Techstrong TV December 3, 2025

Hey, we're in reinvent. You don't know if I'm live or an agent. You're watching Textron Gang.

Hey, everyone. Red Pill, blue Pill. Am I Agent Smith or Shimmy?

That's the story here at Reinvent. That's billions and billions of AI agents coming our way. Um, this is a special edition Textron Gang.

It's just me and Mike Ard. We're gonna bring you up today. Today's Wednesday.

Uh, a WL reinvent really kicked off yesterday. So we, and we did do a live sort of recap on that from yesterday. So if you want to go back on Textron TV or wherever you're watching this YouTube or what have you, you could watch it there.

But, uh, we're gonna update what we found out yesterday and what we're hearing today, Mike. It certainly is a, a different reinvent. This Is true, and there's a lot of chatter in the hallways these days about this open letter that the AWS employees put together last week, warning that the company wasn't paying enough attention to the, uh, economic and employment and, and climate impacts of ai.

And folks are kind of, you know, starting to ask some questions about, geez, well, what are we doing here? And it feels like, you know, we're still in this go fast and break thing mentality. And maybe they and the rest of the world are starting to ask some more thoughtful questions about what does all this mean?

I gotta give you my shimmy take on this. What does it all mean? What they're really saying is, how many jobs is this gonna cost?

I happen to be speaking, you know, everyone sooner or later comes to Boca Raton, even Florida, to visit a grandparent, an aunt, an uncle, a parent, or you get all yourself and you'll move there. So I was in Boca Raton last week, and the people renting the townhouse next door to mine, their son, lo and behold, works for AWS Oh, he's speaking here. I'm not gonna mention names.

Um, but he said that they have laid off, I think something in the area of 40,000, is it, or 30,000, Or at least they have plans to get to that number. And that's only phase one. Yeah.

Phase two is in like, amount of, I think 50 or 60,000 people. And so, while I admire the gist of the letter that we're doing this for, you know, uh, guns, apple pie, and wrapping ourselves in the flag, there is a certain amount of self, no doubt, preservation here at work. Mm-hmm.

Now, do we need regulation of ai? Yes. Is a w do we expect AWS to self-regulate ai and it goes against their shareholders' best interest?

No. So where does this kind of play out at the end of the day? Let's imagine that we automate so many things now that we don't have enough people working to buy the things that we're making through automation.

And we just create this kind of cycle where, you know, we kind have a zero sum game. It's, it's, it's the end of poverty. And Blake EL says, Well, so you think universal basic income is gonna come Star Trek?

What? What, what's the guy's name? Zephyr Cochran.

I spoke about him on Shimmy says Uhhuh. But, but no, seriously, um, the issue is they're not wrong about the dangers of this, about the repercussions of this. Mm-hmm.

But to expect a single company to stand up to do the right thing against its own, maybe baser instincts or the, the better, you know, uh, the, the profits to its shareholders is, is, you know, it, it's a little naive. Now, who's being naive? Is There a demarcation between the interest of companies and the interest of society at this point?

That of, Of course there is, that's capitalism That's becoming more pronounced than it's ever been because, you know, the implications in the scale are much different. Never say it's, it's ever been, because the fact of the matter is, um, it was never as bad as it was during the robber Baron days. Let's call 'em Right.

Of the JP Morgans and the, and the, and the, uh, Vanderbilts and so forth. However, you know, I'll go back. I'm a political science student.

Why do we have a government? Why do, why do we have a social contract mm-hmm. To form communities for the common good.

We can't expect corporations to work for the common good any more than we can expect any individual to work for the common good. This political theory 1 0 1, by the way. Right.

You can't expect individuals to always do what's right for the common good. We need, this is why you have a government, this is why you have governments. This is why you have community.

We need, whether it's truly the government or a community, perhaps, of corporations, of individuals and corporations that are working on behalf of the common good. It, it's unrealistic to think AWS should sacrifice profits or its competitiveness vis-a-vis the market for the common good. So, does somebody need to, you know, is this gonna become a, a plank in a political platform in the next election?

And does this become, Hey, Vote for Shimmy? Um, I didn't know you were running. Well, I'm waiting to get drafted.

Okay. I would, nor will I accept a nomination. Someone said that you'd just love To Be asked.

Yeah. I just want to be asked. But no, seriously, I do think, I do think that whenever we stop futsing around with 80-year-old people running to be president of this country, and we do pass the torch to a new generation, that this new generation is going to ask these questions.

What do we have to do to, what do we, should we reign in big tech? How do we reign in big tech? How do we regulate AI while maximizing its benefit to society?

That's not a question that an individual corporation should answer. It's a question we need to answer as a people All. And that is exactly what we're talking about in the hallways of this conference these days.

I don't think it's happening on the show floor. I don't think it's in the set. No.

'cause they're all about the tech. But, uh, you know, over a beer, people are going, Hey, where are we going? Where are we taking everybody on this ride?

You know, it's funny you bring that up. It reminds me, of course, next month we have our Predict 2026 mm-hmm. Event.

And this, you know, it, uh, tech Strongs, I, I was not Techstrong's person of the year. Text for Textron's Entity of the Year is ai. That's the theme.

And, and the promo for it is actually two ais having a beer or sharing a cocktail saying, isn't it great to be an AI right now? Um, right. I mean, we, we, we are the masters of the universe.

Um, and I think that is the conversation. Do we ask Theis to do what's best for humanity and regulate themselves somehow or something? Is that a, you know, that's a little, you, they might, the guys will fish.

They might argue with each other forever and come up with different answers, just like Republicans and Democrats. Yeah, you're right. And, but maybe, maybe they're immune from politics, Maybe.

Or I, I, you know, I'm just throwing it out. I I Think they would conclude it's the one that builds the most data centers for them is the right answer, Because they have their self-interest. There you have it.

So do AI have their own self-interest that way? Wouldn't that take a level of consciousness that I don't know if they're, I or I just have a voracious appetite for memory, so therefore I go from there? Hmm.

I think I, I, I think you've crossed the line there. I may have. Um, but in any event, I am much like security.

I think regulation of AI is going to be a bolt on an after first we gotta get it working and doing all the things it's doing. Then someone's gonna say, well, let's reign in those horses, even though it may be too late. I would give AWS props on two points.

They did roll out some ability to implement guardrails into AI agents using Code Us. And they also have a tool now that analyzes the behavior of the AI agent to see if it's, you know, acting in the appropriate manner to whatever mission it was given. Which, you know, could, you could think of and see how that might be expanded over time.

I, and, and kudos to them for doing it. I, I don't think that's about really regulating the ais. I think it's just making the ais more efficient.

Your agents more efficient. But Mike, I feel like we're biting at the edges here. Let's, let's get to the meat.

Well, the other side of the thing that people are talking about here is the other companies that are here besides AWS, are also doing significant things that make a difference. Um, red Hat's here talking about a partnership with AWS, where Red Hat is now taking their stack of OpenShift and all their other platforms and making it available on the AWS AI accelerators. Yeah.

Not just the GPUs from Nvidia. So that tells me that we're gonna see the software stack needed to use these other processors, and that's gonna lower the total cost of ai. And I think that's a big part of this conversation.

No, we are, I, I think, you know, it's funny, this is, if not today, within the next day or two, is the three year anniversary of Chad, I think it was, I think It was yesterday. It was yesterday. Mm-hmm.

Was the three year anniversary? I said within a day or two. Mm-hmm.

Uh, was the three year anniversary of the release of Cha GPT? You know, maybe we didn't know it that day, but shortly thereafter, I think we realized the game has changed. Mm-hmm.

Um, here we are three years later and, and we have all that. But, uh, red Hat has certainly, but Red Hat's always been a bellwether AWS partner, I think. Right.

And, and AWS has always been a good partner to Red Hat. They're, they're frenemies, right. Because Yeah.

Red Hat also talks about portability, and you need to make sure your workloads can run anywhere they want. And, and that's a hybrid world, right. In a multi-cloud world, which for A-W-I-A-W-I think AWS acknowledges that there is such a thing as hybrid cloud and multi-cloud and everything else.

But let me, let me give some kudos to AWS as much as this, uh, reinvent this year is about AI and AI agents, they are really playing up their partners. You know, this partner ecosystem is now 140,000 partners strong worldwide. Mm-hmm.

That's incredible. Incredible. Let me say that again.

140,000 partners in the AWS ecosystem. And they're not all the red hats of the world or the Seuss of the world who we're gonna be talking with, uh, later, or actually we spoke about yesterday, we spoke with them, and we'll be talking again with them today, uh, who have a, a whole bunch of AI themed partnerships with AWS that they've announced here. Um, it's the jobbers, the people who were converting the SMBs, you know, migrating them to the cloud, modernizing the stacks, transforming their businesses by putting them into AWS, um, it, it's, it's those people.

It's, it's the small, medium and large software developers and ISVs, independent software vendors. It's an amazing PI mean, aw, look, Microsoft's always giving credit for having an amazing channel. Mm-hmm.

Google's tried to build an amazing Channel. The the partners are having the same issues they had with Microsoft. Right.

And it's just, it's, it's the nature of the beast. But, and you don't know what AWS is gonna do six months to a year from now. And so if you're a partner with them, you're always kinda like, well, uh, today we're selling together, but a year from now, they may have something that does what we do, and then it'll be more of a complicated relationship.

And there'll be this, I think gray nor coined the phrase coopetition, but the, the ecosystem, you know, has some inherent Conflict. Conflict. Well, there, there, let me call a spade a spade here.

There has been a sort of frog in scorpion type of relationship between AWS and partners over the years in that if A DWS sees what the partner's doing, is that successful? They don't often buy the partner. They're not one of those companies that does a lot of acquisitions.

They, they just kind of suck the brains out and build it themselves. They, they create an open, they take an open source project and make it a service. Right?

