Because those standards, like a 2:00 AM CP, any other framework you wanna throw out there for integration is, you know, an abstraction layer.

And those abstraction layers aren't free, right, Ken? They, they do have a cost you have to pay, I think. Well, I think I, I, I wanna agree and disagree with you on that.

So, uh, I agree. I agree that there is, there is, you, there's a trade off whenever you have an abstraction layer. 'cause you're, you're always, you're always in a trade off.

'cause otherwise you would go to a proprietary approach and you might get something very much more specific for your needs. But extraction layers help markets sort of stabilize and mature. They allow people to focus on one thing, and that's what MCP has done.

It allow people to focus on a single way to connect their data, their, uh, and their tools into the LLM. Now, if you just just utilize it in that way, and you don't think about how you deal with authentication, how you deal with governance, how you deal with security, how you scale it, how do you manage it, change management, all of that, then you're gonna be in trouble, like you say, then it, then it's a crutch and it, you're, you're not going to be successful at the end of the day. Um, if you just use agreed, go grab the latest MCP server in some open source community, it might work for you original, initially, but you might, you're quickly probably gonna find out it's a bit brittle, it's a bit fragile.

Even some of the ones from some of the first party, uh, providers out there, they're incomplete. Um, and they don't maybe have all the capabilities that you need in order to solve for your problems. So you gotta build around that.

And that's one of the things at Cdata, we're looking at that. We've built our own MCP servers for over 300 different critical business systems. And we've built all the governance, we've built the, uh, security, we built the scalability, and I also added that semantic layer around it to make it much more effective and much more efficient, so that now you do have something that you can rely on that's stable and that you can, uh, scale your systems on top of.

Yeah, I recall there being a market, oh, I'm sorry, Steven. Yeah, I, I recall there being a market specific to integration, um, that that's all vendors did, and they built connectors. And we, we seem to, as a marketplace, have tried to move away from that and say, oh, you can just do it yourself.

But that's really not the best approach when you're trying to, to have a system that could adapt to that changing data estate that we've been talking about, to be able to say, today I need Salesforce data cloud tomorrow, I, I need something on, you know, a totally different platform from SAP. Yeah. And I, I would say, you know, for very simple things, for very simple APIs, uh, a lot of roll it your own.

But as Cdata, we've lived in that sort of world of connectivity for many, many years. We have 10,000 customers that are, uh, that are licensing and using our connectors, including some of the largest software companies in the world, um, that are embedding it inside their platforms, uh, in order to provide connectivity out to other data sources. So connectors are a critical component.

MCP as we're talking about here, model context protocol puts an abstraction over that concept. Um, so that anything that you can connect to anything that has an API, you now have a way to plug it into an LLM and make use of the data and make use of the actions that you can perform. And, and that's important to, to think about.

It's like MCP can be data access, but it can also be operational execution. Uh, and there are other concerns when it comes to operational execution. You're changing data, you're triggering workflows, you're, you're triggering actions within your organization, within your enterprise that have implications.

And so again, security governance and all around that scoping it, scoping the permissions down to the set that are necessary for that agent to perform the types of actions that would be necessary for whatever its goal or objective is, and not allowing it to stray beyond that. Yeah, I think that you all have a lot more experience with MCP than most of the folks listening. Um, I wonder, I, I appreciate you kind of bringing those, the, uh, thoughts to, to the fore here about MCP and thinking about governance and security.

Um, what else could you tell us if, if we wanna kind of step back here a little bit, um, what should people know about MCP if they're looking at it, if they're thinking of implementing it? Um, you know, what have you learned in all the years of developing or the, the year of developing all these, uh, MCP servers? Um, what are the lessons you've learned?

Yeah, I mean, the lessons I learned, first of all, MCP sits on top of the underlying data sources, the APIs or the, uh, the SDKs are used to access them. There's a lot of complexity. You know, just because something is rest doesn't mean it operates in a certain way.

So there's a lot of complexity underneath it that you need to deal with in order to have a good functioning MCP server authentication is still hard. We wish it could be easier. I mean, so back in the day, I actually worked on the, uh, saml, uh, committee to develop that as a standard, took us a long way.

We're still, you know, utilizing that inside of OAuth and everything else. So, but there is a lot of complexity when it comes to identity. And you, and also with MCP, you have to think about identity in the context of the user now in the context of the agent user and the, and the scoping of that identity.

So that, that's something to deal with. Um, there are their own little sort of enhancements or additions that each, um, client has created open ai. They have their open ai, they have their apps, uh, Claude Anthropic Claw, they have skills, they have different things that they're building around MCP that are specific to each of them.

And so you need to think about how your MCP server is gonna interact with each one of these, whether it's a chat LLM type agent or an agent platform. Uh, that's something to think about. And then there is, there's governance.

There's, how do you discover the registry for discovering, I mean, it's, it's okay if, you know, you've got a handful of CP servers, but what if you've got thousands of MCP servers? What if everything in your enterprise is suddenly, uh, MCP enabled? Now you need to have a concept of a service registry and some side of governance around it.

Um, so we're not getting away. We had that problem with APIs and API management. We had it back in the SOA days with SOA service registries.

It's, uh, we had it even back in the corba days in the nineties. So it, it's, you know, naming and discovery is always gonna be an issue. It's gotten a bit easier, um, with LLMs, but with MCP, you still have that, that concern.

Yeah. Bring, bring back middleware. Uh, I, I, I, I love the, the, the SOA era just because we, we were trying to build software the right way, and it just, it just turned out that it was a little bit more difficult than we thought.

Um, but may, maybe we have the option now, but I, I, I totally, you know, agree with what you're saying, Ken and I, I feel like, you know, when you're talking about CPS as just another means to, to get to those sources, you do have to consider the models. And I've felt for a while now that the models themselves, especially the frontier models, are much more than just, you know, a, a, an endpoint that you're querying. It's, they're actually platforms.

And so you need to have standards, you need to have some sort of registry to understand when Google changes the Gemini, um, API subtly that, you know, it's, it's not gonna break your application tomorrow. Uh, maybe the model itself changes in, in its ability to like, um, refuse a request or continue the request. And all of that is, is much harder to deal with when we have these models that, that are non-deterministic that we're using as infrastructure.

Exactly. If I, if I can just summarize some of my thoughts on that. Uh, so I'm a proponent of MCP.

I'd like that it's being developed in real time and tested in the market and iterated on as opposed to being developed in a, in sort of an ivory tower and, and over-engineered. We've seen that in the past. So I really like the approach that, that the vendors have taken to kind of come together on this and, um, try to not try to solve too much and allow other infrastructure and software companies and the LM providers to come in and build around that, to, to kind of sort of polish those rough edges and provide the additional support and capabilities that are needed.

Uh, I think that, uh, one of the key areas that, that needs a little bit more work and that we're focused on is that semantic context, as we've talked about and under understanding what's, what the capabilities are, the underlying systems. Uh, I think that it opens up and allows for real time data access and action. I think that's very important.

Uh, and it recognizes that so much of the enterprise data is in this sort of structured format that agents being able to access and operate on and com, that combination with these types of standards and these types of capabilities will allow us to get to sort of this promise of digital employees, digital agents that are agent to agent working together, uh, swarming together to solve, uh, solve problems and operate businesses more effectively and creating more enterprise value for us. So all of that said, I think it's time to, for companies to be investing in their data connectivity, investing, investing in their infrastructure, and to do this to enable AI to answer and act on their business. Yeah.

Thanks for that. And, uh, I think that's a good message to leave our audience on here. Uh, as Brad said at the top, uh, you know, you can't really build an effective AI application without good data.

It's all about the data. And, um, that means that this is an area that, uh, companies are gonna have to invest in if they're going to have an effective AI application. Thanks for joining us.

Uh, before we go, uh, many of our listeners may wonder how they can continue this conversation or where they can connect with you. Um, Brad, uh, let's start with you. Uh, what are you researching?

What are you working on, and where can people find you? Yeah, right now, I'm, I'm actually building out a new, um, comparative report using our, our, our signal, um, a agentic report process on data intelligence platforms. And that's gonna be all about how you get that semantic layer and put that in action just like Ken said.

So looking forward to that. com. Excellent.

And, uh, Ken, how about you? Great, thank you, Steven. Uh, likewise, uh, happy to people to reach out to me, connect to me.

LinkedIn's, uh, gonna be the best way. Ken Yagen on LinkedIn. Uh, other social on, on X and other things as well, uh, I mentioned earlier, but I encourage you to download cdata state of AI data connectivity report from our website.

We'll provide the link, uh, along with this podcast, and you can also check out our product. com. Uh, and I'll be speaking in March at the Gartner Data and AI Summit in Florida.

So if you happen to be there, come check out talk. We're gonna be talking about, uh, this same topic there. Excellent.

And, um, as for me, uh, you know, I run Tech Field Day, uh, this week is AI Infrastructure field day four. com and the Tech Field Day socials. And of course, we will be having another AI Field Day in May.

So keep an eye on the Tech Field day socials to learn more about that. Thanks for listening to this episode of the Utilizing AI podcast. If you enjoyed this discussion, please do subscribe on YouTube or your favorite podcast application.

Also, drop us a line, uh, give us a rating, give us a review. We'd love to hear from you. This podcast is brought to you by the experts at, uh, Futurum Group, uh, where Insight meets ai.

ai, the utilizing AI YouTube channel or textron's, uh, new TV app. Thanks for for listening, and we will catch you next week. Hello everyone, and thanks for joining the third session for today, uh, which is called From Notebook to Production.

And we'll look into OpenShift AI on our cloud services. My name is Philip, and like the previous speakers and the managed OpenShift Black bullet Red Hat today, we have seen already two sessions, which are about the foundation and the legacy. So we've already seen how you can manage your containers, uh, next to your VMs, uh, on Reddit, OpenShift.

Now we'll have a look into the future, and I will tell you a little bit about AI workloads, which are also possible to run on OpenShift with our product OpenShift ai. As you can see, um, there are like several people involved in AI or ML projects. So we are talking about business leadership, data engineers and scientists, ML engineers, app developers, and IT operations.

All of them play their part, uh, in the full, uh, lifecycle of these projects. Um, so, um, as you can think, this might open up the opportunity, um, to create silos, but what we've learned in the past, um, with application development, uh, silos are basically not like a really good idea. Um, they're basically coming out of the tool sprawl.

They're using like several different tools, um, which they like to use for their specific part in this development cycle. I would show you later, uh, how with OpenShift ai, we can have an, uh, platform, which, uh, covers all of these aspects and caters to all of these roles. Um, another perspective is, um, that, uh, getting ai, um, project into production is like really hard.

You can see a statistics from 2024, uh, which basically says that, um, about 80%, um, of IT decision makers say that it takes, uh, six months to two years, uh, for their company to, um, get their AI projects from pilot to full production. Um, this is usually because of silos. There is like a complicated handover between teams to actually get these applications, uh, out to the customers.

Um, I want to tell you a little bit more about, uh, OpenShift ai, ai, uh, our integrated AI platform running on top of red OpenShift. Um, so we cover basically all the aspects of the AI and ML lifecycle. So we'll start, for example, with model development.

Um, you can, uh, train your own models, um, or fine tune existing models or just use models, um, that are, um, yeah, like open weights or similar, and you can integrate, of course, several AI and ML libraries frameworks and everything. Um, the next step would be like a model serving. So after you have trained or fine tuned your model, you need to serve it, um, with some inference server or something like that.

So we, uh, can, um, deliver, or you can deploy the models on top of OpenShift, um, with a kind of well-known platform when you, for example, already use it for your container VM workloads. And of course, this comes with an observability stake, um, to, yeah, get some insights into the model's performance. Um, talking about lifecycle of the models, uh, we can use, um, well-known DevOps practices and to create pipelines, um, and extend them to some ML ops, um, workflows to continuously train or fine tune the models and get them out to production.

And of course, we can use the already well-known tools, um, to, um, manage the resources, uh, of their underlying cluster. And this is especially interesting when we're talking about accelerators like gps. So in the session, I will, um, kind of take, uh, two perspectives.

Uh, the first one will be, um, the data scientists view. Uh, data scientists usually want to use the tools to have, and that, uh, like to cover the platform engineer's perspective, um, to, uh, make it more, uh, visible or more obvious that AI workloads are not a black box, but it's basically just another workload. So first we'll start with the data scientists view.

I will directly, um, go into demo and, um, show you how it really looks, uh, within OpenShift ai. We will first see the dashboard and then we'll will, um, go into the creation of a work bench. So this is the, uh, dashboard of OpenShift gi.

On top, you can see, um, all the data science projects and in the background, these are basically Kubernetes, namespace or OpenShift projects. So we are now on the dashboard of OpenShift ai. As you can see on top here, the data science projects.

And these are all the projects already created on the cluster and the, and background relate to, uh, OpenShift projects or Kubernetes namespace. Um, on the bottom, you also have some the learning resources that you could use to, uh, scale up on certain topics. Um, on the left, there's like a navigation.

We can go into the data science projects. So this is just different perspective, uh, of the full list of, uh, projects available on this cluster. We can click on models with the model deployments.

Uh, there we can see, um, all the model deployments available within these data science projects. We also have a pipelines for experiments and for, um, training and running and executing these models. There is also, um, some external applications that you can, uh, get, uh, or install onto your cluster.

Uh, but we'll not cover these, uh, in this topic. Um, there are also, um, learning resources with some, um, documents, blogs and videos that you can, um, use to scale up on certain topics. We have our hardware profiles, we will mention these later.

And, um, user management. So today we want to focus on, uh, the Jupyter Notebooks or the work benches. So I will click into my Cloud Friday demo, uh, data science project where I've already created a one work bench.

Um, if you'd like to create a new one, you just click here, and then you have to fill in some form fields. So basically you have to give it a name, you can describe it, and then you can select the workspace, uh, the workbench image. Um, for the workbench images, we have several options coming with OpenShift ai.

So the first one would be Kuda. Um, kuda is basically used when you want to use the NVIDIA toolkit. So you, if you have Nvidia GPUs as accelerators, uh, these, um, work bench images might be a good foundation.

Uh, we have that standard data science image, which just, um, uh, contains commonly used libraries for machine learning. Of course, we have tens of raw flow and PyTorch, a minimal Python environment with just a Jupyter Lab for, um, executing Python code. We have a trustee I notebook image, um, for model explainability tracing and accountability ha ai for hena GDI devices.

And in technical preview, uh, we have the code server, which basically, um, gives you a BS code environment. So when you select, for example, um, a minimal kuda image, and we give it a name, um, then we can select the version of the work bench image. And afterwards, um, we have the option of the deployment size there.

We can, uh, choose the hardware profile. This is one of the hardware profile we've seen before in the, um, settings. So in this case, since I'm using Coda, I need an Nvidia GPU, so I can select my Nvidia GPO hardware profile.

Um, I can also change my CPU and memory and GPU requirements, um, just by, uh, pushing a buttons or putting some numbers into that. I can add environment variables and, um, attach existing storage or create new, um, persistent volumes that get attached, uh, to this workbench. I can also, um, attach, um, connections.

So this is basically used when you want to access some, uh, object storage, for example, uh, for some, um, external data. Um, going back to OpenShift ai, uh, we can see that I've already created a workbench called Cloud Friday STEM book. Um, I can now start this workbench.

It will, um, create the corresponding Kubernetes app objects, um, like the PO where the Jupyter Lab is running inside, um, and the additional containers that are used, for example, for, uh, and the persistent volume for persistent data. Um, after this workbench is started, and I think this will be in some seconds, uh, we will get a link, uh, to open it, and we will be directly in the Jupyter environment. I will now click this link and the Jupyter server is actually starting up.

Um, as I've tried this before, uh, I immediately see where I left this when I stopped this environment beforehand. And when I execute this code, I can see that this and Jupyter, um, environment's running on a node with a Tesla T 4G PU. So we will stop this workbench again, since we want needed for the next demo.

I will now switch heads and talk more about the platform engineers view, uh, on these AI and ML workloads. For platform engineers, it's like really important that AI and ML workloads are not like a, like, like a black box, but it's, um, is, um, something more well known to them. So we will go again into our OpenShift environment.

So this is the administrator's perspective. When you log into an OpenShift cluster, I will switch to the developer's perspective and go into the Cloud Friday demo project. Um, as you can see, uh, here is a stateful set with the Cloud Friday demo notebook.

So this is basically, uh, the deployment of the Jupyter Notebook. Since I've stopped it beforehand. There are currently no pods and there is also the external route.

So this is basically the link that we've seen in OpenShift, uh, when we started this environment. Um, as mentioned before, everything in OpenShift has an OpenShift representation. Um, so for example, when we start, uh, model deployment, I've prepared already, um, inference service, uh, with the QU model.

Uh, we'll start this and show you in the meantime how this works. So also in this case, we can give you the name. Um, we can select the service runtime.

So for example, we have a VLM, uh, with Nvidia, GPU, uh, on kerv. So these are like a lot of words. So VLLM is an open source ference server.

And this is a project, um, which is, um, there are also a, it basically mainly contributed by, um, red Hat and we will use kerv. So this is a service engine, uh, built on top of K native. So basically a serverless runtime to, um, do interference with, um, LLMs.

Um, so yeah, we can select Canadian serverless and the number of replicas. So we can also choose to have like a minimum replicas of zero. So, and this would basically deploy a model, uh, which is only, um, like creating the real deployment when the first request comes in, and we can choose the maximum replicas that it can scale to.

So we would keep it like with minimum one, maximum one. So it's always running. Again, we can select the hardware profile.

Um, I can again choose an avid GBU U, so this would use Tesla T four, um, to around ference server on this node, we can choose to make this deployment available through an external route. So, uh, when we click this button, um, OpenShift automatically creates a route to this model. It makes it, um, available outside of the cluster.

If we don't click this box, this model would only be available inside of the cluster. Um, the model location, um, I've prepared some, um, OCI con, um, some, some, uh, URLs for OCI containers, uh, which contain the model files. But we can also choose, um, other, um, storages like the OCI compliant registry and S3 compatible object storage or generic URLs when it's, uh, living publicly on the internet.

And we can add an additional serving runtime arguments that these would be arguments used by VLM, uh, for configuration. And again, we could use, uh, environment variables, uh, to inject into the pots. So as we can see, uh, the model is now deployed, and when we switch back, um, to the OpenShift cluster, uh, we can see that now there is, uh, Canadian deployment with my WAN predictor.

Um, we have a public route because I've exposed the model with a route, and we can see that, um, there is currently one instance of it running, uh, since we've defined previously to have a minimum replicas of one and the maximum replica of one. And this will be, uh, a static workload. But as mentioned before, uh, we can also scale is on demand.

Um, since, um, all of the things I did in OpenShift ai, um, are also like YAML files within the OpenShift cluster, um, of course we can, um, can extract them, um, after clicking them together in OpenShift AI and, um, use them, uh, in a ops process. So for example, the model that I've just created, um, I could export, um, the Ference service, um, and yeah, put in a kit repository and now, um, um, use, uh, the GitHub's way of deploying these models, for example, in other environments. Um, yeah, or just to replace this manual process in the future.

Um, yeah, as we have mentioned before, um, this is just an OpenShift, um, um, components. Um, so we can see that there is just a pot like, uh, every other pot on the cluster. So it's basically behaving completely the same as, uh, any other application container.

Uh, we can see that, um, this pod has an, uh, in it container and, um, contains of four, uh, other containers which basically run, um, this model. Um, we have some, uh, volumes mounted, and it's basically, uh, behaving exactly the same. So this also gives us the possibility to use the, uh, observability stack, which comes with OpenShift.