Absolutely. So, so there is that. But I, I will tell you, as tech sunk speaking on behalf of Techstrong for the last three or four years, we've been working with the AWS partner team, and they highlight certain partners that we do webinars with, and we do our mini porters with to highlight their great content and, and their services that they bring to market.

And it is, it's a deep, deep, well of, of real expertise. So, On a positive side, they put out a study this week talking about, and it's not exhaustive, right? I think they talked through an in-depth interview of so-called 35 partners.

And these are not the tech vendors, but more of the service providers. But they, but they, they did note that, you know, depending on the level of add-on services that you deliver, you can make anywhere from a buck 25 to like $7 in change for every dollar that AWS generates. Yes.

And they're just trying to highlight that the ecosystem, you know, can make more money than AWS does. If you take full advantage of it. Now, getting to that $7 mark requires a significant amount of investment and expertise.

And so not everybody gets there, but there is room for pe for companies to live in this ecosystem. Yeah, no, no doubt. And again, I, I would just, you know, there's the Red hats, the Seuss of the world, the f fives of the world.

In other words, there's the big vendor partners, let's call them strategic partners mm-hmm. Who do a significant amount of business with AWS. Then there's, I call 'em service integrators Yeah.

Sis Right. Who are helping people do the modernization transformation. They're doing the lifting, and they, you know, they work on a much different margin basis.

Right. Um, so the $7 for every dollar spent on AWS is, that's phenomenal business for those guys, right? Yeah.

We used to working on basis points, basically. Um, and, and so that, that's great. But there's also this, like everything else, there's this middle class of, you know, they're not red hats, quite quite red hats, but let's call 'em ISVs, people who are building software mm-hmm.

That kind of make AWS easier to use that allow you to do some advanced things that may AWS does sort of mimic at some point down the road if it becomes too successful. Yeah. Well, if you're gonna live in the shadow of something, you gotta realize that maybe that thing is alive and it's gonna roll over one day.

But you know what, yes. You're a hunch. That was a good way of putting it, Mike.

But, you know, but let, look, AWS isn't an ogre here. This is something that, no, this has been done this way forever. I don't think they're malicious about it.

I think that they look at it and they say, you know, we've seen enough requests from customers for a certain capability, and there may be some smaller company that saw that same opportunity and moved a little bit faster, but Well, They interviewed. Yeah. But once it reaches a certain threshold, you're almost guaranteed that AWS is gonna fill that, that what they perceive as a vacuum that you just happen to be in.

And that's when all the air gets sucked out of that particular vacuum. You, I, I think the difference though, Mike, is you, you look at I-B-M-I-B-M was notorious, right? Yeah.

This kind of stuff. Microsoft has the Same rep, Microsoft too, but they both bought a lot, well, not as much Microsoft, but IBM they buy you. Yeah.

Right. They bought you, if you had some good technology, they didn't look to reinvent the wheel, they just buy you. Mm-hmm.

Um, Microsoft made their share of it. Cisco and other company, you know, so many people who were in Cisco and outta Cisco when that is, Cisco started a company and got bought back into Cisco. I think, I think a AWS has more of a tendency to try to engineer it themselves first.

Yes. And then, you know, it's A made here mentality. I'm, I'm hard, I pressed to think of, you know, major acquisitions that they've done.

And a lot of it's not their model. And a lot of it also comes back to, frankly, the way that the platform is built around this nitro virtualization thing that they have, is they need deep hooks into that. And so it's difficult.

If I gotta buy something, they're gonna have to re-engineer it anyway. Yeah. So they might as well just write it.

Agreed. If you don't mind, I'd like to turn to security. Sure.

So there, there's been some security announcement. There's a security agent, one of the big three agents that AWS is pushing. Um, I don't know if I had a classify, would you call it an AppSec agent or more of a, It's kind of a, I call it a DevSecOps agent, right?

'cause it's really aimed at a developer to sit That's meant by AppSec. It's on, it's on the left side of the event. Yeah.

Yeah. Absolutely. Yeah.

It's, Um, but, you know, when I first got into cloud security, when cloud first came out, I was already into security. And, and we got into cloud security. Cloud security was much more about operational security.

Right. My app is up and running. Mm-hmm.

Does it have buffer overflows? It was Part of the SecOps conversation. Yeah.

It was SecOps, let's call it that. You don't hear as much SecOps chatter here as you do call it AppSec chatter. Well, yeah.

I think that's the nature of this particular market. But I won't give you, I'll point you to some stuff that Sumo Logic was talking about this week. Okay.

Um, they're building, you know, they're essentially revamping their approach to ai. They're still calling it Dojo, but they are rebuilding around an MCP server. And what they're saying is that you will have agents for IT ops and agents for SecOps that will share access to the same MCP server, and they will collaborate across each other.

And those functions will increasingly be blended. And they're also saying that you can also bring your own third party agent to that MCP server because they have access to this telemetry data. And that's what all these agents need.

So as you start to think about SecOps, and does it just eventually just become part of the IT function motion? And these agents are all handling that process. And then there's what we call security today is the, the whole, uh, policy driven part of security where, you know, we set up the frameworks and the guardrails.

That still is classic security, but the SecOps part of it seems to be melding more and more into the IT ops function. I think that's a fair, fair way of looking at it. Here's my question on that, though.

We're about a year into this MCP thing. It's become the, it was December of last year. Yeah.

And it's December of this year. That's a year in my book. So how many MCP servers do I need?

How many MCP servers do I want? You could have many, or you could have a few really large ones. They're kind of like Kubernetes clusters in my mind.

The thing about MCP though is it's still early days. I mean, you know, they update MCP every quarter and what it looks like today doesn't look anything like it looked a year ago. Yeah.

Well, but you know, yes and no. It, it, it, what it is and what it does and how it does is become the standard. Mm-hmm.

It's just, do I want to use Sumos MCP server to marry SecOps and and ops? Do I want to use Salesforce's MCP server, ServiceNow's, MCP server, Amazon's MCP server at, at one level, I, I'd like a world where I had one MCP server that allows all my agents to talk to each other. Then does that provider of that MCP server become the dominant sole or entity in all of it?

Because essentially now you're looking to one company to provide the management framework that sits on top of all that MCP servers and everybody else is plugging in an AI agent into that and becoming a satellite of that company. Or will it kind of continue as we currently have it, where there are, you know, maybe 10 large ecosystems that are IT operations frameworks, whether it's ServiceNow or IBM, they all have MCP servers and other companies are kind of providing agents that sit around that. But I don't see how you make money just providing an agent.

So that becomes a interesting challenge. So I feel like there's gonna be some sort of convergence here of all these functions and companies as certain things become agents and other things become, you know, the core orchestration layer sitting on top of an MCP server accessing telemetry data. And that's gonna be where the split is.

But I don't think we're gonna recognize this IT landscape that we, as we see it today, and two years from now, it won't look anything like it has for the last 10. Yep. I have two words for you, young man.

Open source. Open source. I, I think what we need here is an open source MCP server that becomes the defacto standard open MCP server that everyone could use.

And then much like service mesh on top of Kubernetes, people build things on top of that open source MCP server. So you only really have the one MCP server, but, and perhaps you have sort of plugins or meshes that sit on top for different functionalities that you're referring To. It's hard to see how the MCP server provides any differentiated value right now.

From What, that's what I'm saying. And that, and that screams open source to Me. And then the battle then comes about around the orchestration framework that sits above it.

And that's where this thing will be won and lost. I, I agree. I think we're in agreement.

Alright. So let's move on from that then. Wait, let's write this day down.

Yeah. A day that'll live in infamy. No, that's next week.

Um, or later this week. Anyway, so yes. I, I do think security and, and, uh, AI security, of course, like everything else, AI sucks it up.

Now, I wanted to talk about good old fashioned SecOps. We wound up talking about AI security. What else are you seeing out here, Mike?

Um, I think that that is still the vibe. You know, what's amazing to me is just the sheer number of people who are here. And when you go over the show floor and, and you're walking around the Venetian, um, you know, it's like you're, I don't know what the total number is, but I'm gonna say, you know, 25, 30,000 of my closest friends and associates are all walking around over there.

Well, no, I, I, I heard the number was closer to 60. I don't know if it's 60. I think that might be bigger than the Venetian can handle, which is why they're maybe in all these Different No, but they're all, it's not just at the Venetian.

They, they are all over the place. Um, interesting. Good stuff though.

I, I think, you know, we still have two more days of coverage basically. Mm-hmm. Today and tomorrow.

And We will see what comes out of it. Yeah. And I think it's gonna be, you know, what's that Chinese phrase about living in interesting times?

I, how did that become Chinese? I thought it was Irish. No, I thought that was Chinese.

I don't think we, I don't think the Irish made that one, but I've always, I was always told it was an Irishman. It was, it was, it was, I was always told it was the essentially a Chinese curse. Okay.

Okay. And the Chinese probably tell people it was a Jewish thing. Well, that could be too.

And maybe we're all just stealing each other's proverbs And that, and that, that that does go around. Mm-hmm. But you know what else is interesting though?

So, so AWS came out yesterday with this like flurry of announcements. Mm-hmm. Right?

And, and, you know, for, for the PR folks out here, here's a lesson. Don't put your announcement out day one of AWS reinvent because it gets drowned out by the reinvent. Well, you, you know, I guess you get apparently maybe six hours of lead time because AWS starts putting out its announcements on the, you know, right around dinner time of the night before, before, before the official conference.

Right. They have their pre-show press conference, and then they have a regular press conference. Yeah.

And they go from there. But I think day two and three is a great day to put out news. Um, I'm just saying, throwing it out there for you.

Well, I'll tell you the truth of the matter is by the time I get to all the AWS stuff, it'll be day two or three before I get to the third party video. There's that many there, there are a ton. You know, there's a ton of announcements.

I'll tell you something else, though. For those of you watching this live, you're probably not here. And you've asking yourself, should I have gone to reinvent?