So we can, for example, see the CPU use usage, memory usage, um, the network io network errors and things like that. Uh, since all the metrics are just exported, uh, into Prometheus. So we can also create alerts on top of that.

And, um, we see the same events that we're, that we know from our container deployments. Um, yeah, for example, there was some issue with, uh, fulfilling some ingress. And, um, yeah, so if we see some issue when deploying our, uh, model interference server, we can use these perspectives to debug this.

Um, yeah, so we basically have two, um, model serving platforms. So we have to decide on single model, model serving. We can use K server for that, or a multi model serving with a model mesh.

So single models as a case server would basically be used for, um, larger models that you'd like to have running all of the time. While model mesh, uh, might be a good idea. If you have like smaller models and you want to switch between them a lot, uh, of course you have taken into account that it takes quite some time for larger models to get loaded into the, um, memory of the GPU.

Um, so yeah, larger models should be basically static and you could use model match for smaller models. So we've seen like the two perspectives, um, the data scientist perspective and the platform engineer's perspective. Um, we've seen like two products from our Red Hat ai, uh, product portfolio.

We've seen the AI inference server, which is basically our build of VLM, and we've seen OpenShift ai, uh, which also has a corresponding open source project, which is the open data hub. Um, there is also a redhead enterprise Linux ai, which is a build of redhead enterprise Linux with an FERENCE server and, uh, instruct lab for Model ft. Um, of course, like all our products, uh, these work on kind of any hardware and, um, kind of everywhere.

So you're free to choose between bare metal physical nodes, virtual servers, uh, your private cloud, the public cloud, and on the edge. Um, the demo we've seen before was actually running on Azure at OpenShift, which is our managed OpenShift service in Azure. Um, we have, uh, a similar product in AWS in GCP and on IBM Cloud.

So what's the differentiation? So, so, um, OpenShift ai, um, promotes the freedom of choice. So you can choose, um, whatever, um, an AI ML framework or model, um, or tool stack that you want to use.

Um, we have a consistent way, um, of a moving experience to production, uh, with, uh, our application platform. So it's basically following the same process, um, that you can use for your, um, application container workloads. Um, yeah, you can now adapt them for AI and ML workloads and, um, you have hybrid cloud flexibility, so you're free to choose and to deploy your models, uh, where you want to if it's on-prem, in the public cloud or in a completely disconnected environment.

So thanks for watching and if there are any further questions, just get in touch and I'll be happy to answer them. Uh, I'm just gonna jump right in. Uh, mastering personal branding from a ciso.

I will share a little bit about myself. Uh, I started off as a hacker. Uh, one thing led to another and my passion turned into my career, uh, ended up in the cross areas of the government for the hacker side.

Turned out to be a good guy, and it's been profitable ever since. Um, I am a father of four. Uh, my wife and my kids came to the last cruise con, um, and they were like the defacto mascots actually for their cruise con.

And so thank you kq for bringing your kid along and giving us something to, uh, have fun with. Um, I've been to CISO four different times, five, depending on how you look at the divisional CISO role. Um, and, uh, proud millennial.

I think being a millennial has been a big part of the fact that I've been active on social media. Um, and then yeah, second at Cruise Con, uh, still don't know why Ira keeps bringing me back, but here we are. And, um, this guy right here, no brand expert.

This is me at 24 celebrating my first season opportunity at Lockheed Martin. Um, when I told my wife, I don't know about a year ago that I'm gonna be doing a conference, and she said, all right, great. You do all these conferences, what are you gonna talk about?

I said, well, it's on a cruise and we're gonna talk about personal branding. Both of those statements made no sense to her, right? Conference cruise jar, personal branding.

Uh, this guy had no idea what personal branding was. I barely knew what leadership was. And to be honest with you, I'd much rather just be hanging out in my crocs.

And in fact, I tried to bring my Crocs to the last one, and I tried to convince my wife, there's sports mode and relax mode, right? I mean, sports complex, sports mode, cruise, climb, relax mode. She did not go for it.

And I thought, because she didn't make it on this trip that I could sneak it on my luggage, but when I got here, it wasn't there. Uh, she's like, that's not gonna be your brand. So, um, if you didn't know what I mean, I'll buy you a bunch.

A set of Crocs. Well, I don't know if Sean will be okay with that. 'cause we had a conversation and Sean had all types of negative things to talk about Crocs, um, and I was like, oh, man, that's part of my talk, but, uh, let me not speak up on that.

Uh, but for those that aren't aware, it's a real thing. Um, so let's, let's talk about personal brand. What does it actually mean?

People always ask, well, is the brand just your reputation to a degree, but it's the digitization of your reputation. Historically, in the past it was word of mouth was your reputation. That's no longer the case anymore.

Today people are opening up their computer and they're looking you up. People looked you up before you got on this cruise, if they saw that you were coming on the screws, especially the vendors, right? So that is part of what it is.

That's just part of your brand. But what exactly are they looking for? What impact have you had in the industry?

What does your resume look like? What is your track record? What are the things that you stand for?

What, what is your value? Right? And so those are the things that people look at.

And collectively, that's how they determine your personal brand. So for CISOs, it means a lot of different things, but it really means how good are you at your job? How well are you at managing risk?

How well do you communicate that risk to, to leaders? How do you handle the hard situations? I like to say that CISOs are like tea bags.

You never know what's in them until they're in hot water. And, and that's real. And your internal brand and your external brand will be determined by how you handle the hard situations.

A lot of the time, we're all gonna have a bad day. There's just no way around it. It's how you handle that bad day that people will determine this is the right guy for the job or the right, the right lady for the job.

And so when you think about your personal brand as a leader, it's really how well are you impacting the organization internally? How well are you affecting the community and so forth. So how many of you think you already have a brand?

How many of you think you don't have a brand? Fair enough? When someone looks you up online, they're saying something about you.

They've made a determination. Even if they see very little bit about you, 'cause you may be one of those people that try to keep as small of a footprint as possible. IRA has changed that.

'cause you come to Cruise Con, right? So you now have a brand. Everybody has a brand in some capacity in, in some way.

Now the question is, are you shaping it or are you letting other people shape it? And that is the really the crux of the conversation when we talk about, uh, branding. So I'm gonna put this to the test and I'm taking a chance.

I did this last time. It kind of worked out okay. Who actually has internet service on the, on the ship?

Who, who purchased internet service? All of you guys. All right, you guys have, you guys have one minute I'm gonna ask you to Google me.

My name, jar Beason, J-E-R-I-C-H, last name B-E-A-S-O-N. And then I'm gonna pick on a few of you and ask you what you think my brand is based off of what you find online. It could be good, it could be bad.

I got a really interesting bad one last time, but it's all right. That's all part of the deal. Um, but I'm curious what you guys come up with.

It's always the most awkward part for me. Like what are they finding? The people they go to page two and three will go really find the interesting stuff.

Yeah. Oh, some people went to the way back machine. I'm like, come on guys.

You don't have to go all the way there Anymore. Yeah, that's a good point. All right.

Who wants to volunteer something that they think is associated with my brand? Based off of what you've seen so far, John? See the guy wearing a suit.

Guy in a suit. Yeah. I want appear professional even though I'm clearly not.

All right, anybody else, Sean? Sure. Uh, credibility and experience, Credibility, partnership.

I like that. I like that. That's What Chad GBT said.

Yeah, I got All right. Yeah, open AI knows me. I don't know if I like that or not.

I like it. Human-centric and empathic leadership. Human-centric and empathic.

Okay. Ah, man, this is good. Keep giving it to me please.

So make me feel good, Bob. So The first thing I noticed is 41,000 followers. And that is, that's just impressive.

There's a lot of people that follow you for some reason, and you're there looking at the best that we put. Well, thank you. Thank you.

One more. Yeah. You are actively engaged in giving back to the community.

Yes sir. Yes, sir. All right.

This is, this is good so far. Um, I had AI write this for me about what my brand is and I helped shape it. But ultimately what I want my brand to be is that I'm giving back to the community.

I'm shaping the next generation of leaders. I'm equipping the next generation, but I'm also able to communicate with all levels leaders or, or younger. And some of the words I was hoping to hear you guys kind of threw out there, right?

Uh, empathetic. I think I heard someone say that. Experienced, um, getting back to the community cyber peeps.

I run a mixer in the Houston area coming to Dallas soon. Um, neurodiversity. So this is one about three years ago that I picked up because my son was diagnosed with autism, A DHD and dyslexia, which is the trifecta of Neurodivergence.

Um, and as I dug into it, I realized that this is actually hereditary meaning cases. My wife realized it was hereditary and she was like, you're the reason, 'cause you're probably neurodivergent too. Um, turns out I am on the spectrum and if I've had a conversation with any of you guys, you may or may not have noticed.

I never look you in the eye for more than three seconds. I can't, I don't know how to do it. It's just part of my neurodivergent spectrum.

Um, but as I dug into it, I learned the cyber community has more neurodivergent people than any other industry. We're more equipped to have neurodivergent people. And if we know how to harness that power, we can actually do some really good with that.

Um, but we also treat them like pariahs a lot of the time. And one in three people are neurodivergent. So I look across this room, 60% of you guys are neurodivergent from the conversations I've had, right?

But, But that's our industry, right? And so I, I could talk about that for years, but that's something that I just started to pick up about three years ago. And the one really important one is this one right here in the corner.

I want you to think I'm not cheap. You're not gonna ask me to come to a conference and I'm gonna be, you know, doing a conference for free unless it's maybe given back. Or if you wanna hire me, you're not gonna be able to offer me $95,000 and think that that's the CSO role that, that I'm gonna take.

And just off my brand. I don't get those requests. Like, because people would know that that's not gonna fly.

And so that's just an example of some of the things I would want people to say. But now we're gonna do this a little bit different. I want all of you that have a phone, internet or not to write five things you think are associated with your brand.

We're gonna do a little activity. You have one minute. This might be hard 'cause it's your first time doing this, but soon it'll be like your elevator pitch.

All right? Who has all five? All right, I'll give you guys one more minute.

'cause some of you're slow. I'm assuming it's a neurodivergent ones HCPT, That works too. All right?

Because I know I'm not gonna be able to use all my 45 minutes. I'm gonna ask all of you to pair up with one person and you're gonna Google that person and they're gonna Google you. So just pick one person in the room, hopefully someone close to you.

And we're gonna see how close what you think is the case. I don't have Google. I bought the pack.

It's, Yeah. I need the people to have energy. I need the energy up.

All righty, let's wrap it up. Let's wrap it up. Thank y'all.

Alright y'all, thank y'all. Thank y'all. The activity has commenced or IRA's gonna gong me when it's time for this team to be over.

So, uh, thank y'all for participating in this. Uh, real quick, by a show of hands, for the people that uh, participated, um, how many of you had all five of the things that you said called out by the person you partnered with us? What about four of the things that you said About four Four?

How many of you guys had three of the things that you said, alright guys, we're done with the activity. We're done with the activity, we're done with the activity. How many of you guys had three of the things that you said called out by your partner?

Well, we missed the part where we're supposed to call out. Oh, you missed the part. Okay, that's fair.

Sorry, We just, we're doing, That was a, that was my fault. How many of you had two of the things that you said called out two? Alright, so that shows the gap between what you think is your brand and what other people think, right?

And, and that's important for you to understand that. And I encourage you to Google yourself every once in a while and say, is what I'm putting out there aligning with what I want people to see me as? Right?

It's a very simple activity, but it's so often realized that we are not necessarily coming across and we're not being received. Uh, like we think we, like we think we are and like we, like we wanna be. So it's the first takeaway.

Alright, we're done, we're done, we're done with the activity, right? Um, so first thing is your brand can and will change. I use the neurodiversity example, um, because my brand three years ago would not have included neurodiversity.

Most people in this room's brand would not have ever included any type of ai, but maybe it does now, right? As technology changes, as you change, as your life experiences change, your brand is going to change, but most likely your core values don't. So a couple things that your brand is not, it's not your resume.

Really quickly. People think, oh, my brand is, I worked here, I worked here, I worked here, I worked here. But what did you do?

What value did you bring? What do people remember about you at those place places? It's not aspirational.

It know what? I want to be the best looking person in the room, but Chris is in the room so I can't be that right? And so it's not aspirational, It's not your self perception, it's not well I think I'm this 'cause you guys just demonstrated what you think you are and what other people see you are are very different things.

And here's the big one, it's not the truth. People think, well this is the truth. So this is the reality.

But perception is always reality when it comes to your brand. And so just because you have done something, just because you have brought the value, that doesn't necessarily matter. There's a phrase that people like to use.

It's not what you know is who you know. I don't agree with that. It's not who you know, it's who knows what you know is what really matters.

So that's, that is the key. 'cause if you know a bunch of people and they don't know enough about you, like I know a lot of you now because I'll see your face and I recognize your face, but about 20 of you, I can actually say something substantive about you. I don't have much else to say about most of you 'cause we haven't had conversations.

So it's who, who knows what you know, pretty much like Yelp, right? Your brand may look different to different people and that's okay. And that's why last time we did this exercise, someone has something negative to say.

Someone said, uh, those who can do do and those who can't teach, right? And though their assumption was, I don't know what I'm talking about 'cause I teach it, right? I'm like, all right, well let's have a conversation, right?

But the reality is, is that's okay. Like your brand is not gonna be the same to everybody. And that just comes with the territory.

So why does your brand matter? Your brand is leadership. If your brand is that you are a leader in cloud security or agent ai, like you guys are now associating Tim Youngblood as a thought leader on AI security.

You did not before you got in this room, I'm sure of it. No offense to him. They didn't know to, right?

They didn't know what you know, right? But now you do, you view him as a leader on that. So any post he makes about it, any podcast he makes about it, any article he writes about it, you're like, well this guy is a leader on this subject.

You're gonna let him influence your thought process because you associated his brand with those things. Your brand is influence and influence is leadership. More so than anything, leadership is influence.

So lemme give you guys a story. Um, whenever I go in front of a board for the first time, I have a very specific expect set of expectations. Number one, I'm, I'm rehearsing like crazy.

I'm going over materials. I'm thinking about all the questions they may ask. But my goal, the first time I meet a board is for them to feel like they hired the right person.

That's it. I'm not trying to have them know everything about our security issues, what my observations are, where I wanna take the organization. 'cause most likely I haven't spent that much time there to have that kind of information.

I want me to leave the room and them feeling like they hired the right person. If, if I have that, the next time I come, they're gonna hear me and they're gonna respect what I have to say. So that's, that's how I approach it.

So the first time I walked into the boardroom at my current company, the very first question that was asked right after I was introduced by the CIO and he gave my resume, which they already had. 'cause you have to have the pre-read. The one of the board members said, I have a question before you start.

How do you find time to do all these things in the community, advise all these different companies and still be a ciso, which I'm hearing is one of the most demanding jobs and people are self-medicating because it's so demanding. How, how do you have the ability to do all those things and then help secure our organization, which we know needs, needs some work? And, and that I, I stepped back for a second and I thought about it and fortunately I did my homework and I responded with, well, how do you have time to be on four boards and be a CEO of your company?

And they stepped back. They kind of laughed about it. Like, all right, go ahead, continue.

But, but what happened was I didn't have to establish credibility. My reputation was already in the room long before I got there. They all Googled me.

Some of them took my course on how to become a cso. Some of them took my other courses. And so they knew me.

They had, it was credibility, there was trust. And so I did get to jump into like, well, hey, you know what? Since I don't have to spend the first 10 minutes, like I plan on spending it, let's talk about these things.

But it made life so much easier because they knew who I was before they ever met me. Whether you're going for a job interview, whether you're a consultant, whatever it is, if you don't think that people aren't Googling you, just like you're Googling them, you're sorely sorely mistaken. It makes life so much easier when you already have that credibility.

And so I talked about the boardroom impact. I work for a trash company. Trash companies typically don't hire the best talent.

And people typically aren't raising their hand to come work for the trash company. Just doesn't happen. But when I put a job post out there, literally within two days, I've had over a thousand applicants for every job that I've hired over the last year and a half.

Not because it's wm, 'cause mostly y'all don't know all the things we do. Y'all know it as the green trash cook company. But because people think they know who they're coming to work for, they, they, they know the leader, they identify with the leader.

And quite frankly, I say things that are against the grain a lot of the time. I don't want you to apply for the job if you don't agree with those things either, right? I'm okay with that.

If you don't have the same approach on things, if you don't think people first makes a lot of sense, and you think people first is a, you know, a a newfangled way of looking at things and you want to go old school command and control more power to you. I don't want you applying for the job anyway. So the people that are applying in many cases align with the core values that I've kind of put out there as part of my brand.

And people from all types of industries are applying only because they think they know what they're gonna get into. And that just builds organizational confidence. And it makes your life as a leader so much easier.

Because once again, you have influence. Once you have influence, it's a lot easier to get money. It's a lot easier to get partners, it's a lot easier to get support, whatever it may be.

And vendors, some in the room will see that you have influence and they'll throw their products at you almost free sometimes because of the influence that you have on the rest of your industry or the rest of the, um, the rest of cybersecurity in general. So it's, it's a lot of value, um, in having a positive brand. So let's talk about how I built my brand.

This whole thing is a retroactive look because Ira forced me to talk about this. This was never something I wanted to talk about. Um, but when I think about it, I did not build my brand on purpose.

What what actually happened was, uh, during COVID or around that time Kobe Bryant died, and I grew up in la I was 10 years old and he was drafted, he was like my idol. And I'm just online Googling. I'm looking at all of his videos.

And I was so impacted by the leadership lessons that he gave because I was thinking, man, his five year-old kid may not understand this today, but they can Google him 20 years from now, 10 years from now, and they can like, hear these powerful life lessons from their father. Like I, I wish my kids had something like that. So I just started posting, I just started posting on LinkedIn and guess what?

I got like five likes, seven likes, like no one really cared. Um, but that was okay 'cause I wasn't doing it for that. I wanted them to be able to look back and see their father did something substantive in his career.

And then, um, I did a conference and I was at Octane and this was their first conference. I can't remember the year, but it was the first conference they talked about Zero Trust. And I was on a panel and uh, that guy is Dr.

Zero Trust, chase Forester. I'll come back to that in a little bit. Um, um, but on that panel, I said a few things, but I actually got to build relationships in person in real life.

And, uh, the, the first, the second takeaway is in real life experiences, so much more impactful than digital. But your digital life still matters, right? And so today, especially because of ai, we have people that appear to be the brightest, the smartest, the most eloquent, the most articulate, the most knowledgeable with no typos ever.

And of course a long dash in the posts. And, uh, those are the people that have, you know, dubbed themselves as influencers and hoping none, you're in the room. 'cause I didn't insult you just now I'm okay with it, but I hope I didn't.

Um, and so that is what people are seeing and people don't trust that anymore. They're, they're looking for authenticity, they're looking for, for realness. And in real life experiences will help you figure that out really quickly.

I don't know about you, but I've met people in person that I saw online and they couldn't even form a sentence, right? And those are the people that lose their credibility and their brand is shot so quickly. But when your online presence matches your in-person presence, that right there is a credibility builder that will never be broken.

So that conference, it was zero trust and the question was asked, how are you getting your executives to buy into zero trust? And my response, I'm not the king of real talk like Ira, but I do try to keep it real. My response was, I think Zero Trust is a horrible name.

It has a horrible brand. I've been trying to build trust with my executives. Now I'm gonna try to tell them zero trust.

I believe in the concepts, the architecture, the principles, all of that. But I'm never using the word zero trust. I think it is the worst name we could have given it.