Should I go to next year's reinvent? What gets lost in the sauce of all of these big announcements is the literally hundreds of sessions, no matter what you are into, no matter where you looking to expand and learn or dive deeper, there's a session here at Reinvent where you could go in and hear someone talking about it, where you can meet like-minded people in the audience that are into that particular aspect of this thing. And it's a great place for networking, for learning.

There are AWS education training classes, I think that happened the weekend before. Reinvent. Hey, There's some comfort to be taken in the fact that, you know, if you are a little anxious about ai, well, you can walk around with everybody else who is equally anxious and doesn't know exactly what's gonna happen either.

But there is that. But at least it feels better. And, and just another enticement to come to reinvent, it's a heck of a lot cooler here than it is in August for Black Hat.

So, I, I will, I, I'll give him that. But Mike, I know you gotta get busy back out on the floor. We're going to be here, uh, continuing our live coverage.

So stay tuned for that. But you've just watched another episode of Textron Gang. Hey, everyone.

We're back here, live on the, uh, floor of Text of Textron. We're tech strong, but we're back here, live on the floor of CubeCon, and you're watching Text Drunk tv. It's been a long two days.

Uh, let me introduce you to my next guest. His name is Ido. Neiman Ido is with Firefly.

And if you haven't heard of Fly or Fly, don't worry you're going to. But first Ido, welcome to Text Drunk tv. It's great to have you here.

Thank You for having me. So let's talk about you first. Alright.

Give us, give us the Ito Neiman story. So, uh, I feel at home on the tech trunk floor. Oh, within the bigger, uh, Kranta.

It's this little gray area right here. Yeah, exactly. Um, and, uh, I feel at home because, uh, generally I'm a nerd, uh, grew up around computers.

I served 12 years in the Israeli cyber intelligence 8,200 Or, uh, something little bit better. 81. Oh, 81.

Okay. Yeah. Uh, the less the, the less digits you get, the better it is.

Okay. Uh, so, uh, then I led technology for prominent Israeli hedge fund. Okay.

Um, then I had a startup in the serverless field, Uhhuh. And for the past four plus years, I'm building fireflies together with my, uh, partner and great team. Fantastic.

Yeah. And, you know, look for those of us in the industry, not for nothing, but that's a pretty typical Israeli entrepreneur story, right? Yeah.

We, I mean, a lot of them, for people not in tech, they're like, wow. But, you know, but this is of course why they, you know, Israel's a startup country and, and these kinds of things. It's, it's, it's what you, you do there.

Um, you know, I've been interview, I've started four or five companies myself over the years venture, and I've interviewed hundreds of co-founders and, you know, entrepreneurs, no one starts a company lightly. Right? Everyone, you put your, your, your, your guts into it, right?

And, and then your passion. Talk to us about what was your passion in doing Firefly? So back in my Army days, we, we obviously did some very complex technology building, but the scale, it's a nationwide scale, right?

Did some very large cyber activity that's put it this way. So always fascinated by doing large things at scale with efficiency. And back then we were looking at the outside internet and seeing all the tools and wealth of, uh, of possibilities.

And because we are, you know, air gapped, we need to build everything ourself to be military grade, we can only build those. So I remember seeing stuff like infrastructure as code coming to life, wanting it, but need to build our own scripts and internal tools to, to, to do it. So, you know, when I got out after a good run, uh, started coming to shows like this, um, and this, this got me pumped about the possibilities, but also to start thinking about how we harness all of this, because it is quite a lot.

You said the long two days, and we still have at least one day to go. Yes. So many different conversations.

Vendors, technologies, 500 talks outside. Yes. So imagine that you, you come to CubeCon and say, Hey, you know what?

I'm gonna be cutting edge. I'm gonna learn everything. You just can't, you can't Anymore.

Right? There was a time when you could, right? Maybe the first one in San Francisco, 2018.

Yeah. Yeah. Not even 20 by 2018.

That was already, I think Seattle. It was big 20 16, 20 17 maybe. But yeah, now it's just, it's 200 projects in CNCF alone.

Yeah. Lot to wrap around. So when, when, when you say passion is about, Hey, let's build the best technology, let's make it scalable and accessible and simple, but also let's allow the end users to actually adopt what's happening here.

Because everyone wants to create the new shiny CNCF incubation, uh, project. Everyone wants to be, Hey, I got the, the next 10,000 stars and everyone loves me and I'm great. Right?

Everyone. But at the end, we want people to use our technology, right? We want the end customers, of the end customers to gain value out of it.

So at some point they said, Hey, I think this is both interesting, hard, and might, uh, might be useful for to the world. I love it. Yeah.

Now, if I, if I had to ask you define Firefly's mission. You talked a little bit about your passion and how it relates to that, but crystallize it, and you go, look, this, this camera's on. You tell, give them Firefly's mission.

It's reason for being, Our mission is to make cloud simple, managed and controlled. So, uh, Firefly is an agentic cloud automation platform. Basically, we help you understand everything that runs in your cloud.

Because as mentioned, 200 projects, 500 vendors, so many practitioners, how do you even know what you have in the cloud? We just saw it, uh, three weeks ago. We're now talking in November.

But, uh, in late October of 2025, we saw two major, major, major outages, um, by AWS and then by Azure. Sure. Right?

I saw. So now we know that everything runs on the cloud. You can say AI is the most important thing.

I don't disagree, but I say AI runs on the cloud, is why you see all those large AI companies sign deals with cloud. Mm-hmm. So cloud is the most important mission critical thing we have in our lives.

For, for us computer nerd. You know, we couldn't get DoorDash to deliver a lunch, so we know something bad happened, right? Uhhuh, um, so, so important.

But it's so complex. It's so expensive. It's, it changes so fast.

How do we control and automate it? We don't, we don't have one place, one system of record to see our cloud. So this is cloud, uh, agen cloud automation platform.

We connect to everything in your cloud seamlessly. Within minutes, we, we see everything from Oracle to A-W-S-G-C-P, Kubernetes, your Datadog, your MongoDB, everything that it's calculated. We see it in one place, then we sweep in and we tell you over the system of work code, what's properly controlled and qualified.

What's, what's governed, what's managed, what's looking like you want it to look like versus unmanaged or misconfigured. Once we establish this, we bring you a bunch of automation. It can be full agents, it can be ai, AI driven automations.

It can be quote unquote old legacy, uh, algorithm based automations to solve your problems from deploying to the cloud, to govern it, automating it, and up to recover from an incident like we saw three weeks ago. Excellent. Good.

That was good. ai Ai, you, You, you can't, you can't do it, uh, any other way today. com.

Right. com, though we do have AI and IT ones too. There you go.

You see? Yeah. Well, we like to spread it around, but, um, let's talk.

So now let's fast forward, right? We understand the problem that you're solving a little of the history and passion as the company exists today, right? And we live, I mean, yes, cloud's very important.

Agen AI is probably be very important. It's very important. Now will be more important next year and the year after.

Where is the company? Like when you're talking to people here on the floor today, what are the conversations you're having? What are we talking about?

First, we're talking about what are the best parties post the conference, right? That's, It's the most important. That's most important thing.

Yes, Of course. The second thing, and if you want to take the AI out, one thing that, that, that, uh, we've noticed and, and we actually learned about it from our customers, not that we found it, found it, uh, ourselves, is that today, application software is being written by ai. Mm-hmm.

Everyone uses cursor or, or copilot, or cloud code, or, or it doesn't matter what. Right? But when we talk about cloud infrastructure, it's still being run manually with click ops or line by line Terraform Open Tofu.

Um, at best it doesn't make sense. How come all my backend engineers and front engineers are writing to care, say, Hey, help me do this and that. I'm now writing application and, and the agents do it, but the infrastructure that powers it all is still old way.

It doesn't make sense. So people are looking for ways to integrate AI into infrastructure management. We see it through our MCP server.

So, MCP servers allows you to use your cursor, your code code. You get copilot in tandem with Firefly. So you just talk to your AI system, Hey, I wanna now deploy this application into AWS US is one, what do I need to change?

Goes to Firefly it, see? And it solve it. So this is one thing, moving from, um, manual scripted cloud ops into AI and Gentech Cloud CloudOps, just like software engineering.

MO moved it. This is the first thing. The second thing, um, as as I see it, is how do you incorporate agents into your day-to-day workflows?

Because everyone wants it. Right? After every single board meeting, you'll see the, the CEO coming to the CTO, which come to the vp, which come to the developer.

We need more ai. Now, if you don't adopt ai, you're fired. If you adopt AI and you make mistakes, you're fired.

Yeah. I don't like those. So you Do where you're fired Probabilities are bad, right?

Um, so, but what happened? So if you remember, two months ago, a vibe coding solution created an application, deployed it to the cloud, changed it, and then it, it deleted a production Database and lied about it. Yeah.

Yeah. I, I, I, I'm here to talk about the good stuff. Okay.

Okay. Go ahead. I, I leave you to talk about the, the later.

Yeah. So it deleted it. Now everyone say, Hey, we can't use the agents in vibe coding in production.

It's nice for a, you know, your hobby. I want my, my grandma's recipe application, right? Cool.

But I want let it Not mission Critical, right? My trading of, uh, solution. So what do we need?

Because at some point we will need agents to do it because we don't have enough platform engineers, DevOps engineers out there, SREs to do it. We will have it. What are we lacking?

We lacking context. I need the agents or the AI driven automation to know that this database is being used by other teams made, deployed on other lines of business, other continents. How do I know it?

If I have this system of record for cloud, this is the first thing I know. And the second one is guardrails. So as we see it, when we talk to the market, we see AI is being treated like a very good protege or, or an intern.

You wanted to do stuff, but only to some extent. So you say, Hey, AI deploy to, to dev account, maybe to staging, but you don't deploy to production. So you need a guardrail that says, Hey, agents can deploy to staging, but when it comes to production, you must have a human in the loop.