I said this to Doctor Zero Trust, chase Forester at the SANE Conference in front of like, like 300 people not knowing I wasn't thinking about it. I was just, you know, sharing what I'm, what I'm doing. And he was like, you, you do know my name is Doctor Zero Trust.

And I was like, oh my bad, you have a horrible name, but I like what you're pushing, right? Um, he like a year later came out with the Doctor Zero Trust podcast, right? So he still, he still rolled with it and, and it's worked out well for him.

But, um, the next lesson is when everyone zigs, you zag, when I did that, the whole audience was like, well this guy's at least gonna tell like he thinks he, like, he sees it, whether it's right or wrong, he's not gonna just try to fit with the rest of the crowd. And so many times people, oh, the hot thing is ai, I, let me just talk about ai. There's nothing wrong with that.

But if you're saying what everybody else is saying, nobody's gonna remember you or associate you with that thing necessarily. And so what actually ended up happening is I got a bunch of audience questions. I got a bunch of LinkedIn ads and so forth, and that really kind of started a little bit more of my ascension.

Um, but I also built a friendship with Chase. He was like, man, the fact that you had the balls on stage to say that to me, I know you're gonna tell me like it is, come join my company that I'm building as an advisor. And I actually joined his Zero Trust company as an advisor.

And, and long story short, I made 30,000 in advisor fees for telling him his name sucked. Um, and so there's, there's something to be said about using your voice to create conversations that aren't being had. When, when you do that, you become that brand, that theme that you're talking about, especially if we talk about it enough, and it'll separate you.

Because if you don't separate yourself, you're just gonna be a commodity. And so you're either a differentiated brand or a commodity. And what do I mean by that?

Number one, if you're a commodity, you look like everybody else. You sound like everybody else. You get lost in the noise that everybody is saying.

It's nothing unique about you. Unfortunately, there are gonna be people in this room that I forget. There are gonna be people in this room that I don't forget because you found a way to differentiate yourself.

Now, maybe others won't forget you, and that's fine. But if you differentiated yourself, there's this zero way that I'm gonna, I'm gonna forget you. And another thing, people try to find jobs or people want to demand higher salaries and so forth.

If you're like everybody else, I'm gonna go for the other person that is just like you, that's asking for less money. If you're differentiated, you can demand more. Whether it be speaking engagements, advisory roles, a job, you name it.

It's so important that you different yourself from the rest of the group. So let's do a little activity. Put yourself in this position.

You just joined the cruise ship. You didn't buy any drink packages. And they say, we're gonna offer you any one of these waters for the entire time on the ship, but you can't get any other water.

You can either go with the arrowhead at the top, the Dasani in the middle, the Kirkland brand over here, this nondescript glass bottle, or this nondescript square bottle. Who's going for the Arrowhead Ira was weird. It's all free.

It's all free. IRA's going for the Arrowhead. Who's going for the designing?

Okay, who's going for the Kirkland? Man? The, the Costco cult continues.

Who's going for the glass bottle in the bottom? And who's going for the square bottle in the bottom left? So this exercise went a lot differently than last time, but more people still going for the square bottle in the glass bottle.

And there's a reason for that. It's 'cause there's a story behind it. You can make some assumptions just by the shape and the form from something that you've seen in the past.

You can associate it with an experience that you may have had in the past. You see that bottom bottle, of course, you're thinking is Fiji, right? Like, that must be Fiji water.

I'm going for the Fiji water, even though it doesn't say Fiji water. I'd rather take my chances on it. The glass bottle who uses a glass bottle and puts cheap water in it, right?

That's like just assumptions that people will make. The story that you have, that people associate with you is so much more powerful. When you're talking to people and you're meeting people, you share a little bit about your story, how you got to where you are, they're going to remember you so much more.

And then you take that and combine it with the brand thing that you're aligning yourself with. You're going to be memorable. That is one of the differentiators, um, that you have is, is your story.

So when you get opportunities to share it, have a two minute version of it. Have a one and a half minute version of your story, and people will associate that with you. It is the core unique differentiator.

'cause we all have done some of the same jobs. We've all, uh, worked for the, some of the same companies. We've had some of the same deployments, same experiences.

But your story is unique to you. So how do you differentiate yourself? People ask that question all the time.

Like, well, what do I do? There's so many different things to talk about. How do I differentiate myself?

Well, it's not about you. It's about your audience. And so, what do I mean by that?

When I'm up, when I'm posting, I realize that the most powerful capability I have is to talk to the person that I once was. Whether it's who I was at 24, or whether it's who I was 15 years ago or even three years ago. You have learned so much in how much you share based off what you've learned is going to be the thing that separates you from other people.

So I literally talked to myself at 24. I was a leader for the first time. I I got my first CSA role at 24.

People always ask me about that. I was a horrible leader. I didn't know about people leadership.

I didn't know about generations. I didn't know about ebitda. I didn't know about anything as a leader at 24.

And so I say, well, what would I have wanted to know when I'm 24? If you go back and look at every single one of my posts and look at it through that lens that I just shared, it'll make sense. I'm just talking to people that are trying to level up in leadership.

Learn how to trust, um, learn how to, to deal with hard situations. Whatever it may be. Everything that I do, every class that I teach, it's about talking to myself at 24.

For you, it may be talking to yourself 20, you know, two years ago before you learned ai, there's always somebody where you were two years ago. There's always somebody where you were four years ago, six years ago, eight years ago, you name it. Another thing that's important, Ain't none of y'all perfect.

All of y'all have made mistakes. If you're a leader in this room, you've taken out a system on accident. It's happened.

It's come with the territory. Um, if you're new to security, you're gonna take down a system on accident. It comes with the territory.

It's about sharing the lessons that you learned from that. People always talk about like, oh, it's not failure if you learn from it. It's really not about learning from it.

It's about the retroactive. Look back from those lessons learned and how you apply those lessons. And Pixar has a rule.

They have 11 rules that they give to all their writers. But the number one rule that they have is that the audience should identify with the struggles more so than the triumphs. If you identify with someone's failures, number one, you have a different type of credibility, but there's authenticity and there's trust.

If I tell you five times how I sucked at something, when I tell you I was great at something, you're gonna believe it. 'cause I was willing to tell you when I sucked at it. So many people make the mistake of only talking about their successes.

That will get you lost in all the noise of all the other people that are always talking about all the great things that have gone for them. All right? So there's this wall.

There's obscurity and notoriety. When you have a good brand, a solid brand, a popular brand, you have notoriety. And when people don't know who you are, you're a commodity.

You're on the side of obscurity. And it's common for people to try to get from the obscurity side to the notoriety side. 'cause on the notoriety side, we're just pulling it all in.

Opportunities, jobs, speaking engagements. I'm gonna make an assumption here, Tim, but you haven't applied for very many jobs. They just show up, right?

I have not applied for a job in over 15 years, right? It's not about applying for a job. It's not about applying for advisory roles.

It's not about applying for speaking engagements. I didn't ask Ira to do this, right? And I'm happy he did.

But the reality is, is a lot of our speaking engagements except for R rss, a man I keep trying to apply and they don't let me in. I don't know what's up with these people. I don't know.

I used to work for RSA, but it's a different conversation. So everybody wants to get on one side to the other side. 'cause on the other side, life is a lot easier to be honest with you when it comes to those things.

So what do people typically do? They take that sledgehammer and they just hit all across. I'm gonna talk about ai.

I'm gonna talk about GRC engineering. I'm gonna talk about the fact that we do or do not have a, a shortage of talent in the industry. I'm gonna, I'm gonna talk about how much vendors suck or all the things that everyone's talking about over and over and over again.

And when you do that, you're actually not going to compromise the wall because you're taking a sledgehammer and you're hitting it here, here, here and here. That's not how you break past the wall. You break past the wall by hitting the same spot over and over and over again.

And what does that mean? Pick two topics. Make those be the two topics that you talk about over and over and over again.

Whether it be on podcasts or posts or articles that you write or conference talks. Talk about the same thing over and over again. And then you become the supply chain lady and you have a brand that everybody knows as associated with you.

And now she decides to talk about something else. She's already established credibility. She can grow her brand to be something else.

But she got past this wall from her precision on one topic. It's so important. And then once you get past this, you can add all the other topics in the world that you want to add.

But people need to know you for something to know this. This is one I call this person. So I did a poll on LinkedIn and I asked CSOs, how did you get your job?

Only 18% of them said they applied to it. And I think it's lower than that now, to be honest with you. It's, it's who knows what, you know, it's who knows what you've done and who, who knows what you've stood for.

And that is all your brand. Even internally, we had this talk on the deputy CISO panel. If your brand isn't strong, they're bringing in an outside ciso.

Even if you're the deputy ciso, your brand is so important. So I ask people all the time, what is holding you back? It's fear.

I'm not the guy that posts on social media. I, I'm not that person. I don't wanna take pictures of my food.

Like These are the things that people say to me all the time. And I get it. I'm an introverted person and you probably wouldn't believe it until I, until I say it to you, until you realize I'm the one that ducks off before everybody else at night and has to recharge because all this people is people me out.

Right? Um, but the problem is you're looking at it in a very selfish way. Your fear of posting is probably one of the most selfish things you can do because when you post, you're helping somebody else out.

That's all it is. And so I guess I'll end it with, if your only job, your only attempt is to help other people, you're always gonna succeed. And if you start helping people, you're gonna build a brand in whatever way you've helped people.

Happy to take any questions. Uh, what's your thoughts on over exposure, especially on LinkedIn? I don't know if it's possible.

So I ask them for a reason. There are people that talk to the community and they're like, uh, I don't know what I think about X, Y, Z. 'cause they're always posting something that seems to create a negative impression As well.

It can if they don't have the in real life experiences to back it up. Right? If it's all talk, yeah, it's all talk.

But you meet 'em in person, they talk in person, they're doing the job in person. If you talk to anyone that works for me, they're gonna have the same things to say that the people that see the things online. And that's important.

Now, if you go in like that, that guy's an a*****e, right? Well then yeah, that's gonna completely degrade the brand very quickly. So you have to back up what you're saying.

Something Derek, that I would say to that is, uh, I've been approached by I think three different companies that represent, um, uh, uh, they're looking for influencers on LinkedIn. Oh yeah, me too. And Right.

And I signed up just so I could see the, what information they get people to post about. So I can see who in my network is posting to get 50 cents a click and that, but the thing is, the people who are, who are just posting to post, they get a reputation as not being serious in the industry. That's true.

Right? And, and you don't That's true. You know, people don't, but then some people are just like, you see 'em post almost every day for just three or four.

Yeah. Nonsense. And it's like all you did was just like regurgitated something.

You saw someone else post. No one's gonna take them seriously from a branding Perspective that, that's accurate. And I, I have been approached.

'cause you know, if you have 20, 30,000 followers on LinkedIn, people are gonna wanna say, Hey, post this on behalf of our product and, and we'll, we'll pay you. My, my response is always, people trust me 'cause I'm not pushing products. Right?

Right. The minute I push your product and make whatever I make on it, never do it. I lose everybody else's ear.

Right. And so for me, it's, it's not worth it. Wherever you are financially, you do what you gotta do.

Of course. Um, but uh, yeah, that's my statement. Yeah.

I, I was gonna mention like, people tag me because they know that's my focus and they want, you know, that assistance with being able to expand, um, their, their network and that visibility. And I'm selective on those. I mean, I don't repost everything, but for those that, you know, I do or I see something that I saw posted and I'll repost that to make a new connection, it really helps sort of broaden it.

But I am selective of what I repost. Yeah. I'll untag myself from post Me too.

Like from the wrong people. Absolutely. Whether It be from RSA or whatever.

Yes ma'am. My years told me that one person who got very successful, that I actually hired a PR firm via brand Wyoming. So how do you, how many people do you think do that?

Um, I just heard recently, by The way, I didn't, She, so she said she heard that some people have hired PR firms to handle their branding. Um, I heard recently, I can't back this up, but I can find out pretty quickly 'cause I do work with LinkedIn, um, that LinkedIn is clamping down on people posting on behalf of other people. Mm-hmm.

Um, because LinkedIn is gonna start losing its credibility and there will be a new platform that pops up that will replace them if they allow that thing to continue to happen. People say the new Facebook, There's a lot of people that refuse to let that happen. Like take that post to Facebook.

I see that all the time. Um, but there's this weird world of work life balance, work life intersection where, you know what my joyful thing with my kid is part of my work life. 'cause I was sitting at work when that thing happened, right.

So I don't really get into those battles. I, um, but for the most part it's still been very professional for me. Alright.

This is a, a different kind of question. Um, so I do have a history and I do kind of have a bit of an image online. Well now there's a new guy in Southern California with the exact same name that's 20 years younger that's trying to ride the coattails.

And so he is going down and he's more of a salesy guy, guy trying to take all of the things that that security guy has done to sell the things in this space. So what are your, what kind of approach? I mean, I haven't met him, but I haven't sent to anybody to go beat 'em up or anything.

But, you know, I mean it's just, it's like, it's been brought to my attention many times within the last year and a half. I mean, will the real Dan Meacham please stand up? Is the first thing that I'm thinking.

That's right. Um, yeah. Exact same name except for Yeah, you're right.

Middle initial, middle initial. That's why. Um, so LinkedIn has the ability to, um, be verified.

Alright. Um, it is like 19 bucks a month I think it is or something like that. I can't remember what it was at one point.

It was, I think it might be free now, but if you verify yourself, then people can know that you're the legitimate Dan Micum. That's, that's one thing that you can, um, if they're call, if they're saying they're you, like, I work at this and I do this. Or if they're just saying, I'm a security guy, you're a security guy.

I don't know if there's much you can do about that, to be honest with you. Right, right. Yeah.

My, I was, I was the, the other person. 'cause my name is Sean Harris. Yeah.

I show up on, on IC swear thing and there's all kinds of people. You wrote my book and Ask, asking for my picture with me. I'm like, do you know who I am?

I work at nasa. I'm not like, I'm not her. She's not alive in her hairs longer, Uh, as a vendor, when you see a profile of a vendor, what is the reputation you're looking?

Not that I'm gonna build my personality according to what you answered, but I might Yeah. In general, what is it that you're looking As a vendor, if you're adding value to immunity, that is a pull approach, right? Because I'm gonna see what you're saying and I'm gonna pull you into no more.

And how are you solving that problem when you're pushing? That's when we put up the wall and that wall is really hard to get past. And so anyone that's just adding value to the community, so let's just say when the sales loft thing happened, right?

There were vendors like with us, that wouldn't have happened, right? We don't want those vendors. We don't wanna talk to those vendors.

We don't wanna see those vendors. Those are ambulance chasers. But if you're like, Hey, here's a, here's our analysis on it, you know, enjoy all day, we're gonna take that in.

Yeah. Hey, we created this free tool, or Hey, we're gonna give you a free POC, don't have to go through all the paperwork, just download this, whatever. Like, we love that.

But the minute you capitalize on someone else's hard day, you've, you've lost a lot of us very quickly. And there's a CISO Slack channel, it's real uhhuh, it's out there and a WhatsApp. And we talk about you often About me personally, about you.

We Don't Think Tim isn't shaking his Haas and no, because he's like, I'm a CISO and I'm a vendor. Sarah, can I make a comment On that? Yes, sir.

Because I, I am notoriously evil to vendors. And as you know, being outspoken and what I always tell vendors, and frankly, it's like what you're trying to say, like to us is like I tell vendors, and I'm paraphrasing, be a magnet. And most, and what I mean by that is you put out information.

Like if you connect with me and I accept it, I'll see your stuff come across my fee and I'll choose to engage with valuable materials. I'll choose to go ahead and follow up with you. But more important, and I'm assuming it's the same when I was at Walmart, coming to me is a pretty big waste of your time.

Yep. Because the people who make the decision on 99% of products inside an organization are the people doing operational work that you're not gonna give a squishy toy to at RSA, or at least I find most vendors ignore the people who are out there generating needs for products or determining needs for products that they will then elevate to us. And so if you are a magnet and generate positive value, the people, not us, maybe will engage.

But the people who need your stuff are the people who are gonna look at it that you are not engaging with. And that's how I see it going. Most valuably be a magnet for, you know, everyone.

100%. I, um, I don't make tool selections. I tell my team the strategy, they decide the tool.

I think it's like not cool to force them to use a tool that they don't like to use when there's other tools that are out there. So they get to make their decisions. Yeah, Just, I just want to share my experience.

A few years ago, I was too scared of posting on LinkedIn. I was thinking too much about my likes and followers and stuff, and that was, you know, holding back to myself of putting the real value stuff there. Right?

And then, uh, one of my mentor told me, um, if you trust yourself, if you know you have a service to provide or vendor to provide, do not attach yourself to the outcomes. And by default things will fall over. Right?

So I'm, I'm posting out now, don't get the likes and follow, but automatically by default you will get it. So just, you know, outcome is one thing. And, uh, providing value is one thing, A hundred percent.

Uh, number one rule I have is add value to everyone I meet whenever I can. If you do that, that'll be your brand, Mr. Mr.

Bryant. For, for the executives or the leaders that are looking for new opportunities because of what's happening in the economy, and they haven't been on social media any have ability moment because they've been at their job for seven, 10 years and they just, you know, I, I'm not, I'm good. I don't worry, not worry about it.

What would be the first step that you would suggest they would take to help them identify what direction they need to go in to start helping increase their visibility of their brand? That they don't know exactly what their brand is? Well, most likely that person doesn't have a complete LinkedIn profile off the top, right?

Right. Like, what are the things that you've done, some of your experiences, um, like please don't put phrases like hardworking and experienced. Like none of this is gonna work, right?

Like, like, let AI come up with something much better for you. I trust, trust me, it will. And just, just build out your, your LinkedIn profile off the top recruiters and everybody know when you've made a change to your LinkedIn profile, every time you edit your profile, it moves to the top of the list for recruiters and everybody else because their analytics show updates.

And so that's, that's number one. I could have given all kind of LinkedIn tips, but, um, just editing your profile once a month will move your name to the top of people's list. If you've changed nothing else, like, you can literally put a period in and take a period out and you move up to the top of the list for, for people, because they're part of the analytics is who's updated their profile recently.

That's it. Right? And so just updating your profile with something relevant will then at least introduce to people, like, here's some things about me open to work and all those types of things.

Will, will also help. Is that, is that quoting? Like if you're posting and you have any No, no.

It just recognizes profile changes. Yes. And I see the word end, so that means I'm gonna stop.

Usually Ira comes out and pushes you, but I'm gonna follow her. Thank you. Hey everyone.

Welcome back here to Text Drug tv. I'm really happy to have Olivier Blanchard or Blanchard if you're from the US and you want to just lowbrow it. But Olivier is a, an analyst with, from, uh, with Futurum.

He, I've interviewed him before and he, he writes a lot. com covers a lot. He's gonna tell us about it.

Olivier, welcome to Text Drunk TV. Again, it's great to have you on. Hey, thanks for having me.

It's always good to be on. Thank You, Olivier. For those folks who may not be familiar with your area of expertise and kind of, you know, your beat that you cover, give them a little insight.

Yeah, so I'm a research director with, uh, the Futurum Group, uh, as you mentioned. And my primary focus is AI devices. So basically any kind of physical ai, any manifestation of AI that's not in the cloud.

So a smart ring, a smartwatch, a PC that has AI built in, uh, AI enabled phones, all the way up to robots and smart speakers, smart TVs, smart cars, um, anything that's not bolted down, uh, in a data center that has a, some kind of AI capability is my focus. So it's a very wide and broad portfolio, uh, very interesting one, uh, but one that also sort of like, uh, helps map how some of the same players keep turning up in the, in, in different places, uh, especially on the semiconductor Omicron. Got it.

Um, let's, well, first of all, I, I mentioned the Futurum group is where people can follow you and your research and notes and so forth. And, um, I, I wanna make sure people get that before we even go any further. But Olivier, I wanted to speak to you today about a company called, I believe it's Media Tech, right?

That's M-E-D-I-A-T-E-K. So I, I have to admit, this was a, a company that was new to me. That's Fair.

That doesn't mean anything, right? Because God knows, I don't know everything. But for our audience out here that may not be familiar with Media Tech, how would, how would you describe it to them?

Media tech's a really interesting company. Uh, they're definitely not a household name. They're a little bit the way Qualcomm used to be 10 years ago, uh, where everybody might've heard the name or a lot of people might've heard the name, but it didn't quite know what it did.

Um, media Tech is, doesn't even have that kind of level of, of name recognition, at least not in the United States. I think overseas, uh, on global markets, it's, it's definitely more, more visible. Um, but essentially it's, it's a company that, uh, is that kind of started in, in the, or made its bones in the, the mobile world market.

Um mm-hmm. Essentially it's, it's the world's largest provider of smartphone chip sets, which you might not have known, uh, at least by volume and, and definitely a, a global leader in, in cross platform semiconductors. So they can be found, found in a lot of places.

Um, I would say that, that the majority of their revenue, um, is, is from mobile. I think it's just over 50%, 52%, 53% of revenue is mobile. Uh, and then Smart Edge platform.

So basically everything else is about 43% of their revenue. Um, but they have one particularity, um, which is that at least up until recently, they were essentially a, a sort of like, how do you, how, what's a a polite way of saying it, they were sort of like the low to mid-range semiconductor d uh, um, provider for mobile handsets, um, in terms of price points, right? They were sort of like, your, your budget phones, Your mass market, Your mass market phones, right?

Um, two Qualcomms sort of like super premium stuff. And in recent years, what we've seen them is actually get out, well, they're not getting out of it, but they've expanded into that premium segment, but they're up market Coming up market. Yep.

Yeah. Yeah. They're coming up with some really good stuff.

Um, again, not a huge footprint in the United States, but overseas. Internationally, uh, they definitely make their mark and they're, they're a company to watch. You know, that's a very common sort of strategy.

You see it in a lot of maturing markets. Olivier, you have, you have folks who come, let's say from a bottom level up that were aimed at an S-M-B-S-M-E kind of markets, if you will. And then you have the big enterprise guys who want to come down market.

You have the down market guys who wanna come up market and somewhere they meet in the middle, right? And, and, uh, and that's where you determine market shares. So it sounds like media tech though, is ascending in, into up market and, and look, it's, a lot of people tell you it's a lot harder going up than it is going down too sometimes, right?

It, it can be, especially when those markets are obviously a premium is smaller than mass market. Yeah. So they can be the number one, you know, global, uh, smartphone, SOC shipments company.

They can be 10th largest global semiconductor company by revenue in the world, but still struggle to, uh, to penetrate those markets that are very mature, uh, and, and very well protected. Yeah. I'm reminded of a conversation I had with a very good friend of mine, uh, Byron Nicoletti.

Byron is the CEO founder of a company, company called People Cert, one of the largest training providers in the world. They own and operate the IL uh, it, you know, service management language. They also have DevOps Institute and some others.

But Byron always told me in a business, you like a business that has a very wide base Yeah. Rather than just the top of the pyramid. He said, I'll always take the base of the pyramid versus the top of the pyramid.

And there's, you know, that sounds sort of like what we're dealing with here. Perhaps there is a base of the pyramid versus the top of the pyramid. Uh, it's nice to be up and down the whole pyramid though, isn't it?

Um, It, it is. If you can Yeah. Ask Apple, right?

Yeah, yeah, exactly. Exactly. Who by the way, announced they partnering with Google for, for Siri now, so it's gonna Be, yes.

That's a good thing. Gemini, under the hood, under the covers. I think I, I think a a a Google powered or Gemini powered Siri is, is good for everyone.

Absolutely. Well, my security friends may have something to say, but it's another story. Olivier, let's go back to Media Tech though.

Yeah. Um, they recently had some news. I know you're working on some new research notes and so forth, but, um, without letting the cat out of the bag of anything we're not supposed to talk about, what can you tell us about new news over there?

Right. Yeah, so I'll, I'll leave the, the top secret stuff. Top secret.

Uh, but one thing I, I would, um, um, I would like to focus on is Media Tech's partnership, increasing growing sort of connective tissue with Nvidia. Um, and I wanted to dispel any rumors because they've been working on so many things together that, Hey, is Nvidia gonna acquire media Tech? Is is like, is there something going on?

As far as I know, no, and I've asked the the question repeatedly. Uh, the answer is always categorically no. And it's not no wink wink.

It's like, no, no. It's like the, the two companies, uh, are very happy to be independent and doing their own thing. But there are affinities there, there, there are things that meet, detect does very well that Nvidia doesn't, and there are things that Nvidia does very well that Media Tech doesn't.

And so we've seen them partner on a number of, uh, of projects, uh, lately. And so the, the wine that's especially dear to my heart, because I spent the last year and a half focusing so much of my energy on the A IPC, um, uh, segments, and how, uh, even though it's been a little bit disappointing in terms of use cases, the, the PCs being, uh, empowered by AI capabilities, that's, that's definitely a big inflection point for the PC and personal computing. Um, even though it, it might take a few years to get us to a point where PCs truly operate like AI enabled machines.

But anyway, um, media Tech and Nvidia have been, have been working together on this. And one of the things that that came out last year that I think was a bit of a game changer for the AI c segments, um, was NVIDIA's DJX Spark, uh, platform, which is basically a, um, a Blackwell super chip powered desktop that looks like a Mac mini. It just looks like a, a little pallet, right?

Um, you can put it in a bag, it's small enough, not fit in your pocket, but fit in a bag, you put it on your, on your desktop, and, and you have this little AI supercomputer that has a GB 10, um, NVIDIA chip in it. But the, I would say at least 50% of the board looks like media tech ip. And so this, this combination of NVIDIA and Media Tech shows how these two companies can, uh, collaborate together to bring something very unique and very important to that space.

And I'm already seeing them also partnering, uh, in the automotive market, which isn't, um, as big for semiconductors as you would think it should be, especially since intelligence is like getting into all these vehicles all the time. And they can do more things, whether it's automatic or self-driving, or just all of the sensors and sensor intelligence and cockpit intelligence with, um, assistance and agents making their way to the cockpit where you can talk to your card and it talks back. Um, it's, it's still a, a, a very nascent market, but it's growing very quickly.

And, uh, media Tech has a platform for automotive called Immensity Auto, uh, that they're working with in conjunction with Nvidia. And so again, you see the power and the market power of Nvidia, uh, in its name recognition, sort of attaching itself to media tech, which is kind of not very popular, but this workhorse that can scale really well across all of these different areas. Um, and one of the particularities of, of media tech and where it matters with Nvidia is Media Tech is primarily just an arm based architecture semiconductor company.

They use arm, uh, and so does Nvidia. And so there's this natural sort of architectural affinity there where, um, arm on arm arm with arm, uh, low power, high performance works really well for these types of Edge AI applications like automotive, pc, smartphones, uh, IOT and, and even, um, as, as it's becoming like the, the theme of the year after cs, uh, robotics, robotics is gonna be this year. So there, I haven't seen any major Announce Physical ai.

Physical ai. Yeah. I haven't seen a, a lot of major announcements from media tech regarding physical AI robots.

Uh, but I'm, I'm sure that will come, uh, very shortly. There's, there's no way it doesn't. Excellent.

Olivier, thanks for giving us this sneak peek behind the curtain, if you will, on Media Tech and bringing it to our audience's attention. We appreciate it. Keep up the great work.

As I mentioned, if you wanna read Olivier's coverage of media tech as well as all the other great, I mean, his whole AI empowered device thing, what, what a great time to be in that space. Right? com, check it out there, Olivier.

Thank you. Hope to see you back here soon on Tech Trunk tv. Thank you.

Thank you. That complexity takes us to, okay, you've got some great ideas about where you want to take the network. Um, you made some decisions about some products.

What did you, what did you select going forward, and what did, what did this do for you in light of the requirements that you all saw? Okay, so, so our requirements was to have a modern design implementation operation cycle, you know? Mm-hmm.

So we, we need, we use NetOps techniques mm-hmm. Where we, where we're treating our network as code, right. And, and when treating the network really as code, um, I mean network as code, the, the concept itself has been there for a long while.

Sure. But it's been always, like somebody will tell you, write your network as code, and let me write a translator in the middle to enable this network as code to be understood by, by by my tool. And then, and then you have the tool understanding this network as code, but it never tells you if it really implemented your, your code or not.

Sure. It says it's accepted. So we had this idea of that our network, uh, our network as code should, if it's really network as code, then let's treat it as really code, let's treat it and, and put it through a software development cycle.

Mm-hmm. Like we're, we're gonna be more like software engineers treating our network, uh, as really software where we have a production environment. We're, we're taking, we're we're putting it through a versioning system like Git, uh, taking branches, uh, implementing changes in the branch, testing them, feeding back into, into production.

So really this was our vision to have, you know, a NetOps a process or, or or architecture that treats network as code. But we really wanted to, to have a tool, a tool that really understands the code by as code mm-hmm. And not translate it onto something else.

Sure. Why we wanted to feed, we wanted the tool to feed us back information about our real code. Our real code is our real code.

What is implemented? Are you tracking it? We to have this feedback loop.

Mm-hmm. So we didn't want any translators in the middle that will lose information. Right.

That will, that will lose this, this feedback, pure feedback loop. So, and, and that's where we found our, uh, really this, our, our new tools have enabled us to do this. Our new configuration management platform enabled us to do this, treat our net network as code, keep it as code, and feed us backed information about our code, how it's implemented, any deviations, any, any drifts, things like that.

And, and just to be really clear, you know, you, you went down the path of going with Nokia's Sr. Linux as a switch in router operating system and event driven automation or EA as the, the management and automation platform. Yes.

And there's some really interesting connections there from a Kubernetes perspective, from getting away from having to go testing with physical equipment in the lab. Um, I know you can speak to at least both those issues. What, what did you get out of that, you know, out of, um, Sr Linux with e DDA to drive that forward?

Okay, so, so IDA enabled us to, to build, I was talking about the, the, you know, the software development cycle mm-hmm. Where you have a production environment and you have a development environment. Uh, we really wanted our, our development environment to be a copy of our production, but we don't have to, we don't want to build a lab mm-hmm.

That is a development environment. I mean, we've got data centers with hundreds of, of switches in there. Sure.

And we didn't want to go and build a lab or build a scale down of the lab. Sure. A scale down can, can sometimes work, but sometimes in cases where we do migrations, for example, right.

We, we really need every, no, we need to understand every node. Yep. So, so either provide us with this idea of a digital twin mm-hmm.

A true digital twin running the same code, same everything. And the nice thing about it is the intent, the intent, the, the network intent that I'm feeding into the production environment. Mm-hmm.

I can feed the same network intent to my digital twin. No, no changes at all in anything. I don't have to to mess it around or tell it or massage it to fit with this digital twin.

Sure. Just the same code. Put it in the digital digital twin, and then I can play with that code.

I can, I can make changes in that code, I can experiment, I can run traffic on, on this digital environment that is a true replica of production. And once I'm happy, then I can merge into production knowing that my design works well. So I can, I can test things that, you know, routing to low balancers to firewalls, uh, different server configurations.

I can do that in the digital environment. Yep. Uh, and, and feed it back into production.

So, so, so either this idea of a digital twin was very powerful, very, very powerful. And especially in migrations, it was, Yeah. You're using the same control plane that you're using on the physical switches and you're feeding it the same configs and to be able to play what if in the digital twin.

And that's a, that's a huge enabler. Right. And, uh, and The nodes are running the same code as the production nodes as the real SR.

Linux notes. Yep. And you could even change that rev of code.

Right. And still see those changes reflected in the digital twin. Yes.

Yep. Hey guys, thanks for the throw. We're here with Phil Menez, who is vice president of Go to Market Execution for Vast Data.

And we're talking about this flash memory crunch that we're all starting to see. And what are the forces behind all that, Phil, welcome to show. Awesome.

Thanks for having me. So what is going on here? I mean, I think we all kind of generally understand that AI is somehow at the core of this whole thing, but walk us through how does this crunch occur and how is it manifesting itself?

And more importantly, is it ever gonna go away? Yeah. Some great questions.

So I think there's, there's obviously a handful of things driving this. AI consumption is a big one, but there's also the fact that we had, uh, an HDD shortage as well. So we saw a lot of customers looking to high capacity flash drives to address that shortage.

And I think that drove a lot of great modernization. Customers are getting savvier to the idea that they want to be able to have that data on fast access. So that's a piece of it.

Uh, and now we're seeing more and more things in the AI world just driving consumption, right? We just had, uh, jenssen's big announcement around the idea that we need to start storing more of this context on flash. That's driving a ton of consumption as well.

And they do have the traditional drivers of growth that media is getting much more rich. And we are seeing customers get more sa savvy to the idea that there is more value in data and they need to start being more creative about what they capture. So it's really coming at the industry from a lot of different angles.

And then the final question is how long is this going to last? And we think it's going to be something that hangs around for the next 12 to 18 months that customers are gonna have to deal with. Just, it's gonna be difficult to get their hands on sssts and discs in general.

Do we need just more manufacturing capacity? Is that what we're waiting for? Or is some sort of part of the demand equation gonna change?

I think we do need more manufacturing capacity. Ultimately, we're gonna get into a, a place of the world where, I don't expect this to slow, but I also think that there's a lot of opportunities to drive more modern technologies, right? There's still a lot of investment in technical debt where customers are bringing capacity into their environments and just not getting the full value of that capacity in the way they could if they started leveraging more modern platforms like Vast.

Mm-hmm. You kind of alluded to what Jensen Wong was talking about with context. Explain how that manifests itself.

Am I just really caching the prompts more aggressively on my SSDs? Or what kind of data is going into that motion to give people the, at least something that feels like a real time AI experience? Right, Right.

So what happens is I ask a question to a, a model and it looks at what I've asked. Maybe I'm adding a document, right? We're seeing more of that.

I'm adding a document, maybe it's a video, right? An audio file. And I wanna be able to interact with AI around that document.

There's a lot of context that gets stored there. And ultimately you either have to store that context on memory. I have to potentially recalculate a lot, which is really expensive in GPU cycles.

But to your point, also creates this lag in user experience that can be very annoying. So now the idea is can we be more intelligent about storing that context on flash to find the best of both worlds, right? Where I have that context, I can have these long conversations, I can, uh, store context on things like longer documents, and at the same time, I don't have to recalculate very, very often.

Right? So that improves both user experience and it reduces the cost of ai, right? Customers can't just keep, just keep throwing at this problem in a way when it's really to your point, a memory or storage problem.

Hmm. Will some organizations start to hoard some of these types of drives because they'll see this, um, situation evolving and they'll exacerbate it even further by going out and buying more stuff earlier than they needed and therefore making it more difficult for everybody else. Absolutely.

We are seeing it happen in real time was vast. Uh, right now, I think what we saw was that the, the really big buyers, right? You think about the AI labs, the cloud, some of these, uh, companies that probably have more of a direct line relationship to the actual suppliers and manufacturers than maybe a traditional enterprise customer, have seen this problem coming for a a few months now and have been bracing for it.

Now we're seeing more and more customers become savvy to the idea that they are gonna run outta capacity that prices are growing. So we're already seeing that boon, right? I think what's available is going to be chewed up really quickly.

Mm-hmm. And is the memory that's used to drive these things are also in short supply as well? It seems like, you know, I'm hearing reports where, uh, out of consumer technologies 'cause they're just making too much money and there's too much demand on the server side from the enterprise and the AI folks.

So is the whole supply chain kind of changing A hundred percent right? The raw materials are, are in limited supply, right? I think we're seeing now, um, you know, there's different ways, right?

If I'm using TLC flash for the same raw materials, I'm storing less data than I am a QLC flesh, right? And then obviously that business problem comes into play. So it's a matter of how these technologies are being used, legacy technologies, kind of getting less for the raw materials, legacy technologies not being as efficient.

So it's really across the board and I think there's, there's going to be a lag in how we can pump supply back into the market to catch up. Are you at all concerned maybe that we'll get to the point where somebody will build an application and then somebody in it will come to 'em and say, we have to postpone the deployment of that 'cause we don't have enough infrastructure to support it and um, we'll put you on the list and there'll be this bigger backlog? Absolutely.

I think customers are gonna have to make tough choices for the next 12 to 18 months about where they invest. Uh, I think depending on the technologies, customers are gonna have those tough conversations around what data do we really need to keep? Can we delete stuff?

Can we park it somewhere else? Uh, what's active? I think customers are really gonna have to be creative, um, in, in just looking at what they have available, what they options are for what they've got on hand.

And to your point, what projects are priorities? Is this therefore gonna force some, uh, better shall we say good housekeeping around the whole data management motion? Because I think part of our problem is, you know, we have a lot of data.

We store a lot of data, but we don't always manage it so well. I think so in general there's going to be more scrutiny, right? On policies and keeping data.

But I think in the world that we're living in, and I've been in storage for a while, right? We've all kind of known eventually there's a lot of data. I don't wanna let it go because there's value in it.

We haven't been able to tap into that value. Now that opportunity's here with ai and then it's kind of brutal that as AI is really becoming something that feels production ready, that now we don't have the capacity to drive it. That, you know, our opinion is we would really like customers to take a first step at can we more effectively use what we've got and be more careful about what we're doing with the capacity that we bring in with that precious amount of capacity customers will get their hands on.

Can we be more intelligent around how we're using that as opposed to deleting data that's gonna have value in a year? 'cause that's gonna feel really tough for a lot of customers if, if that ultimately becomes the case. And even some of the tools they might need to understand what data has value, kind of needs access to that data before we can make those decisions, right?

So it, it's a very tricky time that this is all converging. At the same time, We need to get better at utilizing our storage systems because of this issue. Maybe we'll need to figure out how to make it easier to share those SSD drives across multiple applications.

'cause sometimes I feel like when it comes to IT infrastructure, you know, we were all out that day when we were taught that learning the share is a good thing. Uh, I think absolutely right. That is, uh, something that's near and dear to us at Vast is that when you look at, you know, a traditional enterprise like say like a global bank that has some of everything, they just have so many different products in even an enterprise data storage, a lot of times multiple products and backup multiple products and data analytics.

Now ai, you're building kind of new islands, existing islands in HPC, all those islands create a lot of waste. And you are gonna see customers looking at their environment and saying, I've got capacity over here, but I can't really use it well for this application over here. So I think it is gonna drive a lot of just focus and rethinking on how I want to plan, because now you're looking at it and having to plan at each individual layer of like every single application, how that application is going to grow and trying to get it exactly right is impossible.

So the only way to really get past that going forward is to have platforms that can support a variety of applications. And then I only have to get really the macro number of my capacity demand, right? As opposed to getting it right 20, 30, 50 times, whatever it is for these individual systems that customers have.