So it opens a pull request for you to do a code review for it. And so, all of these types of, of limitations. So to, to wrap it up, we need to adopt AI in our infrastructure management and operations.

And not just for application, because usually infrastructure move faster than application development. And the second one is have the context and the guardrails to really adopt agents into platform engineering. I love it.

Um, so, you know, the, the, so that's what should happen, right? The world's full of what should happen. Not, you know, but that's not necessarily what happens.

How do, how now and Firefly's mission is to make sure that happens or, or to help make that happen? Enable, Yeah, Enable it. Do you see, and one of the things I see, look, I, I've been in technology a long time, right?

I got the dot coms. But, uh, there's a gap between what should happen and when it happens. How big a, how big a gap is that, how this vision you have of, of how this should work?

When are we going to actually see that? So, you know, uh, many people ask me why we have, uh, AgTech and Agen cloud automation. So the first thing is that they don't let us in into coupon floor, or, sorry, floor without agen in your name.

Right? You don't got ai. You don't have Yeah, Exactly.

But because everyone is talking about it, everyone wants it. But we're still in the gearing up stage, right? We, what I talked about the context in the MCP, this is reality.

We have many customers not talking about the handful. We have many customers who are today consuming Firefly with an MCP first mindset, really. So no ui, no A-P-I-M-C-P first.

And I'm not talking only about the cool kids from Silicon Valley running the AI shops. I'm talking about some very large enterprises, really legacy enterprises, fortune five hundreds. And I love seeing it because it, it means that it's now, you know, it's mainstream.

Yeah. We, we are at the height of it. And it's, it's, it's great for the actual agents to deploy.

It will take some more time because not enough context, not enough guardrails. And we can still Not enough trust. Exactly.

Exactly. I, I, I think that's it too. But that's interesting that you have MCP first.

'cause you think about it, what MCP came out about a year ago now only Makes sense. Yeah. December, I think last year.

And now we think about it. We talk about it like it's been here forever, but it's not, it's even a year up. But here you are selling MCP first.

I think that, you know, you don't need to tell you how successful those ai, big AI companies, but even the, the, the, the companies like Cursor and Windsurf. Yeah. They're so popular.

And every developer software engineer today ask themselves, how can I get better? How can I get stronger? And if you don't do it, it's not that you're getting better.

It's like, um, inflation, your power goes down, right? Yeah. So everyone, even just to keep up, you, It's just table stakes at this Point.

And, and, and now the platform teams, the SRE teams, they say, Hey, we are now behind because they have it writing code. But what about our infrastructure? What's the difference code as a new code deployed for application is more, most often new.

And it's, it's similar. Every company's, every organization's infrastructure is a snowflake. And it comes, if you're not a five person startup, you come with a very big baggage that you need to carry with you and understand and tap into.

So this is why it was harder for them to do it. So, so if you allow them to run as fast as the application teams, they just embrace it and they love it. And they come back to CIO, Hey, you told us to adopt ai.

We're adopting it. See, how cool is it? Hey, I'm talking to the MCP server.

Show me all drifts in my account. Firefly fixes it. Hey, here's a misconfiguration.

Let's use AI to, to solve it. So f finds a problem. The MCP creates the solution, then we bake it into infrastructure code, we deploy it.

People just love it. It's, you know, the future is here. We, we, living in a place where everything can, can, can be quick and effective.

It is, It is an interesting time to be alive. Yeah. I will say that.

Boomer or not Ido. Thank you so much, sir. Thank you.

Don't even firefly here on, uh, tech drunk tv. On the tech drunk TV floor as far as the eye could see. We're gonna take a break.

We're back with more. Stay tuned. Hey everyone, welcome back here to Text Junk tv.

Let me introduce you to John sro. Sro. Yeah.

I'm sro. You got it. Perfect.

All right. Hey, John. Um, John is the CISO and lead product manager of, for America's for Mendix, which is actually a Siemens company, a Siemens business.

But, um, John, first of all, welcome to Tech Trunk tv. It's great to have you on here, Man, it's a real pleasure to be here, Alan, thanks for inviting me and, uh, allowing us to talk a little bit about, uh, what's going on in the wide world of DevOps. So I'm, I'm pretty excited.

Always Good stuff going on. But before we get into that, John, let's talk a little bit about you. As I, as I mentioned, you know, when we were agreed rooming there for a while, it's not often, especially in a non-security company, or every company's a security company in my world, but in a company that's not known necessarily as a cyber company.

Mm-hmm. Where we see a CISO is also the lead product manager. Yeah.

I want hear about that. But let's, let's take it from the beginning. You were born and then what happened Now?

I Mean, I spent 20 journey, I'll just skip. Yeah. Uh, so prior to joining Mendix, I, I had a, a little over 15 years in software development architecture, uh, including working at some of the largest names in energy and finance.

So, uh, in fact, I, I like to joke around that. Um, you know, uh, it's often that, uh, I would end up with managers who'd say, well, I, I used to code, but I don't do that anymore. And I, I joke, 'cause I'll say, oh, actually I still code in, in my free time for fun.

So, uh, it kinda keeps me fresh. Um, then, uh, through a number of different, uh, circumstances, I ended up landing at Mendix, which is a low-code software development platform. Um, partially because I worked for a company that was a customer of theirs.

And I really liked, uh, the, the modeling approach that Mendix was taking. Um, and so that led to, uh, multiple different, uh, roles here at Mendix. And most recently I've been, uh, leading up the, the field product management for Americas.

And that then led into the field CSO role, which is interesting because what, uh, my, my European colleagues have found is that the security posture and the governance posture within the Americans is very different than what they're used to. And understanding that, and tailoring a low-code soup to nuts software development lifecycle that fits into that sort of box is really difficult, especially if you're not used to thinking that way. It doesn't mean it's impossible.

It doesn't mean we can't be cloud first or cloud data while doing it, but you have to like check the boxes. And as we got more and more into the highly regulated spaces, including federal, uh, they needed somebody who was a US citizen on US soil to show up and understand not just the technical side, but the security side as well. And it just sort of naturally flew with my background.

So that's how I ended up with the CISO and the, uh, the field product manager title. So, a little, uh, complicated. I love it.

But hopefully not too crazy. No, not too crazy at all. What a great, you know, I, 'cause I will tell you, you know, I have about 30 years in tech, other than the last 10, 12 years, more, more, I was a startup guy.

Mm. And about 25 years in security. And, um, though I think today we see the EU probably has a little bit more process, a little bit more rigor and, and security compliance.

Once you start getting into the highly regulated industries, you know, finance, healthcare, and then once you get into the fed space, which it might last security company, about 65% of our business was, was actually DOD. Mm-hmm. And, and DOD and agencies, you know, the three letter agencies.

Yeah. Um, then you then, you know, now that cyber expertise and, and the amount of, uh, of, uh, cyber, uh, certifications and accreditations really, you know, gets ratcheted up a lot. I spent an awful lot of time at, uh, Fort Huachuca, for instance, in the Army information assurance technology labs there, getting our products, uh, certified and so forth.

So it, it makes a lot of sense to me. Um, you mentioned no-code mm-hmm. Mentioned mendix, and I think mendix is to a certain extent with some of our audience synonymous with lo no low code, no code.

Um, How does that, is that a good, does that make you feel comfortable or not quite Alan? So, so I, I, uh, I would put it solidly in the low code bucket. Um, and I think one of the things that attracted me to Mendix when I was on the customer side of the equation is that every conversation I had with Mendix was about respect.

It was about respect for the person doing the work and the respect for the person trying to get value out of the work, right? Uh, so you kind of got respect on both sides of the coin, and they gave you a beautiful modeling environment, but the minute that you need to flip the switch and dive into code or do something more complex, they didn't hold your hands or try to lock you in. They instead said, go for it, and here's how you hook into us.

And that, that openness is very attractive. And I actually think as we get into more of the AI conversation, that that sort of openness, that approach is very relevant as we think about things like AI transparency and trust and, and responsibility. And it also helps you understand, uh, in your DevOps and software development lifecycle journey, what's happening at each piece of the puzzle.

So that way, not just the doer, but the stakeholder knows what's happening. And I, I think that's, that's really important. So I feel like we, we solidly fit into the low code space, um, rather than the no code space.

Uh, and I don't think that, uh, our goal is to enable everyone, we have the, the slogan go make it. Our goal is for the makers and to enable the makers to do, to do good work, if that makes sense. Absolutely.

Absolutely. John, John, I wanted to segue into DevOps, Right? com.

com in 2013 14, um, a lot of people may say, wait a second, how does low-code, you know, fit into the DevOps kind of puzzle? Um, but it does, right? Because it, it, it's about automation, it's about kind of breaking down the silos and all of, you know, just because we're doing this in a low-code environment doesn't mean that we don't have the same issues around deployment, deployment, security mm-hmm.

Operating that code, you know, everything else that we, that goes into a modern, you know, software factory, if you will. But, you know, from where you sit, John, how do you see that connection? Well, you know, it's really interesting that you bring that up because it's, I like to say there's no such thing as magic.

The rules and laws of physics still apply, okay? Mm-hmm. At the end, end of the day, you know, it's still, it may be terraform, it may be helm charts, whatever it is, there's still a thing doing the work on the soup and nuts and, and bolts underneath the covers is just how you present that to the end user or to the stakeholder that matters, right?

So it, it, at the end of the day, whether you are looking at a series of DevOps experts or you're literally just looking at an old Windows administrator, at the end of the day, they're still trying to get a piece of capability into the hands of somebody to get some sort of value out of it, or in order to manage that, to understand what's going on. And so our approach at mendix is to just make that transparent, but still automated and put some nice packaging and wrapping around that. So for example, if you want a fully managed SaaS offering, we've got that.

If you need to own and control your own environment and understand every step of the way, we've got that too. If you understand how to build pipelines and Jenkins or Azure DevOps, go for it. If you need a pipelines product that does it for you, we have that too.

So it's sort of, it's at the end of the day, you're still packaging and shipping some piece of, uh, executing software. It's just at what level do you need to know and understand that to be successful? And what, at what level can your organization know and understand that, you know, Uh, agree.

I agree. Agree, agree. So, John, you know, DevOps, well, for me anyway, DevOps is a subject area, a market that has been evolving, changing since every, you know, since I first encountered it 14 years ago, um, 15 years ago.

But obviously we live in interesting times. We've got AI and the rise of shadow ai mm-hmm. Vibe coding, right?

We're, we're turning out more code than we ever did before. Uh, you know, there it's like, and as if we didn't have enough toys to play with, AI has rendered, you know, infinite amount of new toys and apps and methodologies. Hmm.

Um, platform engineering, another big thing that has risen, let's say in the last four years, maybe five years, as you look at the DevOps space now, and, and, you know, let's qualify that as it applies to the mendix business, as it applies to low code. How are all these, I don't wanna call 'em AI's a mega trend. Yeah.

But how are all these different trends kind of shaping the market and what you see going forward? Well, I think, I think one of the trends that I'm seeing, and I think it's, it's a change. Um, I, I'm unfortunately, uh, I hate to say it, but I'm old enough to remember when DevOps was, was controversial when you would say DevOps and people would just freak out and not really understand what you're talking about.

Um, and, you know, uh, I think there's also a lot of, uh, denial and sort of avoidance that, that entered the equation. I got the scars to prove it, my friend. Yeah, yeah, yeah.

com, right? Yeah. I, I caught the brunt to that.

0, which is a, a new standard that's come out from the federal government around, uh, contractors doing business and, and the technical, the technical rigor that they have to go through to prove that they're secure enough to do business with the government these days. And the message, the message there, and actually, if you look at the controls, DevOps solves so many of them if you do it right and well, and the message at the bottom line is it's not optional. It is now non-optional.

And moreover, it's non-optional because it's safer, it's more secure, and it, it enables and empowers your people to do the work that they need to do rather than restricting them. It's not a control mechanism, it's an enablement mechanism that you can build safety, security, and governance around The tricky part here, and I think you look at all these trends like vibe, coding, um, you know, uh, a AI in the loop, human in the loop for AI and, and all of the other things that are going on, how that fits into the software development lifecycle and how you, you, and I think governance is an abused term, but how you govern control and, and build in safety without growing the pendulum too far in one way or the other is, is really key. I think some of that we're starting to get figured out, and some of that ha has not been figured out at all yet.

Um, in fact, I think 2026 is, uh, uh, it was actually a prediction. We just, uh, released, I think 2026 is the year that we have the major ai, uh, security breach. I think that, that we haven't had the major AI explosion yet.

This is the ma this is the year something goes wrong, right? Like the, the target breach or, or something similar, the Equifax breach, right? This is the year we have that for ai, and it's not because that's impossible to control for now, it's that we are not being as responsible and transparent and trustworthy as we run this race as we should be.

Uh, which, you know, kind of as is, you know, uh, expected but with great powers, you know, the same goes, comes great responsibility and mm-hmm. Those capabilities are key. So what we're doing at Mendix a little bit, and I, I hate to self plug, but we are really honing in on not just enabling and empowering people to go build and make, but also to understand, you know, there's always a human in the loop.

There's always a, a guard and a check. But then that responsibility and trust needs to be visible to a layer of people who don't understand how the technology is executing what it's doing. And that's true.

Whether, whether you're building the software or whether you're deploying it. And I think a, a very poorly, I don't wanna say it's poorly understood, it's not widely understood, but this concept of software supply chain is just so crucial and critical. As you can see with some of the recent, recent issues with MPM packages, um, and the recent breaches there.

It's something that we have to get our, our hands around in a way that is enabling and empowering, uh, rather than, you know, uh, I think you and I are both old enough to remember the, uh, the fallout of Sarbanes Oxley and, you know, we just quadrupled the time it takes to get anything done. We don't want to end up in that scenario again, if that makes sense. Right?

Absolutely. And I, I think, you know, that's a high wire balancing act, right? Providing compliance that's worth its salt versus the level of, of, of bureaucracy, if you will.

And, and it's something we, you know, as, as we move from EU to, to North America, you know, there, there, I think there is a different, uh, focus. I I, it's not focused, you know, what it is in America. I don't know if we have the political will to get things done as much as they seem to have over in the EU with some of these newer compliant, even just AI itself compliance.

Mm-hmm. Right. The EU is kind of way out in front of that.

We're still talking about how much, you know, trillions of dollars we could gather up to spend on this thing. Um, so I, I do, it's gonna be an interest. I think, look, these things have a way of kind of smoothing out over time.

Mm-hmm. And it's gonna be an interesting couple of years here, as, as we see sorta, you know, the, the quest to do more fast versus the, the, the imperative to do it safely and securely. Uh, they gotta cut, you know, at some point they've gotta come together.

They can't exist in two separate kind of, uh, planes. But, yeah. Anyway, Sorry, go Ahead.

No, no. I was just gonna say, I, it's, it's gonna be, it, it's interesting time. I mean, there's so much going on and it's, it's, it's interesting times, John.

I I wanted to let people know, look, if you're interested in what we're talking about here, if you're interested in what Mendix is about and what they're doing, John, what's the best way to engage with Mendix? Oh, that's easy. com.

Uh, we have lots of, whether you're in the defense space, the industrial space, finance, banking, whatever, we have lots of, uh, user information, user stories, user journeys, testimonials, uh, you can get demos. We also have a lot of really good, well thought out, um, white papers and guides around, uh, governance around, uh, executing and, and building COEs around how do you do this in a highly regulated, uh, fashion Right? And manner.

We, one of our most popular guides is the, the guide for our defense industrial base, uh, folks. com. Um, you can also reach out to me.

com. I'm happy to connect you with the, with the right folks here at Mendix. Um, and you know, Alan, there's, you know, there's some interesting trends here around, um, what vibe coding is, what AI can really do, but also the cost in human capital, the cost and human knowledge, and understanding that, you know, we're just starting to be able to tackle.

And that, I think it's, it's, there're interesting discussions to have. com and, uh, tech tv. I, I really like some of the thoughts that are going on there, especially around responsible ai.

And I, I really look forward to seeing more of that content up. So, uh, it's been a real pleasure to be here and talk with you a little bit about it. Absolutely.

Well, John, don't worry too much. Elon says, the robots are gonna take all our place. We're gonna eliminate poverty, and we're all going to get a basic income, a livable, basic income.

So let the good times roll. Um, anyway, John, it's a pleasure having you all keep up the great work. Come back and visit us.

Keep us posted on what's going on at Mendix. Okay. Uh, I'd love to, Alan.

All right. Cheers, sir. Thank you.

All righty. John Koro, CSO, lead product manager, Americas, uh, from Mendix here on Techstrong tv. We're gonna take a break.

We'll be back. Hey everyone, welcome back. We're live here at CubeCon.

You know, I guess the, the, uh, show floor is open 'cause I see a lot of people walking around, but it is a huge show floor and we're down. Well, I, you are looking this way, but behind us that way is the meals and seating area to our right is all of the, uh, different projects from the CNCF. I think there's over 200 projects now, right?

Yeah. Crazy. Um, and it's getting a little crowded here, which is good because it brings the heat of the place up.

It's so cold down here. Um, let me introduce you to my next guest. His name is Yo Tom.

Ya Nailed it. Did I get that right? A Plus.

Beautiful. Yo Tom is the CEO of Causley company. You've probably heard about or you've heard of, but I don't know if you know everything they do.

Yo, Tom, welcome, welcome to Tech Drunk tv. Let's start with you. Yeah.

You're the CEO of Causley. How did, how did you get here? So, Causley's, my fifth startup, the first one that I'm fortunate enough to be the CEO of, uh, but actually this whole team that I joined here at Causley was a team that was part of my first startup, a company that was called VM Turbo.

Okay. May remember VM Turbo. Mm-hmm.

Eventually, we rebranded the company into Turbonomic Uhhuh acquired by IBM for $2 billion in 2021. Beautiful. And in 2022, Causley was founded.

I actually joined the team in 2024. At that time, I had actually gone to another startup that was acquired by Cisco. And so I was working inside of Cisco running the incubation team for the security business unit.

Okay. And Schmuel, who's the founder of Causley, who I believe you've had on your show, uh, pulled me out of, uh, Cisco and said, Hey, why don't you come join us here in Causley? And, uh, for me it's, uh, it's like a full 360 like, you know, career moment to come back with the team where I started all To your team.

That's a great thing. I, you know, look, this is Techstrong is probably the fifth or sixth at least Startup. I've done two, no, this one not venture backed.

This is the first one I did that, you know, new Venture and I, I, there's something to be said for working with people you're comfortable with. A hundred percent, You know, you need fresh blood, but when you work with people that you've worked with, in my case 20 years Yeah. Or more Yes.

Especially in my exec team. Yes. It's, uh, it's comfortable.

Yeah. Well, especially if you're trust, if you're a remote first company like we are, I think that added measure of trust and knowing how helps to communicate with each other. Yeah.

You know, when you, you gotta kind of get right to the point, uh, really helped. Huge, huge. And, um, so did you come in as CEO in 2024?

Is that It? I did, yes, I did. Very cool.

Um, so I mentioned a lot of people out here may have heard of Causley. I don't know how many of them really know Causley though. So if you wouldn't mind, yo, Tom, let's start there.

What, what's Causley? Well, you in your own words. Yeah.

So we're on a mission to help people automate service reliability. Uh, we can unpack that further. But, uh, if you had Schmuel in this seat, he would tell you that this is really a continuation of his life's work.

So the first startup that Schul co-founded was a company called Smarts in the nineties. Yep. And, uh, smarts was a leading provider for root cause analysis for networks.