What is that thing you see people doing today that just makes you shake your head a little bit and go, folks, we need to be a little bit smarter about how we're using these systems. I, I think, and you know, I don't think we can, you know, it's not like, oh, they were wrong, but when we look at environments, there's a lot of systems in, you know, an enterprise environment where you're gonna find data triplicated or you think about, you know, kinda legacy data analytics, HDFS, even things like, you know, log analytics, Splunk, where you're gonna have a lot of triplicated data and thinking about the idea that, you know, I can buy 10 petabytes of data and get, you know, store three petabytes of capacity on it. I don't think it's like that's, I wouldn't blame the customer for it, but I think now you're realizing that some of that technical debt is just such a big drag on capacity and I think there's a lot of low hanging fruit that, you know, you just have to look at the calculus of, of what you're buying and what you can store on it.

And those technologies have been, are, are, and have been a really big drag on that capacity utilization. Do you also think there's a certain amount of data hoarding going on? I mean, people are just kinda, uh, storing everything in anything without much regards to whether they actually need it?

I think So. Uh, but I also think it's, I think across the industry you find a lot of customers do struggle with the idea of what do they actually have, right? And I think there's now a lot of uncertainty around what has value, what doesn't.

Um, so in our opinion, right, we, we love capacity in the world that, um, you know, we would like to see more intelligence put into that process, right? Of understanding what data you have, understanding where there's value, and I just think it's very difficult for customers to make those decisions. So I don't know that it's hoarding.

I think it's almost, uh, your kind of paralyzed by your capability to actually get insights into what's out there. Mm-hmm. One of the things I also hear is that people realize that they need somebody who functions more like a data engineer rather than say a storage administrator, but they don't seem to be able to find enough of these data engineers out there.

So is part of our issue is that we just need more folks who are savvy about how to manage data programmatically versus just kind of storing data and, you know, making sure that there's enough capacity on a drive. I agree. I think a lot of, a lot of environments are very much anchored to the infrastructure as opposed to looking at the data, right?

So we spend a lot of time talking about data pipelines and how data flows through organizations, and there's just, you know, there's a lot of muscle memory that, you know, I need a system over here that captures data, right? That I'm gonna move it over here to do some data prep if I wanna analyze it, or, you know, do faster queries. I've gotta have it on a separate platform.

So there's a lot of muscle memory. There's also a lot of organizational challenges in that you have so many different groups touching these things that to try to make a wholesale change to understand how data can flow through our organization more effectively, you need a lot of people on the same page, Right? Who's leading that conversation?

Because, you know, we've always seen CIOs core to this, but recently we saw the rise of chief data officers and chief AI officers, and there's all these lines of business folks, and sometimes I wonder if there's too many chiefs looking at all this stuff, but who's taking the lead to help resolve this stuff? Yeah, I, I do think the rise of the chief data officer and chief AI officer, and we engage with customers where those are merging and we still see worlds where those are different roles, right? Uh, but ultimately what we've seen is in my conversations, even the infrastructure people are getting savvier to the idea that the demands coming down from these folks, right?

The chief data officer, chief AI officer, who are getting more funding than infrastructure teams are getting influenced. They are, you know, really very much tied to these critical business issues are challenging the infrastructure folks to realize that they probably can't build things the way they've been building them and meet those needs. I think that's really something that's coming to bear and, and we're seeing, you know, even more than it has been these data teams driving more of that strategy.

And I think we are seeing customers get savvier to the idea that it's not about data storage, it's about the actual data itself and what we're trying to do with it. Mm-hmm. All right.

It's early in 2026. Get out your crystal ball. What's your prediction for the coming year?

Uh, I think this, even with the challenges, this is the year where you really start to see inference at scale in the enterprise. And I think it's gonna take us about a year to get there. But, uh, AI has very much been something where when we've, we've seen a lot of the spend over the first kind of few years of this AI boom, a lot on training.

Now we're seeing some more inference at scale, I would say, in, you know, the re tech savvy companies, the leaders. And I think over the next year you're gonna see more of that bleed into the enterprise. I think, you know, in the mid-market, that's still gonna feel out of reach in terms of some of the areas they're gonna lean on, um, SaaS solutions to really achieve ai.

But more and more, I think by the end of the year, you're gonna see more inference at scale in the enterprise, um, kind of realizing some of the potential that we've been talking about for quite some time. Yeah. Hey, folks, you heard it here.

No matter how advanced it gets, it seems to keep coming back to one thing. It's all about the data. Hey, Phil, thanks for being on the show.

Awesome. Thanks for having me. All right.

And back to you guys in the studio. Enterprise AI applications need a solid data foundation bringing together disparate data sets in a secure and flexible manner. But despite years of effort, most businesses still have a diverse data environment.

Before we will see the value of AI in enterprise applications, we have to solve the challenge of data access. And that's what we're discussing today with Ken Yagen of cdata. Welcome to utilizing ai, the podcast focused on practical applications of artificial intelligence from the Futurum group.

Each episode brings together diverse perspectives to explore news and use cases in the ways in which AI is transforming enterprise IT and the industries it serves. I'm your host, Stephen Foskett, president of the Tech Field Day Business Unit here at the Futurum Group. Before we dive into the discussion, let's meet who's on the panel today.

Hi everyone. Brad Shiman. Um, good to be back with you.

I am the VP and practice lead for data integration, excuse me, data intelligence. I, I'm already thinking about chatting, chatting with Ken today, uh, of data intelligence, analytics, and infrastructure here at futurum. And, uh, it's, it's my pleasure to join you guys.

And we have a, an exciting guest on, I'm going to introduce him now. His name is Ken Jagan with cdata. Hi, Ken.

Hi, Brad. Brad, Steven, thank you very much for having me. I'm the Chief Product Officer at cdata, Ken Yagen, and, uh, CDATA is a leader in enterprise data connectivity and integration.

And, and we have one of the first managed AI connectivity platforms on the market. So excited to talk today about ai. Uh, as Chief Product officer at cdata, my focus is really on how our customers turn data connectivity into governed scalable foundation for enterprise ai.

And that's why we're happy to talk to you about this, because again, this is utilizing ai, we're all about figuring out practical, useful solutions based on ai. And I, uh, learned about cdata last year. And boy, it, it is such a great idea because essentially one of the ways in which AI is going to become useful is when it can ingest and act on the various types of corporate data that enterprises have, and really, um, bring that data together and give us, you know, help us to, uh, build applications that use it.

Uh, the problem is that that data exists all over the place in all sorts of different formats and so on. And, uh, from the initial discussions with cdata, it seems like y'all are, are really focused on solving that problem. So, Ken, let's start with just a little bit of an understanding.

What's the problem when it comes to data and ai? Yeah, Steven, uh, the, the problem around data and AI is really, data is an AI problem. Um, the strongest predictor of AI success really is that maturity of your underlying data infrastructure that takes and delivers the enterprise context to these powerful models that companies are investing in.

And so the companies have to act on that data. They need to be able to understand it, they need to be able to access it securely and correctly, and then they need to be able to take action on it, which is sometimes requires writing back into the systems as well. That is a data integration problem, and that requires a lot of sophistication and understanding the semantics of the data and how to access those systems.

There's some, you know, new, new technologies and protocols and techniques that are greatly unlocking that, but you also need, uh, that understanding and governance layer as well. And that's what we try to do at cdata. Yeah, and I, I would, I would argue that there is no AI without data.

Um, I, I think that they too go hand in hand, peanut butter and jelly all day long. And, um, like Ken, you said it is a bit of a challenge for enterprises because they have been working hard for decades now to try to modernize and streamline and democratize access to their data estates. But that is not easy.

And it's certainly, you know, if you take 10 enterprises and sit down with them and say, okay guys, you know, where are you at in terms of, you know, trusting your data, having quality data available to your business users, uh, as well as your agents that you're building right now. And I think most of them would tell you that, you know, it is very much a hit and miss sort of affair right now that they don't have full trust. They don't have full access.

So we ran a survey this summer, um, uh, actually autumn, um, asking data professionals, you know, are you investing in, in, in AI and are using ai? And, you know, as we see everywhere, you know, by and large, over 52% said, we are already using it. We are building on it.

That is our top, top priority is ai. Uh, and yet when you ask them, you know, what are your biggest obstacles, guess, guess of the biggest one is it's, it's not security, it's not integration, it's not money, it is data quality, trust and governance. I agreed.

And, uh, Brad, it's interesting 'cause we actually ran a very similar survey, uh, on our side, the state of AI data connectivity, and we spoke to over 200 leaders in both the enterprise and in software technology companies. And our findings were very, very similar to yours. So we might've been talking to the same people.

Um, we found this Probably were Maybe, yes, uh, 60% of companies had had the highest level AI maturity also had the most mature data infrastructure. Yeah. And the inverse of that, 53% of companies that had immature AI also had immature data systems.

So there was a strong correlation between the maturity of their data systems and the maturity of their AI initiatives. Yeah. I wonder the, the biggest, oh, sorry, go ahead, Steven.

Yeah, Lemme just jump in there. Okay. Lemme hey, here's a, and here's an edit point.

We're demonstrating how to do this. All right. I wonder if that is a cause or an effect or both.

Uh, you know, I mean, if a company has a mature, uh, data foundation, if they really understand their data and they've, they've spent the time and energy and effort to, uh, bring it together in, in some way, uh, they may be better, uh, prepared to develop AI applications right from the start. But at the same time, as you're pointing out, if they haven't done that well, then they're just not going to be able to get the benefit of AI applications, even if they do invest in them. What, what do you think of the chicken and the egg care, Ken?

Is is this a, uh, uh, a requirement or is this a symptom? Yeah, I, I, I under understand, and I think I agree with you with some of this or the, the dual nature of this. Um, and, um, I would say that there is a, if a company has a culture of stewarding their data, having good data infrastructure, they already have a culture that's gonna allow them to move quickly and adopt ai.

And, but on top of which they're gonna have that good infrastructure in which to, to do it. Those that haven't made that investment, they're trying to play catch up, they're trying to, um, swap the engine in flight by plugging in better data while also trying to plug in ai. Um, there's some ways to accelerate that, but you're gonna have to a little bit of the work required along the way.

Um, and you know, what we try to do at C day is we try to help them sort of accelerate that, take advantage of what they have. Um, the good news is, you know, often when you talk about data infrastructure, you think about, let's get everything into a data warehouse or a data lake, and let's stage it all there and everything. And that's important, especially when it comes to understanding your business and analytics.

And you can apply AI to that. But there's also sort of the need, and we saw this in our, our survey and our study as well for realtime data. And realtime data is not data that's STD in a warehouse, but the data that's sitting inside your operational systems data that you're gonna act on directly and orchestrate your workflows and business processes around.

And this is where agents in the world of AI are really starting to come into their own and their ability to sort of do that. And again, they require good understanding of that underlying data. What is, what is the semantics of that data being stored in that underlying system?

You know, how do you operate on it? What are the correct ways to work with that data? And ha combining the semantics with that data access is what will is that sort of accelerant that will allow you to take advantage of it and mature your AI projects much more quickly.

Yeah, I agree, Ken. And, um, it's, it's funny because access and understanding, you know, have to go hand in hand. And I, I feel like right now in the enterprise, we have unprecedented access comparatively to where we were just, you know, uh, a even a few months ago, uh, at the hands, for example, of the model context protocol that Anthropic came out with about a year and a half ago, and how, you know, surprisingly, you know, a a a dominant that has become as a means of helping models in particular access data, but understanding what the data means is, uh, I think a greater challenge and one that not a lot of companies are, are really, you know, uh, able, able to chat about.

Um, I mean, I would love to come back and actually talk about, you know, how the easy button of model context protocol and how dangerous that is because I think you guys, uh, are are definitely seeing that on your customer base. But before that, can we talk about the semantic layer? Can we talk about, you know, what companies really need to do to build that understanding?

Do you feel, Ken, that we're getting to a point now where we have the ability to not go the data warehouse data mart route, but instead have this open composable data lake house, let's say that, you know, totally separates storage and compute and lets me use whatever query engine I want, depend on who I in the company and always have, you know, access to and knowledge of when I say quarter close for accounting and sales, that it means this and not that. Yep. Well, you know, the, the, the common phrase is garbage and garbage out.

So you have to make sure that you're putting good data into, into that data lake, um, in order to apply that semantic understanding of it. So a absolutely, I think we are approaching that. And I think AI is, again, is an accelerant of that and the ability to, um, have deeper understanding of the structure of the data and the meaning of the data.

And both of those are important. Lemme lemme tell you what I mean by that. So, structure of the data is, is how's the data stored?

What is, where do you find and how do you connect the different fields, uh, of the data together? And, and, and then can interpret meaning out of that. And then understanding that data, like what does it mean?

What is this number? Is this a quarterly number? Is it a monthly number?

Uh, does it include us or world or, you know, north America, you know, whatever that might be. Understanding the context, what, you know, accounting principles, if you're talking about financial data, apply to it. So there's a lot of context in, in order to interpret that data.

And AI is really good at sort of stitching that context together. And, um, at cdata we do is we take that we have some understanding of the underlying structure, semantics, and a bit of the understanding of what the actual data is, and we inject that into the context of the model. And that semantic, semantic context is super critical because without it, you, I like to say is you're left with a system that just burns tokens on ambiguity rather than delivering value to your user.

And that's so, uh, difficult when you have such a diverse set of data sources. Um, you know, not every data sources created equal, and not every everyone is going to be able to have that kind of context. Uh, talk to us a little bit more about how you deal with diverse data sets.

Sure. Well, there, I mean, enterprises, the, the typical enterprise uses hundreds of different systems with data stored in all sorts of different, uh, data, uh, locations. Uh, it could be internal databases on-prem software and systems, SaaS-based solutions, uh, partner systems and so on.

So you have to be able to pull all that data together and connect it, uh, in the LLM in order for that context to be valuable. And, and, and that is what a connectivity platform really helps with. We are actually able to go out and connect across systems and join data across systems, take a bit of the burden off the LLM, so you're not consuming all of your context and all of your tokens by having to bring all the data and do that processing in the LLM, we can push that down into these underlying systems across multiple locations, and then expediently bring that back to the LLM so we can do the final sort of reasoning or actions that it needs to perform.

So being able to handle diversity is really, really critical, especially in some of these new agent workflows that businesses are building. We as humans, we deal with that sort of, you know, diversity day by day. You move in between what you used to call swivel chair integration and moving between system to system, pulling data together, copying and pasting, pulling up analytics reports.

We do that as part of our job. We're now asking AI and agents to do this. And so it needs to be able to manage and handle that diversity.

So you need to be able to, to have a system and underlying infrastructure that supports, uh, that diversity as well. Yeah. At scale, right, Ken, because, um, as we start to get into, you know, these, these very advanced ag agentic pieces of software that we're building right now, getting data to the model is half of the challenge, half of that battle and, and not just understanding it.

And so you see a lot of investment right now in, in things like memory caching for being and being able to batch process and be able to, you know, not burn tokens, but still get the data to the models. And I think we need to also think about the fact that it's a entirely new constituency, uh, not just for consuming data, but for producing data. 'cause these ag agentic processes create a lot of information as they go, and it's data that needs to be managed by the business because, you know, you, the, we, I was just actually talking to, uh, a number of, uh, companies who are building out commerce systems that are agentic.

And the biggest challenge they felt they had was, was being able to take the data that gets generated from each interaction with their customers and to, you know, have that available to the agent, not just today, but tomorrow and the day after tomorrow. Yeah. And that, that's, that's where these, uh, data management platforms, large data lake solutions can really valuable.

You have a place you can go store that at scale and then go back through, through agents, uh, and access it and bring it into the context of, of your workflow. Um, I do wanna, I can go back to Brad to this point. You've brought it up a couple times in the concept of the explosion of access, uh, and, and, and what you were saying just there reminded me of it again.

And that is really important to think about because I, so I've, I've been around the data and application integration world for almost 20 years now and saw all the explosion of, of APIs and SaaS and, and integration and iPads, and now, uh, with, uh, AI and agents and MCP and data's getting easy and easier to access, requiring less and less sort of technical work to, to bring it to the point of use within the business. And access is really critical access, both in terms of scale, like you said, you know, you can bring back too much data, burn a lot of tokens, uh, bring back inappropriate data that maybe the user's not, or the agent is not allowed to operate on. Or maybe the agent might do something with it that a user would know not to do.

Uh, so you need to be able to sort of govern that. You need to be able to handle at scale. Uh, you mentioned model context protocol.

You can have a tool explosion model context protocol represents everything as tools, and you can only handle so many tools within the context of an LLM. So you need to be very efficient in the tools that you expose within an agent to the LLM and to how much data you bring back. So leveraging the power of these underlying systems, not overload the LLM with too much data.

So there's a lot of things around access that need to be thought of by someone architecting and a gentech system. And, uh, again, those are areas that we, I spend a lot of time thinking about how do you actually scale this and do it effectively and efficiently. Uh, and it's something that we see our customers, um, really kind of struggling with when they come to us, but realizing that there is, there are better ways to do this.

There are definitely better ways. Um, and, and unfortunately the, the technology is moving so quickly that it, it's becoming a, as you mentioned, too easy to access data and to do so unwittingly, un responsibly. Um, and also it, it's, you know, performance wise, the, the tech, the tools that we have available to us are allowing us to build systems that we can't support our, our infrastructure just isn't ready for.

And I, I like to think, you know, when I, when I think about cdata and you know, vendors that are playing in the space, you are that at the end of the day, it's about, you know, helping customers see that they should not go the shadow IT route because that, that's dangerous. Um, but there are options to, you know, accelerate what they have and to meet those evolving capabilities as well as needs that we're seeing start to, to come into market. I mean, I saw a model come out just this week, uh, that has the ability to handle 400 tool calls in a single, you know, long running pass.

That's insane. Uh, there, that is, that's, that's a lot of processing, a lot that, a lot of power that you have in that, in that type of model. And that's the thing, we don't know what's gonna come out next week.

We don't know how these models are going to evolve and what capabilities they have. We know they're gonna do more than they do today. And so you have to kind of plan for this unknown future.

And so when you're thinking about how you design these systems, you do need to think about scale and governance. And you also need to think about what might be possible six months from now. Um, and the other thing is make sure that you're designing to, to update and refresh this, realizing that your architecture's gonna change.

There's gonna be innovations to take advantage of. So you have to be agile. And so you need to use underlying infrastructure that's also agile and gives you that flexibility.

Don't tie you down to, to one particular model or infrastructure vendor, uh, allow you to move around, consume new data sources that you didn't necessarily have that, that you weren't thinking about before. So that agility is really important in this type of fast moving world of ai. Well then that point that you're making about is, goes to the point of maintainability.

And that's been a, a key problem. Anytime you're building an application that integrates diverse data sets or tools, I is the inherent sort of fragility of those systems because, uh, you know, vendors can change the ways that their APIs work. Uh, they can change their, you know, they could abandon, uh, one API or another.

They could, uh, really upset the apple cart. And this is especially true. It gets multiplied when you have more and more and more disparate you, you know, components in there.

So one of the points that I was gonna ask, and, and I think you've sort of just a answered it, is why not just rely on the vendors to make this accessible? Why not just work with, uh, you know, whatever happens to work? And I think that the, your answer might be, because even if it works now, it might not work later.

And also maybe, you know, different vendors may have different approaches. They may not wanna support this or that model, and you would perhaps allow them to, uh, integrate with, uh, a broader set of data. Is that right?

Yeah, exactly. You might wanna switch vendor, you might wanna switch model vendors, uh, next year. You know, did you tie yourself to the, to the capabilities or the interface of that particular vendor?

Or do you have the ability to kind of switch, switch, switch that out? Uh, this is particularly the case when going with sort of full stack solutions from some of the legacy players. You miss out on some of that innovation that's happening in the market.