Uh, Turbonomic just jumping ahead to the second company, that's where I joined the team and started working with Schmuel very early on in the day, uh, was automating resource management for cloud and virtualized infrastructure. If you think about this, this is like the continuation of that journey, which makes a lot of sense because cloud native applications are an interconnected web of microservices that are very functionally similar to complex networks. Sure.

Uh, and what everyone is trying to do is trying to apply AI in a way to automate the reliability of those services. And, uh, we believe we have a unique insight and, uh, a lot of lived experience to solve hard problems in the space. So let me ask you an honest question.

So I I, I remember talking with Ri, he lives in New York. No. Yes.

Um, brilliant guy, right? Yes. Yeah.

He's been working on this issue long before AI was a thing. Yes. As he likes to say, the first hype cycle of AI in 80, right?

Yeah. The first time. Yeah.

It's pretty funny. Um, but you know, today it's disrupting everything, right? It's, it's, it sucks the oxygen from everywhere.

Um, how has, I mean, and in some ways, I guess it's been good for Causley 'cause you are already on that road for this. Yes. But in some ways it had to have disrupted Causley go to market, right?

Because now you've gotta accelerate AI adoption, accelerate integration, accelerate, you know, riding this wave. Well, I think in, in some ways it's been a good tailwind. I mean, I, I think you've heard this term AI Probably the best tailwind you've ever had.

Yeah. You've heard this term ai, SRE flying around. Yep.

Uh, and so there's a lot we can unpack there. Uh, but I think if you go back to like our time at Omic, we used to tell people we were gonna help them automate where to place workloads across infrastructure. And the idea of automation was scary to people.

I think now, I think now we're sort of seeing this turn where everyone's saying, okay, I have to find a way to drive productivity gains. I have to use AI to automate things. And, uh, and the good news is we're sort of at this like nice intersection where people are trying to automate operations and they're learning what they can do with these agentic systems.

And also they're learning what the limitations are of those systems. And that's where we really have a unique value proposition offered to the market in terms of what our, uh, product does. Excellent.

Love it. Um, ai, SRE let's unpack that one. Yes, that's a good one.

So like, thank you so much. You know, like, like every other job function, I think in tech, AI is having its way, if you will. I don't know if it's patently obvious to our audience how this is applying in in the world of SRE.

Yes. Let, let's talk about that a little bit. Sure.

So we'll talk about what's out there and we can also then sort of unpack some of the limitations. So some of the good in the space is it makes a lot of sense that if I need to write a postmortem and learn from why an incident happened, using a language model to write up a summary makes a lot of sense. Yeah.

Uh, it also would make sense that if I'm trying to parse through a lot of unstructured log data to look for something meaningful, again, using a language model, which in a lot of ways is like a pattern matcher or a pattern finder could make a lot of sense. And so I think what you're seeing on the market today that's gaining some traction are these effectively like new age chatbots that SREs are using to help them troubleshoot. Uh, Gartner recently came out with a cool vendor report around the A-I-S-R-E space.

Really, they, they missed us on that one, but they're gonna get us on another one. But one of the things that they, uh, mentioned in that, uh, in that article was that every A-I-S-R-E company they covered is missing the point and missing the fundamental opportunity, which is that the bigger opportunity is to actually help the service owning product development teams to automate reliability as part of how they build their products. And the idea to only apply AI to the SRE function as an SRE function is actually missing this opportunity of bringing reliability engineering as a sort of first class citizen to a product development team.

So I have my own opinions with Gartner, but I'm not gonna voice them here. Uh, over the years at other startups I've done, of course, I've paid the money that I think we all pay in ransom to garner there Is a tax. Yes.

Um, We have not paid the tax. We were not in the report. Right.

The original ransomware. But, um, let's talk about this though. If we, if we use AI to build that automation and reliability at the product level, doesn't that make sort of SREs a little obsolete?

So it's an interesting point. I would tell you the best SREs I've ever worked with always describe their job as automating themselves out of a job. Sort of like that commercial, I want to be obsolete by the time I'm 35 or whatever.

Yeah. Yeah. And you know, you've probably heard the saying like, AI's not taking your job.

The person, someone who knows ai Yeah. Someone who knows how to use AI to do your job is taking your job. Yep.

Um, but I think there's, look, AI at this point for a lot of people just means using language models, which is different than what causally is doing. And I think that starts to lead to the limitations of some of these AI SRE that are out there. Uh, and people are starting to learn that, you know, it's like cha pt, sometimes it's right, sometimes it's not.

And you know, if you're trying to automate reliability, you can't use something That's Right. Coin flip, whether it's gonna be right or not. Yeah.

I used, so I was in the hosting business for a long time and back then, and even still today, you know, five nines give you an SLA five nights. Yep. But what a lot of people didn't realize is even with five nines, I think it's four minutes or so, I think I have many minutes a month.

Yeah. You're down and you're still in your five nine SLA And for people who are mission critical, those couple of minutes a month means a lot of money. Yeah.

Well, look, I'll go even the next step, which is that, and again, maybe this is, uh, a colloquial thing that people already have grown tired of talking about, but for some people, slow is the new down, right? Yeah. So there's also the point that if you can apply the ethos of reliability engineering, and you take SRE in terms of the idea of error budgets and SLOs for every service and a blameless postmortem and all these other sort of ethos elements of the SRE culture, and you apply that to how you build software, you end up not just troubleshooting things faster, but preventing issues in the first place.

And that's really the big opportunity that we see, particularly for how our model and our system works. Love it. Now, so do you think you are, who, who's your target customer here then?

The product manager or the SRE? So, uh, well, I'll give you a different persona, which is the software engineering manager. So the people that are building the actual customer facing services and applications, uh, we have real money gaming clients, uh, that they use us for their sportsbook app and their casino app.

Sure. Uh, we have SaaS companies where, you know, it's the development teams that are building the customer facing product. Uh, and look, at the end of the day, we also do have SREs and platform engineers that use the product.

I was just gonna say pla the software engineering teams probably a lot of platform engineering in here now though, too. Yeah. Because a lot of the shape of these problems with cloud native applications is, um, because everything's an interconnected web and you're trying to figure out is the problem in the app or it in the infrastructure or one of my, yeah, yeah.

One of my, uh, friends likes to say, is it an ish you or an ish me. Right. Yeah.

It's, and so, and so, you know, it's, it's a problem that really spans personas and user types. Uh, but we really do find that when we are the most successful in a customer, it's because an engineering manager or director of software engineering really adopts the idea that, Hey, I wanna automate reliability work. Help me do that.

So it's, it's, that's not really, that's not really top down though. It's not. 'cause it's not the same middle, It's like middle out.

Yeah. It's middle Out. Yeah.

Up and down. Yes, exactly. That makes sense.

And then, but ma'am, we jumped into things and I didn't get a chance to talk more about Causley the website. Yes. ai.

com. Sharks love sugar. Love it.

Yes. So now you've said it. How, where's that come from?

Well, so you know, the, the sort of next best alternative to what causally does, which is we're building this causal model of, uh, the cause and effect between things in your environment. The next best alternative is the correlation engine. And so we like to sort of have this play on correlation is not causation.

You know, just because there are more shark attacks in the summer doesn't mean that sharks love sugar. Sure. Good.

Right. Because, you know, ice cream consumption also goes up in the summer. Got it.

The idea. So that's, it's sweeter. Yes.

ai. It is, um, I know you guys recently announced an MCP server. Oh, without making it a Me too.

Tell me what's the deal there? Yeah, So look for development teams, which is who we're really trying to appeal to, you gotta be where they work. And where they work is in their IDE.

Yeah. And so everything that Causley does, we expose not just through our ui, but also via an API also via webhook. And now, as of recently, an MCP server as well.

So if you're a developer and you're in your IDE and you, for example, want to, uh, improve the Java heap size, uh, in a, in A-J-V-M-H, how do you know when it, when you're actually gonna get your bang for your, your buck to do those types of things? Uh, or let's say you want to change, um, the, you've got a slow database query that you need to optimize. You could run a profiler to always do those things, but how do you know you're really getting the juice for the squeeze?

And so our causal model is helping you understand the actual cause and effect in terms of sort of at a meta level, which optimization actually gets me something from a performance perspective. You know, sort of the bang for the buck of the work I'm gonna go do. So we're exposing everything from our causal model now via an MCP server.

I love it. I love it. Um, yeah.

You know, isn't it amazing though? I mean, it's, it's, I think to be fair, I think MCP came out last December, not January or February, but within a year. I mean, we talk about it like it's, you know, table stakes I emptied up, right?

Yeah. Let's play poker. Um, at some level, I, it still kind of freaks me out how quickly this whole thing is really just coming together.

Um, well, I Think, can I add a little thought on that? Sure. Is that I think we're seeing like markets go in waves from bundling to unbundling.

And I think that particularly in the part of the market that we're in, there's this sort of unbundling to build your sort of best in class Right. Observability stack. That, That's always how that pendulum seems.

Yeah. Right. Especially when it's newer stuff because it's, we're still figuring it out.

And then once kind of best practices really emerge, then you go back to the buny at some point. Makes the world go round, my friend. Yes.

Right? Yes. Um, what about coup Con?

Yes, but so far so good. It is buzzing, like you said, it's starting to buzz and, uh, it, I'm excited about what's Here. It's a very, um, it's a very passionate crowd.

I, I will tell you, you know, I've been covering Coup Con now for, I don't think the first year, but eight or nine years. And, um, it used to be all developers and all people like in t-shirts and backpacks over the, and I do both here in the European one. Over the years I've seen more people wearing sports jackets, uh, more ops people, of course SRE people, platform engineers.

So it's diversified into, uh, quite a community. But it's a pa a community that's passionate, it's passionate about open source, it's passionate, obviously about, you know, doing more faster in the cloud though. The other thing about cloud native is it's not necessarily in the cloud.

Yeah. Yes. Every everywhere.

Yes. Right. It could be on bare metal, it's on the edge.