'cause they're gonna be a little bit more, they're gonna be a little bit slower to bring that capability to market. And so you, you wanna have that agility. That's exactly right.

Yeah. And I, I think it's, um, you know, when I look at the marketplace for this year, uh, one of our biggest trends that we see evolving is this acceleration through integration. And that, you know, last year we probably would've talked about, you know, the format wars and is it gonna be Apache or, or you know, is is it gonna be Delta?

And you know, that's done. It is, it is definitely, you know, Apache iceberg all day long. And that separation of storage and compute I mentioned, and what that does is place the burden on the vendors who are building these systems to, to provide that sort of interoperability.

And what I worry about honestly, is that we sometimes, when we get a shiny new toy, we over rely on that toy to, to scale with, you know, our needs in the marketplace. And I think MCP is one of those that's just been so overused, uh, you know, right now that it's, it's becoming itself a, a sort of liability in terms of that. That's, you know, like we talked about before, understanding the meaning of the data that it's accessing, accessing the right data.

How do you secure that access point? Because those standards, like a 2:00 AM CP, any other framework you wanna throw out there for integration is, you know, an abstraction layer. And those abstraction layers aren't free, right, Ken?

They, they do have a cost you have to pay, I think. Well, I think I, I, I wanna agree and disagree with you on that. So, uh, I agree, I agree that there is, there is, you, there's a trade off whenever you have an abstraction layer.

'cause you're, you're always, you're always in a trade off. 'cause otherwise you would go to a proprietary approach and you might get something very much more specific for your needs. But extraction layers help markets sort of stabilize and mature.

They allow people to focus on one thing, and that's what MCP has done. It allow people to focus on a single way to connect their data, their, uh, and their tools into the LLM. Now, if you just just utilize it in that way, and you don't think about how you deal with authentication, how you deal with governance, how you deal with security, how you scale it, how do you manage it, change management, all of that, then you're gonna be in trouble.

Like you say, then it, then it's a crutch and it, you're, you're not going to be successful at the end of the day. Um, if you just use agreed, go grab the latest MCP server in some open source community, it might work for you original, initially, but you might, you're quickly probably gonna find out it's a bit brittle, it's a bit fragile. Even some of the ones from some of the first party, uh, providers out there, they're incomplete.

Um, and they don't maybe have all the capabilities that you need in order to solve for your problems. So you gotta build around that. And that's one of the things at Cdata we're looking at that.

We've built our own MCP servers for over 300 different critical business systems. And we built all the governance, we built the, uh, security, we built the scalability. And I also added that semantic layer around it to make it much more effective and much more efficient, so that now you do have something that you can rely on that's stable and that you can, uh, scale your systems on top of.

Well, yeah, I recall there being a market, oh, I'm sorry, Steven. Uh, I I recall there being a market specific to integration, um, that that's all vendors did, and they built connectors. And we, we seem to, as a marketplace, have tried to move away from that and say, oh, you can just deal with yourself.

But that's really not the best approach when you're trying to have a system that could adapt to that changing data estate that we've been talking about. To be able to say, today I need Salesforce data cloud tomorrow, I, I need something on, you know, a totally different platform from SAP. Yeah.

And I, I would say, you know, for very simple things, for very simple APIs, uh, a lot of roll it your own. But as Cdata, we've lived in that sort of world of connectivity for many, many years. We have 10,000 customers that are, uh, that are licensing and using our connectors, including some of the largest software companies in the world, um, that are embedding it inside their platforms, uh, in order to provide connectivity out to other data sources.

So connectors are a critical component. MCP as we're talking about here, model context protocol puts an abstraction over that concept. Um, so that anything that you can connect to anything that has an API, you now have a way to plug it into an LLM and make use of the data and make use of the actions that you can perform.

And, and that's important to, to think about. It's like MCP can be data access, but it can also be operational execution. Uh, and there are other concerns when it comes to operational execution.

You're changing data, you're triggering workflows, you're, you're triggering actions within your organization, within your enterprise that have implications. And so again, security governance and all around that scoping it, scoping the permissions down to the set that are necessary for that agent to perform the types of actions that would be necessary for whatever its goal or objective is, and not allowing it to stray beyond that. Yeah, I think that you all have a lot more experience with MCP than most of the folks listening.

Um, I wonder, I, I appreciate you kind of bringing those, the, uh, thoughts to, to the fore here about MCP and thinking about governance and security. Um, what else could you tell us if, if we wanna kind of step back here a little bit, um, what should people know about MCP if they're looking at it, if they're thinking of implementing it? Um, you know, what have you learned in all the years of developing or the, the year of developing all these, uh, MCP servers?

Um, what are the lessons you've learned? Yeah, I mean, the lessons I learned, first of all, MCP sits on top of the underlying data sources. The APIs are the, uh, the SDKs are used to access them.

There's a lot of complexity. You know, just because something is rest doesn't mean it operates in a certain way. So there's a lot of complexity underneath it that you need to deal with in order to have a good functioning MCP server.

Uh, authentication is still hard. We, which it could be easier. I mean, so back in the day, I actually worked on the, uh, saml, uh, committee to develop that as a standard, took us a long way.

We're still, you know, utilizing that inside of OAuth and everything else. So, but there is a lot of complexity when it comes to identity. And you, and also with MCP, you have to think about identity in the context of the user now in the context of the agent user and the, and the scoping of that identity.

So that, that's something to deal with. Um, there are their own little sort of enhancements or additions that each, um, client has created open ai. They have their open ai, they have their apps, uh, Claude Anthropic Claw, they have skills, they have different things that they're building around MCP that are specific to each of them.

And so you need to think about how your MCP server is gonna interact with each one of these, whether it's a chat LLM type agent or an agent platform. Uh, that's something to think about. And then there is, there's governance, there's, how do you discover the registry for discovering, I mean, it's, it's okay if, you know you've got a handful of MCP servers, but what if you've got thousands of MCP servers?

What if everything in your enterprise is suddenly, uh, MCP enabled? Now you need to have a concept of a service registry and some sided governance around it. Um, so we're not getting away.

We had that problem with APIs and API management. We had it back in the SOA days with SOA service registries. It's, uh, we had it even back in the corba days in the nineties.

So it, it's, you know, naming and discovery is always gonna be an issue. It's gotten a bit easier, um, with LLMs, but with MCP, you still have that, that concern. Yeah, bring, bring back middleware.

Uh, I, I, I, I love the, the, the SOA era just because we, we were trying to build software the right way, and it just, it just turned out that it was a little bit more difficult than we thought. Um, but may, maybe we have the option now, but I, I, I totally, you know, agree with what you're saying, Ken and I, I feel like, you know, when you're talking about CPS as just another means to, to get to those sources, you do have to consider the models. And I felt for a while now that the models themselves, especially the frontier models, are much more than just, you know, a, a, an endpoint that you're querying.

It's, they're actually platforms. And so you need to have standards, you need to have some sort of registry to understand when Google changes the Gemini, um, API subtly that, you know, it's, it's not gonna break your application tomorrow. Uh, maybe the model itself changes in, in its ability to like, um, refuse a request or continue the request.

And all of that is, is much harder to deal with when we have these models that, that are non-deterministic that we're using as infrastructure. Exactly. If I, if I can just summarize some of my thoughts on that.

Uh, so I'm a proponent of MCP. I'd like that it's being developed in real time and tested in the market and iterated on as opposed to being developed in a, in sort of an ivory tower and, and over-engineered. We've seen that in the past.

So I really like the approach that, that the vendors have taken to kind of come together on this and, um, try to not try to solve too much and allow other infrastructure and software companies and the LM providers to come in and build around that, to, to kind of sort of polish those rough edges and provide the additional support and capabilities that are needed. Uh, I think that, uh, one of the key areas that, that needs a little bit more work and that we're focused on is that semantic context, as we've talked about and under understanding what's, what the capabilities are, the underlying systems. Uh, I think that it opens up and allows for real time data access and action.

I think that's very important. Uh, and it recognizes that so much of the enterprise data is in this sort of structured format that agents being able to access and operate on and com, that combination with these types of standards and these types of capabilities will allow us to get to sort of this promise of digital employees, digital agents that are agent, agent working together, uh, swarming together to solve, uh, solve problems and operate businesses more effectively and creating more enterprise value for us. So all of that said, I think it's time to, for companies to be investing in their data connectivity, investing, investing in their infrastructure, and to do this to enable AI to answer and act on their business.

Yeah, thanks for that. And, uh, I think that's a good message to leave our audience on here. Uh, as Brad said at the top, uh, you know, you can't really build an effective AI application without good data.

It's all about the data. And, um, that means that this is an area that, uh, companies are gonna have to invest in if they're going to have an effective AI application. Thanks for joining us.

Uh, before we go, uh, many of our listeners may wonder how they can continue this conversation or where they can connect with you. Uh, Brad, uh, let's start with you. Uh, what are you researching?

What are you working on, and where can people find you? Yeah, right now, I'm, I'm actually building out a new, um, comparative report using our, our, our signal, um, ag agentic report, processed on data intelligence platforms. And that's gonna be all about how you get that semantic layer and put that in action just like Ken said.

So looking forward to that. com. Excellent.

And, uh, Ken, how about you? Great, thank you, Steven. Uh, likewise, uh, happy to people to reach out to me, connect to me.

LinkedIn's, uh, gonna be the best way at Ken Yagen on LinkedIn, on other social, on, on X and other things as well. Uh, I mentioned earlier, but I encourage you to download cdata state of AI data connectivity report from our website. We'll provide the link, uh, along with this podcast, and you can also check out our product.

com. Uh, and I'll be speaking in March at the Gartner Data and AI Summit in Florida. So if you happen to be there, come check out talk.

We're gonna be talking about, uh, the same topic there. Excellent. And, um, as for me, uh, you know, I run Tech Field Day, uh, this week is AI infrastructure field day four.

com and the tech field, a socials, and of course, we will be having another AI Field day in May. So keep an eye on the Tech Field day socials to learn more about that. Thanks for listening to this episode of the Utilizing AI podcast.

If you enjoyed this discussion, please do subscribe on YouTube or your favorite podcast application. Also, drop us a line, uh, give us a rating, give us a review. We'd love to hear from you.

This podcast is brought to you by the experts at, uh, Futurum Group, uh, where Insight meets ai. For show notes and more episodes, head over to Textron ai, the utilizing AI YouTube channel or textron's, uh, new TV app. Thanks for for listening, and we will catch you next week.

Hello everyone, and thanks for joining the third session for today, uh, which is called From Notebook to Production. Uh, we're look into OpenShift AI on our cloud services. My name is Philip, and like the previous speakers and the managed OpenShift black bullet Red Hat today, we have seen already two sessions, which are about the foundation and the legacy.

So we've already seen how you can manage your containers, uh, next to your VMs, uh, on Reddit, OpenShift. Now we'll have a look into the future, and I will tell you a little bit about AI workloads, which are also possible to run on OpenShift with our product OpenShift ai. As you can see, um, there are like several people involved in AI or ML projects.

So we are talking about business leadership, data engineers and scientists, ML engineers, app developers, and IT operations. All of them play their part, uh, in the full, uh, lifecycle of these projects. Um, so, um, as you can think, this might open up the opportunity, um, to create silos, but what we've learned in the past, um, with application development, uh, silos are basically not like a really good idea.

Um, they're basically coming out of the tool sprawl. They're using like several different tools, um, which they like to use for their, uh, specific part in this development cycle. I would show you later, uh, how with OpenShift ai, we can have an, uh, platform, which, uh, covers all of these aspects and caters to all of these roles.

Um, another perspective is, um, that, uh, getting ai, um, project into production is like really hard. You can see a statistics from 2024, uh, which basically says that, um, about 80%, um, of IT decision makers say that it takes, uh, six months to two years, uh, for their company to, um, get their AI projects from pilot to full production. And this is usually because of silos.

There is like a complicated handover between teams to actually get these applications, uh, out to the customers. Um, I want to tell you a little bit more about, uh, OpenShift ai, uh, our integrated AI platform running on top of red OpenShift. Um, so we cover basically all the aspects of the AI and ML lifecycle.

So we'll start, for example, with model development. Um, you can, uh, train your own models, um, or fine tune existing models or just use models, um, that are, um, yeah, like open weights or similar, and you can integrate, of course, several AI and ML libraries frameworks and everything. Um, the next step would be like a model serving.

So after you have trained or fine tuned your model, you need to serve it, um, with some inference servers, something like that. So we, uh, can, um, deliver, or you can deploy the models on top of OpenShift, um, with a kind of well-known platform when you, for example, already use it for your container VM workloads. And of course, this comes with an observability stack, um, to, yeah, get some insights into the model's performance.

Um, talking about lifecycle of the models, uh, we can use, um, well-known DevOps practices and to create pipelines, um, and extend them to some ML ops, um, workflows to continuously train or fine tune the models and get them out to production. And of course, we can use the already well-known tools, um, to, um, uh, manage the resources, uh, of their underlying cluster. And this is especially interesting when we're talking about accelerators like GPUs.

So in the session, I will, um, kind of take, uh, two perspectives. Uh, the first one will be, um, the data scientists view. Uh, data scientists usually want to use tools to love and that, uh, like to cover the platform engineer's perspective, um, to, uh, make it more, uh, visible or more obvious that AI workloads are not a black box, but it's basically just another workload.

So first we'll start with the data scientists view. I will directly, um, go into demo and, um, show you how it really looks, uh, within OpenShift ai. We will first see the dashboard, and then we'll, um, go into the creation of a workbench.

So this is the, uh, dashboard of OpenShift. On top, you can see, um, all the data science projects and in the background, these are basically Kubernetes, namespace or OpenShift projects. So we are now on the dashboard of OpenShift ai.

As you can see on top here are the data science projects, and these are all the projects already created on the cluster. And they and background relate to, uh, OpenShift projects or Kubernetes namespace. Um, on the bottom, you also have some the learning resources that you could use to, uh, scale up on certain topics.

Um, on the left, there's like a navigation. We can go into the data science project. So this is just different perspective, uh, of the full list of, uh, projects available on this cluster.

We can click on models with the model deployments. Uh, there we can see, um, all the model deployments available within these data science projects. We also have a pipelines for experiments and for, um, training and running and executing these models.

There is also, um, some external applications that you can, uh, get, uh, or install onto your cluster, uh, but we'll not cover these, uh, in this topic. Um, there are also, um, learning resources with some, um, documents, blocks and videos that you can, um, use to scale up on certain topics. We have our hardware profiles, we will mention these later.

And, um, user management. So today we want to focus on, uh, the Jupyter Notebooks or the work benches. So I will click into my Cloud Friday demo and data science project.

We've already created a one work bench. Um, if you'd like to create a new one, you just click here, and then you have to fill in some form fields. So basically you have to give the name, you can describe it, and then you can select the workspace, uh, the workbench image.

Um, for the workbench images, we have several options coming with OpenShift ai. So the first one would be Kuda. Um, kuda is basically used when you want to use the NVIDIA toolkit.

So you, if you have N media, GPU as accelerators, uh, these, um, work bench images might be a good foundation. Uh, we have a, that standard data science image, which just, um, uh, contains commonly used libraries for machine learning. Of course, we have tens of floor flow and PyTorch, a minimal Python environment with just a Jupyter Lab for, um, executing type and code.

We have a trustee I notebook image, um, for model explainability tracing and accountability HANA AI for HANA Gaudi devices. And in taking a preview, uh, we have the code server, which basically, um, gives you a BS code environment. So when you select, for example, um, a minimal kuda image, and we give it a name, and then we can select the version of the work binge image.

And afterwards, um, we have the option of the deployment size there. We can, uh, choose the heart profile. This is one of the hardware profile we've seen before in the, um, settings.

So in this case, since I'm using coda, I need an Nvidia GPU, so I can select my Nvidia GPU hardware profile. Um, I can also change my CPU and memory and GPU requirements, um, just by, uh, pushing your buttons or putting some numbers into that. I can add environment variables and, um, attach existing storage or create new, um, persistent volumes that get attached, uh, to this workbench.

I can also, um, attach, um, connections. So this is basically used when you want to access some, uh, object storage, for example, uh, for some, um, external data. Um, going back to OpenShift ai, uh, we can see that I've already created a work bench called Cloud Friday demo book.

Um, I can now start this workbench. It will, um, create the corresponding Kubernetes app objects, um, like the PO where the Jupyter Lab is running inside, um, and the additional containers that are used, for example, through, uh, uh, and the persistent volume for persistent data. Um, after this workbench is started, and I think this will be in some seconds, uh, we will get a link, uh, to open it, and we will be directly in the Jupyter environment.

I will now click this link and the Jupiter server is actually starting up. Um, as I've tried this before, uh, I immediately see where I left this when I stopped this environment beforehand. And when I execute this code, I can see that this and Jupiter, um, environment running on a node with a Tesla T 4G PU.

So we will stop this workbench again, since we want needed for the next demo, I will now switch ahead and talk more about the platform engineers view, uh, on these AI and ML workloads. For platform engineers, it's like really important that AI and ML workloads are not like a, like, like a black box, but it's, um, is, um, something more well known to them. So we will go again into our OpenShift environment.

So this is the administrator's perspective. When you log into an OpenShift cluster, I will switch to the developer's perspective and go into the Cloud Fridays demo project. Um, as you can see, uh, here is a stateful set with the Cloud Fridays demo notebook.

So this is basically, uh, deployment of the Jupiter notebook. Since I've stopped it beforehand. There are currently no pods and there is also the external route.

So this is basically the link that we've seen in OpenShift, uh, when we started this environment. Um, as mentioned before, everything in OpenShift has an OpenShift representation. Um, so for example, when we start, uh, model deployment, I've prepared already, um, inference service, uh, with the QU model.

I will start this and show you in the meantime how this works. So also in this case, we can give you a name. Um, we can select the service runtime.

So for example, we have a VLM, uh, with N Media, GPU, uh, on K Serve. So these are like a lot of words. So Wheel LM is an open source ference server.

And this is a project, um, which is, um, there are also a, it basically mainly contributed by, um, red Hat and we will use kerv. So this is a service engine, um, built on top of ative. So basically a serverless runtime to, um, do interference with, um, lms.

Um, so yeah, we can select Canadian serverless and the number of replicas. So we can also choose to have like a minimum replicas of zero. So, and this would basically deploy a model, uh, which is only, um, like creating the redeployment when the first request comes in, and we can choose the maximum replicas that it can scale to.

So we would keep it like with minimum one, maximum one, so it's always running. Again, we can select the hardware profile. Um, I can again choose an GBU U, so this would use Tesla T four, um, to around ference server on this node, we can choose to make this deployment available through an external route.

So, uh, when we click this button, um, OpenShift automatically creates a route to this model and makes it, um, available outside of the cluster. If we don't click this box, this model would only be available inside of the cluster. Um, the model location, um, I've prepared some, um, OCI, some, some, uh, URLs for OCI containers, uh, which contain the model files, but we can also choose, um, other, um, storages like the OCI compliant registry and S3 compatible object storage or generic URLs when it's, uh, living publicly on the internet.

And we can add additional serving runtime arguments. These would be arguments used by VLM, uh, for a configuration. And again, we could use, uh, environment variables, uh, to inject into the pots.

So as we can see, uh, the model is now deployed, and when we switch back, um, to the OpenShift cluster, uh, we can see that now there is a Canadian deployment with my predictor. Um, we have a public route because of exposed the model with a route, and we can see that, um, there is currently one instance of it running, uh, since we've defined previously to have a minimum replicas of one and the maximum replica of one. And this will be, uh, a static workload.