It's everywhere. Anyway. Hey man.

Pleasure having you on. Hey, great. Thanks for having me Stay.

Hello to Spiel for Yes, thanks. Um, let's see if I remember this right. Yo, Tom, ya yai yai Yai got agree the first time I did.

But just flip It in. We weren't live. They could and Replay.

Oh no. They, they expect this from, you know what I mean? I can never do it.

Very good. I'm like, ai, I can never do the same thing twice. Exactly.

com. Yes. Very good.

Thanks again. I love it. All right.

We're live at Cuon. We'll be back here in a moment. Hey everyone, welcome back here to Text Drunk tv.

My next guest is Jeff Baxter. Jeff is the VP of Product marketing at NetApp. Let's welcome him.

Hey, Jeff, welcome to Text Drunk tv. It's great to have you on here. Hey, thanks for having me.

I appreciate it. Glad to be here. No problem.

So, Jeff, I always like to let our audience get a, a glimpse behind the curtain, if you will, of, of who's talking to 'em. So if you wouldn't mind, I mean, beyond your name and your title and your work at NetApp, give us a little bit of your story. Yeah.

So, um, you know, if we start back in the Paleolithic era, no, I'm just kidding. Um, One thing, NA but go ahead. Yeah, Exactly.

Exactly. Uh, so I've been with NetApp for now 18 years. So a fair amount of time in, in Silicon Valley.

Before that, I was a Solaris admin and a san admin, um, maintained, you know, large scale data storage systems. And joined NetApp as a systems engineer, ended up, uh, as the CTO for the Americas. Um, so spent a lot of time, um, being sort of the senior technical advisor to, uh, a ton of senior enterprise companies that are using NetApp.

Uh, made a change after that. It, it turns out that's a fun job, but when you have, uh, young kids, the 99% travel is a little bit much. So I made a switch into product.

They had done that. Yeah. So I made a switch into product management and, uh, ran, uh, large parts of product management for our enterprise storage systems for several years.

And then, uh, about two years ago, they asked me if I'd, uh, take on a leadership role running, uh, sort of global product marketing for Napp. So that's what I've been doing for the last few years. But, so it's been a fun journey here.

Um, lot to change in the industry. I love it. Yeah.

Yes. It has, you know, hearing you mention some of those names makes me smile. Yeah.

Solaris and, and stuff like that, you know? Yeah. My first tech company, I started in 96.

Yeah. 96. We were a sun shop running all Solaris and stuff like that.

And there was something to be said for Solaris, my friend. Yeah. There was, There was, There was the other day I was, I was editing a demo for, for our keynote, and I suddenly had to drop into VI and was saying there going like, oh man, this is, this is taking me back.

Right. So yeah. It It was lot of fun.

It goes for the day though. But it worked. It worked great.

Yeah, it worked. Um, and you know, this, your path is unique, but not that unique. I, you know, I, I have a lot of friends who, you know, came from quite frankly, coding and engineering backgrounds, and then 10, 12 years into their careers, 15 years into their careers, switched over to, you know, they called the business side of the house.

Yeah. And, and, and doing that. And, um, I think it makes for a better business person having had that, you know, the dirt under your fingernails, if you will, of, of working in the trenches on, on code and on systems and, and stuff like that.

So, kudos to you and congratulations. Um, Jeff NetApp is a company that, you know, our audience, I, if I ask 10 people in the audience, nine of them are gonna say, yeah, no, of course. I know NetApp network attached storage hardware.

But, you know, today's NetApp is, is more than that. How would you describe it to our audience? Who, who are tech people, right?

So you don't have to be too elementary. Yeah. But how would you describe NetApp today?

You know, I, I think it's interesting 'cause you, you started that core as network attached storage. And one of the nice things I like about NetApp is we haven't stopped doing anything we've been doing for, for 30 years, right? So we, we take that network attached storage that we basically invented, or at least popularized and, and grew to what it is today, back in, you know, 1992 when we were founded.

And we built on top of that unified storage. So the idea of being able to put, um, block storage around it, and we were really the first to unify block and file, and then object storage and have built out to an entire unified data storage portfolio that, you know, spans basically every workload you can possibly have on prem. And then on top of that, uh, I think the extending it out to hybrid multi-cloud has really been the journey that we were on for the past 10 years, to the point where, um, we're the only ones really embedded natively, not just in one major cloud, but in all three of the largest clouds out there.

So it's, it's an interesting business we're in where we built out this intelligent data infrastructure, as we call it, right? That's the marketing term. But what it fundamentally means is you're able to take the same, uh, operating system, NetApp, ontap, and run it across any workload in any data center, um, and in any of the major clouds.

And so fundamentally, that's the backbone of NetApp's business today, is providing that intelligent data infrastructure that lets people manage data pretty much wherever, right? Um, on-prem in the cloud. I love that we're fundamentally agnostic to wherever the best place is to run your workload.

Um, and we'll support you with these sort of enterprise grade features and data management regardless of where it is. I love it. I think, Jeff, I think that's a great way of describing what NetApp is, who NetApp is today in the market, and, and where you, where you are.

But of course, you know what they say in tech, if you're not moving forward, you're dying, right? And mm-hmm. Today, when we talk about moving forward, you can't move forward without talking about ai, whether it's generative or agentic or whatever comes next.

You know, everybody wants to know kinda what's your AI story? How is AI impacting what you're doing? How are you gonna leverage ai, ai, a i ai it sounds like E-I-E-I-O.

There you go. Um, but, um, you know, let me ask you, how big an impact has AI already had on NetApp's business and as you go planning, you know, going forward? Yeah, so, so obviously AI is incredibly strategic for us specifically.

You know, we partner with, uh, Nvidia, with Intel, with, with so many of the leading ai, you know, startups. And I think what's fundamentally cool about what NetApp does is we're not out there trying to sell another AI model. We're not trying to, to do any of that.

There are hundreds of companies to do that. What we're focused on is the same thing we've been focused on for 30 years, which is around the data. And the fundamental problem for a lot of businesses with AI is, uh, you know, everyone looks at what model am I gonna use?

Uh, you know, how am I gonna get all the GPUs I need? Am I gonna do it on-prem? Am I gonna go to one of the neo clouds?

Am I gonna use a hyperscaler? But what they don't always focus on is, do I actually have the data in place to support whatever AI I build? And no matter what analyst firm you look at or what study you look at, uh, you know, there was one that said 60% of AI projects over the next year are gonna fail because of lack of AI ready data.

So you can, you can solve all these, you know, crucial problems about having data scientists in place, having the right models in place, everything like that. But if your data is scattered and if your data isn't compliant, and if it isn't, isn't prepared to, for training, for inferencing, for retrieval, augmented generation, it doesn't matter. And so that's really what NetApp has been focused on over the last couple years as we built up for this, you know, era of AI is how can we really, uh, you know, add a at your fingertips, present AI ready data for your data engineers and your data scientists to immediately be able to put to use Agreed.

Agreed. Um, now I don't want to be a glass half empty or a glass half fly. I'm gonna try to play this right down the middle, but you know what, Jeff, as we we're two, three years into this AI revolution, evolution, whatever you want to call it, and like every other tool that I've seen come down over the last 30, 35 years of my career, you know, there's always the question of does it scale?

How do we get it to scale? Uh, how do we get people to like let down their guard thinking it's not taking their job away or, or what have you. Right.

Um, what do you think is the biggest obstacle to scaling AI adoption? Uh, you know, not to, not to be repetitive, but I think it's about allowing AI to have access to the right data. Um, and from both directions, right?

If AI doesn't have access to your enterprise's data, it becomes fundamentally just a chat bot, right? And so I think we've all used general purpose chat bots, and they're wonderful. And, and we look at them as, you know, productivity enhancers, right?

They let us do more as opposed to, uh, replacing people. They just make everyone more efficient. I mean, I know I use generative AI every day to, to make me more efficient.

Um, but it, it doesn't do much more than that. And it definitely doesn't move you towards the agent AI era where AI can actually take action unless you can give access to the right, uh, mission critical data from within your enterprise. But on the flip side, if you go too far and you give AI unfettered access to data, that's where the concerns start to come in about security.

Um, what is the AI going to do with it outside of scope? Um, you know, there's been examples of prompt engineering and other places where if you train an AI model on data that you don't want to go outside your company, and then you expose that model in any particular way, say a chat bot, customer service, anything like that, no matter what guardrails you put on a at the end, it, it want, at these LMS fundamentally want to be helpful, everything for them is a construct. Everything for them is about vectors.

So if you give them the right prompt, they'll unveil their secrets. And so for us, it's, it's fundamentally, you know, how do you make AI productive? It's exposing it to the right data in your enterprise so they can truly give you unique insights without training it on anything that you don't want it to be trained on.

And that's fundamentally what we focus on over the last year, is building out this, um, AI data engine concept that can take your enterprise data, uh, put all the right guardrails in place at the start and transform it into data that's easily consumable by ai, um, by any AI application just right outta the gate. And that's how we think we make ai, uh, immediately productive and useful for, you know, all the enterprises out there. So, to paraphrase, Cindy Lauper's song, girls just Wanna Have Fun.

LLMs just want to be helpful. They, they Do. Yeah.

So, but Jeff, I think what you've described is the technical, uh, requirements for scaling AI adoption, but are we dealing with a people problem as well? In what, in what way specifically Do you, you know, change? People always resist.

Change has been my mm-hmm. Uh, experience, especially a change when you're hearing it's going to cost you, you know, it's gonna take your job eventually. And it, and all of the kind of AI boogie me stories we hear.

Yeah. Yeah. Do you think that, and I'm wondering if maybe you see this at NetApp or you see it with customers that you're dealing with, that there's a human kind of stiffening, if you will, or resistance to Yeah.

To really adopting this at that scale? Well, I'll, I'll say that in NetApp, I think we've had a broad adoption of, of AI internally. So I haven't, I haven't seen that, but I, I certainly know what you're talking about.