But as mentioned before, uh, we can also scale is on demand. Um, since, um, all of the things I did in OpenShift ai, um, are also like YAML files within the OpenShift cluster, um, of course we can, um, can extract them, um, after clicking them together in OpenShift AI and, um, use them, uh, in a ops process. So for example, the QU model that I've just created, um, I could export, um, the Ference service, um, and yeah, put in a kit repository and now, um, um, use, uh, the GitHub's way of deploying these models, for example, in other environments.

Um, yeah, or just to replace this manual process in the future. Um, yeah, as we have mentioned before, um, this is just, um, OpenShift, um, um, components. Um, so we can see that there is just a pot like, uh, every other pot on the cluster.

So it's basically behaving completely the same as, uh, any other application container. Uh, we can see that, um, this pod has an, uh, in it container and, um, contains of four, uh, other containers which basically run, um, this model. Um, we have some, uh, volumes mounted and it's basically, uh, behaving exactly the same.

So this also gives us the possibility to use the, uh, observability stack, which comes with OpenShift. So we can, for example, see the CPU use usage, memory usage, um, the network io network errors and things like that. Uh, since all the metrics are just exported, uh, into Prometheus.

So we can also create alerts on top of that. And, um, we see the same, uh, events that we're, that we know from our container deployments. Um, yeah, for example, there was some issue with, uh, fulfilling some ingress.

And, um, yeah, so if we see some, uh, issue when deploying our, uh, model inference server, we can use these perspectives to debug this. Um, yeah, so we basically have two, um, model serving platforms. So we have to decide on single modern model serving.

We can use SER for that or multi model serving with a model mesh. So single model case serve would basically be used for, um, larger models that you'd like to have running all of the time. While model mesh, uh, might be a good idea.

If you have like smaller models and you want to switch between them a lot, uh, of course you have take into account that it takes quite some time for larger models to get loaded into the, um, memory of the GPU. Um, so yeah, larger models should be basically static and you could use model mesh for smaller models. So we've seen like the two perspectives.

Um, the data, uh, scientists perspective and the platform engineers perspective. Um, we've seen like two products from our Red Hat ai, uh, product portfolio. We've seen the AI inference server, which is basically our build of VLM, and we've seen OpenShift ai, uh, which also has a corresponding open source project, which is the open data hub.

Um, there is also a redhead enterprise Linux ai, which is a build of redhead enterprise Linux with an FERENCE server and, uh, instruct lab for model fine tuning. Um, of course, like all our products, uh, this work on kind of any hardware and, um, kind of everywhere. So you're free to choose between bare metal physical nodes, virtual servers, uh, your private cloud, the public cloud, and on the edge.

Um, the demo we've seen before was actually running on Azure at OpenShift, which is our managed OpenShift service in Azure. Um, we have, uh, a similar product in AWS in GCP and on IBM Cloud. So what's the differentiation?

So, um, OpenShift ai, um, promote the freedom of choice. So you can choose, um, whatever, um, um, AI ML framework or model, um, or tool stick that you want to use. Um, we have a consistent way, um, of a moving experience to production, uh, with, uh, our application platform.

So it's basically following the same process, um, that you can use, uh, for your, um, application container workloads. Um, yeah, you can now adapt them for AI and ML workloads and, um, you have hybrid cloud flexibility, so you're free to choose and to deploy your models, uh, wherever you want to if it's on-prem, in the public cloud, or in a completely disconnected environment. So thanks for watching, and if there are any further questions, just get in touch and I'll be happy to answer them.

Uh, I'm just gonna jump right in. Uh, mastering personal branding from a ciso. I will share a little bit about myself.

Uh, I started off as a hacker. Uh, one thing led to another and my passion turned into my career, uh, ended up in the cross areas of the government for the hacker side. Turned out to be a good guy, and it's been profitable ever since.

Um, I am a father of four. Uh, my wife and my kids came to the last cruise con, um, and they were like the defacto mascots actually for the cruise con. And so thank you kq for bringing your kid along and giving us something to, uh, have fun with.

Um, I've been to CSO four different times, five, depending on how you look at the divisional CISO role. Um, and, uh, proud millennial. I think being a millennial has been a big part of the fact that I've been active on social media.

Um, and then, yeah, second time at Cruise Con, uh, still don't know why Ira keeps bringing me back, but here we are. And, um, this guy right here, no brand expert, this is me at 24 celebrating my first season opportunity at Lockheed Martin. Um, when I told my wife, I don't know about a year ago that I'm gonna be doing a conference, and she said, all right, great.

You do all these conferences, what are you gonna talk about? I said, well, it's on a cruise and we're gonna talk about personal branding. Both of those statements made no sense to her, right?

Conference cruise jar, personal branding. Uh, this guy had no idea what personal branding was. I barely knew what leadership was.

And to be honest with you, I'd much rather just be hanging out in my crocs. And in fact, I tried to bring my Crocs to the last one, and I tried to convince my wife, there's sports mode and relax mode, right? I mean, sports complex, sports mode, cruise, climb, relax mode.

She did not go for it. And I thought, because she didn't make it on this trip that I could sneak it on my luggage, but when I got here, wasn't there, uh, she's like, that's not gonna be your brand. So, um, if you didn't know what I mean, I'll buy you a bunch, a set of Crocs.

Well, I don't know if Sean will be okay with that. 'cause we had a conversation and Sean had all types of negative things to talk about. Crocs.

Wow. Um, and I was like, oh man, that's part of my talk. But, uh, Let me not speak up on that.

Uh, but for those that aren't aware, it's a real thing. Um, so let's, let's talk about personal brand. What does it actually mean?

People always ask, well, is the brand just your reputation to a degree, but it's the digitization of your reputation. Historically, in the past it was word of mouth was your reputation. That's no longer the case anymore.

Today people are opening up their computer and they're looking you up. People looked you up before you got on the cruise, if they saw that you were coming on the screws, especially the vendors, right? So that is part of what it is.

That's just part of your brand. But what exactly are they looking for? What impact have you had in the industry?

What does your resume look like? What is your track record? What are the things that you stand for?

What, what is your value? Right? And so those are the things that people look at.

And collectively, that's how they determine your personal brand. So for CISOs, it means a lot of different things, but it really means how good are you at your job? How well are you at managing risk?

How well do you communicate that risk to, to leaders? How do you handle the hard situations? I like to say that CISOs are like tea bags.

You never know what's in them until they're in hot water. And, and that's real. And your internal brand and your external brand will be determined by how you handle the hard situations.

A lot of the time, we're all gonna have a bad day. There's just no way around it. It's how you handle that bad day that people will determine this is the right guy for the job or the right, the right lady for the job.

And so when you think about your personal brand as a leader, it's really how well are you impacting the organization internally? How well are you affecting the community and so forth. So how many of you think you already have a brand?

How many of you think you don't have a brand? Fair enough? When someone looks you up online, they're saying something about you.

They've made a determination. Even if they see very little bit about you. 'cause you may be one of those people that try to keep as small of a footprint as possible.

IRA has changed that. 'cause you've come to Cruise Con, right? So you now have a brand.

Everybody has a brand in some capacity in, in some way. Now the question is, are you shaping it or are you letting other people shape it? And that is really the crux of the conversation when we talk about, uh, branding.

So I'm gonna put this to the test and I'm taking a chance. I did this last time. It kind of worked out okay.

Who actually has internet service on the, on the ship? Who, who purchased internet service? All of you guys.

All right, you guys have, you guys have one minute I'm gonna ask you to Google me. My name Jared Beason. J-E-R-I-C-H, last name B-E-A-S-O-N.

And then I'm gonna pick on a few of you and ask you what you think my brand is based off of what you find online. It could be good, it could be bad. I got a really interesting bad one last time, but it's all right.

It's all part of the deal. Um, but I'm curious what you guys come up with. I It is always the most awkward part for me.

Like, what are they finding? The people that they go to page two and three will go really find the interesting stuff. Yeah.

Oh, some people went to the way back machine. I'm like, come on guys, you don't have to go all the way there Anymore. S you.

Yeah, that's a good point. All right. Who wants to volunteer something that they think is associated with my brand?

Based off of what you've seen so far, John? See the guy wearing a suit. Guy in a suit.

Yeah. I want appear professional even though I'm clearly not. All right.

Anybody else, Sean? Sure. Uh, credibility and experience.

Credibility, leadership. I like that. I like that.

That's what Chad GPT said. Yeah. All right.

Yeah, open AI knows me. I don't know if I like that or not. I like it.

Human centric and empathic leadership. Human-centric and empathic. Okay.

Ah, man, this is good. Keep giving it to me please. So make me feel good, Bob.

So the First thing I noticed is you have 41,000 followers. And that is, that's just impressive. There's a lot of people that follow you for some reason and bears looking at the rest.

Well, well, thank you. Thank you. One more.

Yeah. You are actively engaged in giving back to the community. Yes, sir.

Yes, sir. All right. This is, this is good so far.

Um, I had AI write this for me about what my brand is and I helped shape it. But ultimately what I want my brand to be is that I'm giving back to the community. I'm shaping the next generation of leaders.

I'm equipping the next generation, but I'm also able to communicate with all levels leaders or, or younger. And some of the words I was hoping to hear you guys kind of threw out there, right? Uh, empathetic.

I think I heard someone say that. Experienced, um, giving back to the community cyber peeps. I run a mixer in the Houston area coming to Dallas soon.

Um, neurodiversity. So this is one about three years ago that I picked up because my son was diagnosed with autism, A DHD and dyslexia with is the trifecta of Neurodivergence. Um, and as I dug into it, I realized that this is actually hereditary In many cases.

My wife realized it was hereditary and she was like, you're the reason. 'cause you're probably neurodivergent too. Um, turns out I am on the spectrum and if I've had a conversation with any of you guys, you may or may not have noticed.

I never look you in the eye for more than three seconds. I can't. I don't know how to do it.

It's just part of my neurodivergent spectrum. Um, but as I dug into it, I learned the cyber community has more neurodivergent people than any other industry. We're more equipped to have neurodivergent people.

And if we know how to harness that power, we can actually do some really good with that. Um, but we also treat them like pariahs a lot of the time. And one in three people are neurodivergent.

So I look across this room, 60% of you guys are neurodivergent from the conversations I've had, right? But, But that's our industry, right? And so I, I could talk about that for years, but that's something that I just started to pick up about three years ago.

And the one really important one is this one right here in the corner. I want you to think I'm not cheap. You're not gonna ask me to come to a conference and I'm gonna be, you know, doing a conference for free unless it's maybe given back.

Or if you wanna hire me, you're not gonna be able to offer me $95,000 and think that that's the CSO role that, that I'm gonna take. And just off my brand. I don't get those requests.

Like, because people would know that that's not gonna fly. And so that's just an example of some of the things I would want people to say. But now we're gonna do this a little bit different.

I want all of you that have a phone, internet or not to write five things you think are associated with your brand. We're gonna do a little activity. You have one minute.

This might be hard 'cause it's your first time doing this, but soon it'll be like your elevator pitch. All right? Who has all five?

All right, I'll give you guys one more minute. 'cause some of you're slow. I'm assuming it's a neurodivergent ones CPT.

That works too. All right? Because I know I'm not gonna be able to use all my 45 minutes.

I'm gonna ask all of you to pair up with one person and you're gonna Google that person and they're gonna Google you. So just pick one person in the room, hopefully someone close to you. And we're gonna see how close what you think is the case.

I don't Google. I bought the pack. It's, Yeah, I need the people have energy.

I need the energy up. All righty, let's wrap it up. Let's wrap it up.

Up. Thank y'all. Alright y'all.

Thank y'all. Thank y'all. The activity has commenced, or IRA's gonna gong me when it's time for this team to be over.

So, uh, thank y'all for participating in this. Uh, real quick, by a show of hands, for the people that uh, participated, um, how many of you had all five of the things that you said called out by the person you partnered with? What about four of the things that you said?

Think about four. Four. How many of you guys had three of the things that you said, alright guys, we're done with the activity.

We're done with the activity, we're done with the activity. How many of you guys had three of the things that you said called out by your partner? Well, we missed the part where you're supposed to call out the five Zero Oh, you missed the part.

Okay, that's fair. Sorry, We just, we're doing, That was a, that was my fault. How many of you had two of the things that you said called out two?

Alright, so that shows the gap between what you think is your brand and what other people think, right? And, and that's important for you to understand that. And I encourage you to Google yourself every once in a while and say, is what I'm putting out there aligning with what I want people to see me as?

Right? It's a very simple activity, but it's so often realized that we are not necessarily coming across and we're not being received. Uh, like we think we, like, we think we are and like, like we wanna be.

So it's the first takeaway. Alright, we're done, we're done, we're done with the activity, right? Um, so first thing is your brand can and will change.

I used the neurodiversity example, um, because my brand three years ago would not have included neurodiversity. Most people in this room's brand would not have ever included any type of ai, but maybe it does now, right? As technology changes, as you change, as your life experiences change, your brand is going to change.

But most likely your core values don't. So couple things that your brand is not, it's not your resume. Really quickly.

People think, oh, my brand is, I worked here, I worked here, I worked here, I worked here. But what did you do? What value did you bring?

What do people remember about you at those place places? It's not aspirational. You know what?

I want to be the best looking person in the room, but Chris is in the room so I can't be that right? And so it's not aspirational, It's not your self perception, it's not, well I think I'm this 'cause you guys just demonstrated what you think you are and what other people see you are are very different things. And here's the big one.

It's not the truth. People think, well this is the truth. So this is the reality.

But perception is always reality when it comes to your brand. And so just because you have done something, just because you have brought the value, that doesn't necessarily matter. There's a phrase that people like to use.

It's not what you know is who you know. I don't agree with that. It's not who you know, it's who knows what you know is what really matters.

So that's, that is the key. 'cause if you know a bunch of people and they don't know enough about you, like I know a lot of you now because I'll see your face and I recognize your face, but about 20 of you, I can actually say something substantive about you. I don't have much else to say about most of you.

'cause we haven't had conversations. So it's who, who knows what you know, pretty much like Yelp, right? Your brand may look different to different people and that's okay.

And that's why last time we did this exercise, someone has something negative to say. Someone said, uh, those who can do do and those who can't teach, right? And though their assumption was, I don't know what I'm talking about 'cause I teach it, right?

I'm like, all right, well let's have a conversation, right? But the reality is, is that's okay. Like your brand is not gonna be the same to everybody.

And that just comes with the territory. So why does your brand matter? Your brand is leadership.

If your brand is that you are a leader in cloud security or agentic ai, like you guys are now associating Tim Youngblood as a thought leader on AI security. You did not before you got in this room, I'm sure of it. No offense Tim, they didn't know to, right?

They didn't know what you know, right? But now you do, you view him as a leader on that. So any post he makes about it, any podcast he makes about it, any article he writes about it, you're like, well this guy is a leader on this subject.

You're gonna let him influence your thought process because you associate his brand with those things. Your brand is influence and influence is leadership. More so than anything, leadership is influence.

So lemme give you guys a story. Um, whenever I go in front of a board for the first time, I have a very specific expect set of expectations. Number one, I'm, I'm rehearsing like crazy.

I'm going over materials. I'm thinking about all the questions they may ask. But my goal, the first time I meet a board is for them to feel like they hired the right person.

That's it. I'm not trying to have them know everything about our security issues, what my observations are, where I wanna take the organization. 'cause most likely I haven't spent that much time there to have that kind of information.

I want me to leave the room and them feeling like they hired the right person. If, if I have that, the next time I come, they're gonna hear me and they're gonna respect what I have to say. So that's, that's how I approach it.

So the first time I walked into the boardroom at my current company, the very first question that was asked right after I was introduced by the CIO and he gave my resume, which they already had. 'cause you have to have the pre-read. The one of the board members said, I have a question before you start.

How do you find time to do all these things in the community, advise all these different companies and still be a ciso, which I'm hearing is one of the most demanding jobs and people are self-medicating because it's so demanding. How, how do you have the ability to do all those things and then help secure our organization, which we know needs, needs some work? And, and that I, I stepped back for a second and I thought about it and fortunately I did my homework and I responded with, well, how do you have time to be on four boards and be a CEO of your company?

And and they stepped back. They kind of laughed about it and like, all right, go ahead, continue. But, but what happened was I didn't have to establish credibility.

My reputation was already in the room long before I got there. They all Googled me. Some of them took my course on how to become a cso.

Some of them took my other courses. And so they knew me. They had, I was credibility, there was trust.

And so I did get to jump into like, well, hey, you know what? Since I don't have to spend the first 10 minutes, like I plan on spending it, let's talk about these things. But it made life so much easier because they knew who I was before they ever met me.

Whether you're going for a job interview, whether you're a consultant, whatever it is, if you don't think that people aren't Googling you, just like you're Googling them, you're sorely sorely mistaken. It makes life so much easier when you already have that credibility. And so I talked about the boardroom impact.

I work for a trash company. Trash companies typically don't hire the best talent. And people typically aren't raising their hand to come work for the trash company.

Just doesn't happen. But when I put a job post out there, literally within two days, I've had over a thousand applicants for every job that I've hired over the last year and a half. Not because it's wm, 'cause mostly y'all don't know all the things we do.

Y'all know it as the green trash cook company. But because people think they know who they're coming to work for, they, they, they know the leader, they identify with the leader. And quite frankly, I say things that are against the grain a lot of the time.

I don't want you to apply for the job if you don't agree with those things either, right? I'm okay with that. If we don't have the same approach on things, if you don't think people first makes a lot of sense and you think people first is a, you know, a a newfangled way of looking at things and you want to go old school command and control more power to you, I don't want you applying for the job anyway.

So the people that are applying in many cases align with the core values that I've kind of put out there as part of my brand. And people from all types of industries are applying only because they think they know what they're gonna get into. And that just builds organizational confidence.

And it makes your life as a leader so much easier. Because once again, you have influence. Once you have influence, it's a lot easier to get money.

It's a lot easier to get partners, it's a lot easier to get support, whatever it may be. And vendors, some in the room, well see that you have influence and they'll throw their products at you almost free sometimes because of the influence that you have on the rest of your industry or the rest of the, um, the rest of cybersecurity in general. So it's, it's a lot of value, um, in having a positive brand.

So let's talk about how I built my brand. This whole thing is a retroactive look because Ira forced me to talk about this. This was never something I wanted to talk about.

Um, but when I think about it, I did not build my brand on purpose. What what actually happened was, uh, during COVID or around that time Kobe Bryant died, and I grew up in la I was 10 years old and he was drafted, he was like my idol. And I'm just online googling, I'm, I'm looking at all of his videos.

And I was so impacted by the leadership lessons that he gave because I was thinking, man, his five-year-old kid may not understand this today, but they can Google him 20 years from now, 10 years from now, and they can like, hear these powerful life lessons from their father. Like I, I wish my kids had something like that. So I just started posting, I just started posting on LinkedIn and guess what?

I got like five likes, seven likes, like no one really cared. Um, but that was okay 'cause I wasn't doing it for that. I wanted them to be able to look back and see their father did something substantive in his career.

And then, um, I did a conference and I was at Octane and this was their first conference. I can't remember the year, but it was the first conference they talked about Zero Trust. And I was on a panel.

And uh, that guy is Dr. Zero Trust, chase Forester. I'll come back to that in a little bit.

Um, um, but on that panel, I said a few things, but I actually got to build relationships in person in real life. And, uh, the, the first, the second takeaway is, in real life experiences are so much more impactful than digital, but your digital life still matters, right? And so today, especially because of ai, we have people that appear to be the brightest, the smartest, the most eloquent, the most articulate, the most knowledgeable with no typos ever.

And of course a long dash in the post. And, uh, those are the people that have, you know, dubbed themselves as influencers and hoping none of you in the room, 'cause I didn't insult you just now I'm okay with it, but I hope I didn't. Um, and so that is what people are seeing and people don't trust that anymore.

They're, they're looking for authenticity, they're looking for, for realness. And in real life experiences will help you figure that out really quickly. I don't know about you, but I've met people in person that I saw online and they couldn't even form a sentence, right?

And those are the people that lose their credibility and their brand is shot so quickly. But when your online presence matches your in person presence, that right there is a credibility builder that will never be broken. So that conference, it was zero trust and the question was asked, how are you getting your executives to buy into zero trust?

And my response, I'm not the king of real talk like Ira, but I do try to keep it real. My response was, I think Zero Trust is a horrible name. It has a horrible brand.

I've been trying to build trust with my executives and I'm gonna try to tell them zero trust. I believe in the concepts, the architecture, the principles, all of that. But I'm never using the word zero trust.

I think it is the worst name it could have given it. I said this to Doctor Zero Trust, chase Forester at the Octane Conference in front of like 300 people not knowing I wasn't thinking about it. I was just, you know, sharing what I'm, what I'm doing.

And he was like, you, you do know my name is Doctor Zero Trust. And I was like, oh my bad, you have a horrible name, but I like what you're pushing, right? Um, he like a year later came out with the Doctor Zero Trust podcast, right?

So he still, he still rolled with it and, and it's worked out well for him. But, um, the next lesson is when everyone zigs, you zag, when I did that, the whole audience was like, well, this guy's at least gonna tell like he thinks he, like, he sees it, whether it's right or wrong, he's not gonna just try to fit with the rest of the crowd. And so many times people, oh, the hot thing is ai, I, let me just talk about ai.

There's nothing wrong with that. But if you're saying what everybody else is saying, nobody's gonna remember you or associate you with that thing necessarily. And so what actually ended up happening is I got a bunch of audience questions.

I got a bunch of LinkedIn ads and so forth, and that really kind of started a little bit more of my ascension, um, but also built a friendship with Chase. He was like, man, the fact that you had the balls on stage to say that to me, I know you're gonna tell me like it is. Come join my company that I'm building as an advisor.

And I actually joined his zero trust company as an advisor. And, and long story short, I made 30,000 in advisor fees for telling him his name sucked. Um, and so there's, there's something to be said about using your voice to create conversations that aren't being had.

When, when you do that, you become that brand, that theme that you're talking about, especially if you talk about it enough and it'll separate you because if you don't separate yourself, you're just gonna be a commodity. And so you're either a differentiated brand or a commodity. And what do I mean by that?

Number one, if you're a commodity, you look like everybody else. You sound like everybody else. You get lost in the noise that everybody is saying it's nothing unique about you.

Unfortunately, there are gonna be people in this room that I forget there's gonna be people in this room that I don't forget because you found a way to differentiate yourself. Now, maybe others won't forget you and that's fine, but if you differentiated yourself, there's this zero way that I'm gonna, I'm gonna forget you. And another thing, people try to find jobs.

If people want to demand higher salaries and so forth, if you're like everybody else, I'm gonna go for the other person that is just like you, that's asking for less money. If you're differentiated, you can demand more. Whether it be speaking engagements, advisory roles, a job, you name it.

It's so important that you different yourself from the rest of the group. So let's do a little activity. Put yourself in this position.

You just joined the cruise ship. You didn't buy any drink packages. And they say, we're gonna offer you any one of these waters for the entire time on the ship, but you can't get any other water.

You can either go with the arrowhead at the top, the Dasani in the middle, the Kirkland brand over here, this nondescript glass bottle or this nondescript square bottle. Who's going for the Arrowhead? I was weird.

It's all free. It's all free. IRA's going for the Arrowhead.

Who's going for the designing? Okay, who's going for the Kirkland? Man?

The, the Costco cult continues. Who's Going for the glass bottle in the bottom? And who's going for the square bottle on the bottom left?

So this exercise went a lot differently than last time, but more people still going for the square bottle and the glass bottle. And there's a reason for that. It's 'cause there's a story behind it.

You can make some assumptions just by the shape and the form from something that you've seen in the past. You can associate it with an experience that you may have had in the past. You see that bottom bottle?

Of course you're thinking it's Fiji, right? Like that must be Fiji water. I'm going for the Fiji water, even though it doesn't say Fiji water.

I'd rather take my chances on it. The glass bottle who uses a glass bottle and puts cheap water in it, right? That's like just assumptions that people will make.

The story that you have, that people associate with you is so much more powerful. When you're talking to people and you're meeting people, you share a little bit about your story, how you got to where you are, they're going to remember you so much more. And then you take that and combine it with the brand thing that you're aligning yourself with.

You're going to be memorable. That is one of the differentiators, um, that you have is, is your story. So when you get opportunities to share it, have a two minute version of it.

Have a one and a half minute version of your story and people will associate that with you. It is the core unique differentiator because we all have done some of the same jobs. We've all, uh, worked for the, some of the same companies.

We've had some of the same deployments, same experiences. But your story is unique to you. So how do you differentiate yourself?

People ask that question all the time. Like, well, what do I do? There's so many different things to talk about.

How do I differentiate myself? Well, it's not about you. It's about your audience.

And so what do I mean by that? When I'm up, when I'm posting, I realized that the most powerful capability I have is to talk to the person that I once was. Whether it's who I was at 24, or that's who I was 15 years ago or even three years ago.

You have learned so much in how much you share based off what you've learned is going to be the thing that separates you from other people. So I literally talked to myself at 24. I was a leader for the first time.

I, I got my first Cs a role at 24. People always ask me about that. I was a horrible leader.

I didn't know about people leadership. I didn't know about generations. I didn't know about ebitda.

I didn't know about anything as a leader at 24. And so I say, well, what would I have wanted to know when I'm 24? If you go back and look at every single one of my posts and look at it through that lens that I just shared, it'll make sense.

I'm just talking to people that are trying to level up in leadership, learn how to trust, um, learn how to deal with hard situations, whatever it may be. Everything that I do, every class that I teach, it's about talking to myself at 24 for you and maybe talking to yourself 20, you know, two years ago before you learned ai, there's always somebody where you were two years ago. There's always somebody where you were four years ago, six years ago, eight years ago, you name it.

Another thing that's important, ain't none of y'all perfect. All of y'all have made mistakes. If you're a leader in this room, you've taken down a system on accident, it's happened.

It is come with the territory. Um, if you're new to security, you're gonna take down a system on accident. It comes with the territory.

It's about sharing the lessons that you learned from that. People always talk about like, oh, it's not failure if you learn from it. It's really not about learning from it.

It's about the retroactive. Look back from those lessons learned and how you apply those lessons. And Pixar has a rule, they have 11 rules that they give to all their writers.

But the number one rule that they have is that the audience should identify with the struggles more so than the triumphs. If you identify with someone's failures, number one, you have a different type of credibility, but there's authenticity and there's trust. If I tell you five times how I sucked at something, when I tell you I was great at something, you're gonna believe it.

'cause I was willing to tell you when I sucked that in. So many people make the mistake of only talking about their successes. That will get you lost in all the noise of all the other people that are always talking about all the great things that have gone for them.

All right? So there's this wall, there's obscurity and notoriety. When you have a good brand, a solid brand, a popular brand, you have notoriety.

And when people don't know who you are, you're a commodity. You're on the side of obscurity. And it's common for people to try to get from the obscurity side to the notoriety side.

'cause on the notoriety side, you're just pulling it all in. Opportunities, jobs, speaking engagements. I'm gonna make an assumption here Tim, but you haven't applied for very many jobs.

They just show up, right? I have not applied for a job in over 15 years, right? It's not about applying for a job, it's not about applying for advisory roles, it's not about applying for speaking engagements.

I didn't ask Ira to do this, right? And I'm happy he did. But the reality is, is a lot of our speaking engagements except for R rss, a man I keep trying to apply and they don't let me in.

I don't know what's up with these people. I don't know. I used to work for RSA, but it's a different conversation.

So everybody wants to get on one side to the other side. 'cause on the other side, life is a lot easier to be honest with you when it comes to those things. So what do people typically do?

They take that sledgehammer and they just hit all across. I'm gonna talk about ai, I'm gonna talk about GRC engineering. I'm gonna talk about the fact that we do or do not have a, a shortage of talent in the industry.

I'm gonna, I'm gonna talk about how much vendors suck or all the things that everyone's talking about over and over and over again. And when you do that, you're actually not going to compromise the wall because you're taking a sledgehammer and you're hitting it here, here, here and here. That's not how you break past the wall.

You break past the wall by hitting the same spot over and over and over again. And what does that mean? Pick two topics.

Make those be the two topics that you talk about over and over and over again. Whether it be on podcasts or posts or articles that you write or conference talks. You talk about the same thing over and over again.

And then you become the supply chain lady and you have a brand that everybody knows as associated with you. And now she decides to talk about something else. She's already established credibility.

She can grow her brand to be something else. But she got past this wall from her precision on one topic. It's so important.

And then once you get past this, you can add all the other topics in the world that you want to add. But people need to know you for something to know this. This is one I call this person.

So I did a poll on LinkedIn and I have Cecils, how did you get your job? Only 18% of them said they applied to it. And I think it's lower than that now, to be honest with you.

It's, it's who knows what you know, it's who knows what you've done and who, who knows what you stood for. And that is all your brand. Even internally we had to talk on the Deputy CSO panel.

If your brand isn't strong, they're bringing in an outside ciso. Even if you're the deputy ciso, your brand is so important. So I ask people all the time, what is holding you back?

It's fear. I'm not the guy that posts on social media. I'm not that person.

I don't wanna take pictures of my food. Like These are things that people say to me all the time. And I get it.

I'm an introverted person and you probably wouldn't believe it until I until I say it to you, until you realize that I'm the one that ducks off before everybody else at night and has to recharge because all this people is people me out. Right? Um, but the problem is you're looking at it in a very selfish way.

Your fear of posting is probably one of the most selfish things you can do because when you post, you're helping somebody else out. That's all it is. And so I guess I'll end it with, if your only job, your only attempt is to help other people, you're always gonna succeed.

And if you start helping people, you're gonna build a brand in whatever way you've helped people. Happy to take any questions. Uh, what's your thoughts on over exposure?

Especially on LinkedIn. I don't know if it's possible. I asked them for reason.

There are people and talk to the community and they're like, uh, I don't know what I think about X, Y, Z. 'cause they're always posting something that seems to create a negative impression. So It can, if they don't have the in real life experiences to back it up, right?

If it's all talk, yeah, it's all talk. But you meet 'em in person, they talk in person, they're doing the job in person. If you talk to anyone that works for me, they're gonna have the same things to say that the people that see the things online.

And that's important. Now, if you go in like that guy's an a*****e, right? Well then yeah, that's gonna completely degrade the brand very quickly.

So you have to back up what you're saying. Something Derek, that I would say to that is, uh, I've been approached by I think three different companies that represent, um, uh, uh, they're looking for influencers on LinkedIn. Oh yeah, me too.

And Right. And I signed up just so I could see the, what information they get people to post about. So I can see who in my network is posting to get Yeah.

50 cents a click and that. But the thing is, the people who are, who are just posting to post, they get a reputation as not being serious in the industry. That's True.

Right? And, and you don't That's true. You know, people don't, but then some people are just like, you see 'em post almost every day for just three or Yeah.

Nonsense. And it's like all you did was just like regurgitated something. You saw someone else post.

No one's gonna take them seriously from a branding Perspective that, that's accurate. And I, I have been approached. 'cause you know, if you have 20, 30,000 followers on LinkedIn, people are gonna want to say, Hey, post this on behalf of our product and and we will, we will pay you.

My my response is always people trust me 'cause I'm not pushing products. Right? Right.

The minute I push your product and make whatever I make on it, you never do it. I lose everybody else's ear. Right.

And so for me it's, it's not worth it. Wherever you are financially, you do what you gotta do. Of course.

Um, but uh, yeah, that's my statement. Yeah. I, I was gonna mention like people tag me because they know that's my focus and they want, you know, that assistance with being able to expand, um, their, their network and that visibility.

And I'm selective on those. I mean, I don't repost everything, but for those that, you know, I do or, or I see something that I saw posted and I'll repost that to make a new connection. It really helps sort of broaden it.

But I am selective of what I repost. Yeah. I'll untag myself from Post Me too.

Like from the wrong people. Absolutely. You Know, whether it be from RSA or whatever.

Yes ma'am. My years shut of mind told me that one person who got very successful and I actually hired a PR firm via branding Wyoming. And so how do you, how many people do you think do that?

Um, I just heard recently she, so she said she heard that some people have hired PR firms to handle their branding. Um, I heard recently, I can't back this up, but I can find out pretty quickly 'cause I do work with LinkedIn, um, that LinkedIn is clamping down on people posting on behalf of other people. Um, because LinkedIn is gonna start losing its credibility and there will be a new platform that pops up that will replace them if they allow that thing to continue to happen.

People Say to your Facebook, There's a lot of people that refuse to let that happen. Like take that post to Facebook. I see that all the time.

Um, but there's this weird world of work life balance, work life intersection where, you know what my joyful thing with my kid is part of my work life. 'cause I was sitting at work when that thing happened, right? So I don't really get into those battles.

Um, but for the most part it's still been very professional for me. Alright. This is a, a different kind of question.

Um, so I do have a history and I do kind of have a bit of an image online. Well now there's a new guy in Southern California with the exact same name that's 20 years younger that's trying to ride the coattails. Ah.

And so he is going down and he's more of a salesy guy, guy trying to take all of the things that that security guy has done to sell the things in this space. So what are your, what kind of approach? I mean, I haven't met him, but I haven't said to anybody to go beat him up or anything.

But, you know, I mean it's just, it's like, it's been brought to my attention many times within the last year and a half. I mean, will the real Dan Meacham please stand up is the first thing that I'm thinking. Um, yeah.

Exact same name except for Yeah, you're right. Middle initial, middle Initial. That's why I, um, so LinkedIn has the ability to, um, be verified.

Alright. Um, it is like 19 bucks a month I think it is or something like that. I can't remember what it was at one point.

It was, I think it might be free now, but if you verify yourself, then people can know that you're the legitimate Dan Chu. That's, that's one thing that you can do. Um, if they're call, if they're saying they're you, like, I work at this and I do this.

Or if they're just saying, I'm a security guy, you're a security guy. I don't know if there's much you can do about that, to be honest with you. Right, right.

Yeah. My, I was, I was the, the other person. 'cause my name is Sean Harris.

Yeah. I show up on IC swear thing and there's all kind of people you Wrote my book and asking, Asking for my other picture with me. I'm like, do you know who I am?

I work at nasa. I'm not like, I'm not her. She's not alive in her hair as longer, Uh, as a vendor.

When you see a profile of a vendor, what is the reputation you're looking? Not that I'm gonna build my personality according to what you answer, but I might Yeah. In general, what is it that you're looking As a vendor, if you're adding value to immunity, that is a pull approach, right?

Because I'm gonna see what you're saying and I'm gonna pull you into no more. And how are you solving that problem when you're pushing? That's when we put up the wall and that wall is really hard to get past.

And so anyone that's just adding value to the community, so let's just say when the sales loft thing happened, right? There were vendors like with us, that wouldn't have happened, right? We don't want those vendors.

We don't wanna talk to those vendors. We don't wanna see those vendors. Those are ambulance chasers.

But if you're like, Hey, here's a, here's our analysis on it, you know, enjoy all day, we're gonna take that in. Hey, we created this free tool, or Hey, we're gonna give you a free POC, don't have to go through all the paperwork, just download this, whatever. Like, we love that.

But the minute you capitalize on someone else's hard day, you've, you've lost a lot of us very quickly. And there's a CISO Slack channel, it's real uhhuh, it's out there Yep. And a WhatsApp.

And we talk about you often About me personally, about you. I Don't think Tim isn't shaking his head yes and no because he's like, I'm a CISO and I'm a vendor. But Jared, can I Make a comment On that?

Yes, sir. Because I, I am notoriously evil to vendors and as you know, being outspoken and what I always tell vendors, and frankly, it's like what you're trying to say, like to us is like I tell vendors and I'm paraphrasing, be a magnet. And most, and what I mean by that is you put out information.

Like if you connect with me and I accept it, I'll see your stuff come across my fee and I'll choose to engage with valuable materials. I'll choose to go ahead and follow up with you. But more important, and I'm assuming it's the same when I was at Walmart, coming to me is a pretty big waste of your time.

Yep. Because the people who make the decision on 99% of products inside an organization are the people doing operational work that you're not gonna give a squishy toy to at RSA, or at least I find most vendors ignore the people who are out there generating needs for products or determining needs for products that they will then elevate to us. And so if you are a magnet and generate positive value to people, not us, maybe we'll engage.

But the people who need your stuff are the people who are gonna look at it that you are not engaging with. And that's how I see it going. Most valuably be a magnet for, you know, everyone.

100%. I, um, I don't make tool selections. I tell my team the strategy, they decide the tool.

I think it's like not cool to force them to use a tool that they don't like to use when there's other tools that are out there. So they get to make their decisions. Yes.

I just want to share my experience. A few years ago I was too scared of posting on LinkedIn. I was thinking too much about my likes and followers and stuff, and that was, you know, holding back to myself of putting the real value stuff there.

Right? And then, uh, one of my mentor told me, um, if you trust yourself, if you know you have a service to provide or value to provide, do not attach yourself to the outcomes. And by default things will fall over.

Right? So I'm, I'm posting out now don't to get the likes and followers, but automatically by default you will get it. So just, you know, outcome is one thing.

And, uh, providing value is one thing, A hundred percent. Uh, number one rule I have is add value to everyone I meet. Whenever I can do you do that, that'll be your brand, Mr.

Mr. Bryant. For, for the executives or the leaders that are looking for new opportunities because of what's happening in the economy.

And they haven't been on social media and have the ability to active moment because they've been at their job for seven, 10 years and they just, you know, I, I'm not, I'm good. I don't worry, not worry about it. What would be the first step that you would suggest they would take to help them identify what direction they need to go in to start helping increase their visibility of their brand?

If they don't know exactly what their brand against, Well, most likely that person doesn't have a complete LinkedIn profile off the top. Right? Right.

Like, what are the things that you've done, some of your experiences, um, like please don't put phrases like hardworking and experienced. Like none of this is gonna work, right? Like, like let AI come up with something much better for you.

I trust, trust me. It will. And just, just build out your, your LinkedIn profile off the top.

Recruiters and everybody know when you've made a change to your LinkedIn profile, every time you edit your profile, it moves to the top of the list for recruiters and everybody else because their analytics show updates. And so that's, that's number one. I could have given all kind of LinkedIn tips, but, um, just editing your profile once a month will move your name to the top of people's list.

If you've changed nothing else, like you can literally put a period in and take a period out and you've moved up to the top of the list for, for people because they, part of the analytics is who's updated their profile recently. That's it. Right?

And so just updating your profile with something relevant will then at least introduce to people, like, here's some things about me open to work and all those types of things. Will, will also help. Is that, is that including like if you're posting and you have any No, No.

It just recognizes profile changes. Yes. And I see the word end, so that means I'm gonna stop.

Usually Ira comes out and pushes you, but I'm gonna follow her. Thank you.