And I think it's true for any technical evolution, any sort of technical revolution. It was sort of the same thing, uh, with the cloud 10, 15 years ago, where absolutely, there was a lot of discussion, there was a lot of discussion in my industry and, and people that I worked with who said, oh, cloud is gonna destroy data centers. It's gonna take all of our jobs.

We should fight it. Right? And, and a lot of our competitors, quite frankly said that as well.

And what we've said is, look, you can, uh, swim against the tide for, for only so long before you have to realize that if there is business value to be obtained, uh, it's, it's our job. It's your job. It's my job to find how to extract that business value.

And I think it, it's, you know, you can go back to the industrial revolution and say, the industrial revolution caught cost a ton of jobs, right? A ton of agrarian jobs, other things like that. But it created whole new, whole new categories of jobs.

And so that's really this, this movement towards knowledge workers towards using human ingenuity so that our engineers, instead of spending a bunch of time on writing test cases or other things that don't require ingenuity, can have AI generate those so they can spend their time solving the hard problems. And I think that's, you know, you don't study, um, for years and years and years of computer science to go and write rote code over and over again, right? You study it so that you can think about it and truly solve unique problems.

And that's fundamentally what we're seeing is we're not reducing our number of engineers. We're not reducing, uh, the number of people who are, you know, in, in the marketing team or other things like that. We're just saying, how can we do better?

How can we do more? Um, and how can we be, in our case, more informative using AI as a force multiplier? But, you know, to your point, there's, there's always gonna be resistance to change.

Um, you know, my kids are growing up in an AI era where it's, it's very, it, you know, if, if, for me it was worries about using calculators in math class, for them, it's worries about using chat GBT in every class. Um, and so it's, there's gonna be cultural change. There's gonna be gen, you know, generational change by the time, uh, my kids are in the workforce, AI will just be a tool like PowerPoint or like anything else we use to optimize, uh, getting along throughout the day.

And so, you know, heck, we wouldn't be doing this over Zoom, you know, 10 years, 20 years ago, right? And, and today it's a, it's a vital productivity tool. And I think AI will be much the same, Maybe even bigger.

Even bigger. So here, yeah, here, here's, and continuing in that vein, right? If this isn't going to be huge, if this isn't going to be, you know, game changing, should we be putting this kind of effort and emphasis and resources into it?

Um, so, but it, but if it's not gonna be, if, if it is going to be, we've gotta be able to be ready for it. If it's not gonna be, geez, we're wasting a lot of time and effort, what organization-Wide impacts do the, does a modern intelligent data infrastructure strategy have, let's say short term and then maybe longer term? Yeah, I, I think you're, you're right to say that, right?

In terms of how do we make reasonable investments so that we don't miss the wave, but we don't overinvest. And I think what we talk about with customers is really organizational best practices that they should be doing anyways. So when we talk about, uh, data storage or building out a intelligent data infrastructure for ai, it's not throw out everything you have.

So we announced a, a new system, NetApp, A FX, for example, which uses the exact same ONTAP software that, you know, tens of thousands of customers are already using, so that they can start to build out this AI infrastructure without having to reinvent everything that they're doing. And they can start to building governance and compliance and security and cyber resilience directly into that infrastructure so that all their data is AI ready. And to be quite frank, even if they end up with only a 10th of the AI experiments, they're thinking about, um, the fact that their data is still structured and ready and compliant is a boon in and of itself.

In fact, you can look at AI as sort of an impetus to do what a lot of businesses may not have done anyways. It's kind of like spring cleaning, right? It's not much fun to clean out your garage and, and reorganize everything, but this gives you a reason to do it.

That ties into one of the major imperatives of our time. But regardless of how you end up using ai, the fact that all of your data is ready to be utilized, is unified and is compliant, is, uh, a, a gift in and of itself. Agreed.

Agreed. I, I, I, uh, don't disagree with you there, Jeff. I, I, I know we're running on time.

These things go quick, but, um, we're just, I guess, what has it been about a month since in sight now? Three weeks? Yeah.

Well, by the time people see this, it might be closer to a month. Um, lot of announcements, a lot of news coming out. We covered some of it at rum mm-hmm.

As part, you know, tech Strong as being part of rum. We, Daniel Newman of course, was there, and we had some of our other analysts there, but our audience probably hasn't seen a lot of that coverage for people who weren't there in regard to this. Can you share more about kind of some of the info or announcements that came out of Insight 2025 that has, you know, buried on this subject?

Yeah. So I think there are a couple different announcements. And I kind of talked about a few of them.

Uh, you know, for ai, we announced this NetApp A FX, which is a, uh, enterprise grade disaggregated architecture. It fundamentally takes everything that we've done for the last several decades, uh, in building this, this truly enterprise grade, both from features and resiliency, uh, operating system, NetApp ontap, and extends it to being this massive excess scale disaggregated architecture so that customers can, uh, you know, feed the GPU Beast, right? As, as GPUs keep getting faster and they demand more and more throughput, um, A FX can scale and, and immediately was, uh, super pod certified by Nvidia.

So it can, it can work across, uh, the largest AI clouds as well as starting pretty small inside enterprises and, and growing to that scale. So that was a key part of it. And then the next part on top of that was the AI data engine, the NetApp AI data engine that I mentioned, which goes all the way from finding all your data across your data state, both on prem, um, and in the cloud, uh, builds a metadata catalog across all of that so that your data can easily be searchable by your data scientists, by your data engineers.

They can create a curated data set that goes through compliance guardrails. So you can say, I want you to strip out any credit card numbers or any personally identifiable information or any HIPAA information, and then transforms it into a vector database that lives directly within your storage layer. So we can skip multiple different tools, multiple different steps, and have an embedded vector database that any AI application can use outta the gate.

And so we think that combination of a FX plus A IDE was probably the biggest announcement coming out of Insight 2025 to really enable really, um, that AI ready data. Um, I love it. And then the other ones, so around cyber resilience, um, the other thing we announced was this new NetApp ran ransomware resilience service.

And so we actually have been the leaders, I think in embedding all these security services directly into the data storage layer. We talk about ourselves as the most secure storage on the planet. And we back that up as being, you know, the only one certified by the US government to store top secret data.

Um, and the only commercial, um, storage available to do that. And we continued to evolve the zero trust principles. You know, back in the day you thought, okay, well I have my perimeter firewall, I'm all set now.

We operate on the assumption that any given data center, any given network, is constantly breached because it's generally a safe assumption. And so we have to harden even down to the storage layer. So years ago, we built ransomware detection directly into NetApp ontap.

So we have real time ransomware, um, attack detection built directly into where all your data is stored. And at Insight, we announced an expansion of that to also capture data breaches or data exfiltration, because we know most of the attacks that are happening today, they don't start with the ransomware, with the encryption attack. They start with copying all of your data, then they encrypt your data so they can double or triple extort you.

Um, so for us now, we can actually capture as the exfiltration is happening, so you can block that user before they get access to the majority of your data. And top of that, we built in an integrated, um, isolated recovery environment so that if there is a malware attack after we alert you to it, and you've gotta do some basic cleaning, right? Say they get to 1%, 2% of your data estate, we can establish a clean room for you, find the latest known good copies of data, scan them to make sure there was no malware previously embedded in them, and then bring them back online for you all as part of one integrated recovery process.

And so that was, that's the NetApp where NetApp ransomware resilience service, kind of in a nutshell. Excellent. You know, it's funny, we, in the last couple weeks, one day we had a report that ransomware, it's down one day we had a report, it's back up.

Uh, it, it continues to be a thorn more than a thorn. It continues to be a major pain Yeah. For organizations all around anyway.

Hey Jeff, we're about outta time. I wish we had more time to go over 'cause there was more on insight, but you know, people can go read that on the website, quite frankly. Yeah, absolutely.

But talking about, you know, this shift that we're, that we're all undergoing, right? It's, it's different than the Solaris to Linux thing, right? Yeah.

This is, this is just a whole different time warp. And, uh, it's gonna be interesting how NetApp and companies out there, right? Seize the moment and mm-hmm.

And ride this wave. Anyway, thanks for coming on text on tv. It's a pleasure to have you on here.

Continue success, keep it up. 18 years at the same company in the Valley is more than just, you know, a little unusual. It's, it's quite an accomplishment.

So, congratulations. Thank you. Appreciate It.

Thank you. All right. Jeff Baxter, VP product marketing here at NetApp.

We're gonna take a break. We'll be back with more tech drunk tv. Hey everybody, welcome back to Ingram Micro One.

We're here with my new friend Eric, talking about the relationship between Proofpoint and Ingram Micro and how that's kind of changing the way we transact business in the cybersecurity space. Buddy, welcome to the show. Thank you.

Appreciate it. Well, one of the things that we do know about cybersecurity is it changes all the time, right? I mean, just when we think we've got one mo whack the next one comes up, how does the relationship between Ingram Micro and Proofpoint kind of help the providers and, and, and their customers at the end of that day kind of stayed current Yeah.

And protect them from the threats we face? Yeah. Well first I'll say that we celebrated our 10 year anniversary with our Proofpoint partnership this year.

Um, and it's been a very exciting partnership. I've been involved with it since we signed the contract 10 years ago. And we've certainly seen the strength of this partnership grow and grow.

And so it's been a really exciting relationship for us. But, you know, early last year, mid last year, we sat down together and just started having conversations about what can we do to free up the channel to respond faster to the threats that their clients are seeing. And this, you know, where we are today is a result of just asking those simple questions.

It seems like what you guys are really doing is taking as much friction out of that process as we possibly can. And some of that may be using AI and other technologies, but what is the mission and what are some of the things you're targeting to kind of make it easier to do business? Yeah.

Well, you know, one of the things was for the commercial market segment, again, we wanted to empower the channel to act faster, right? So today, a channel partner as a net new opportunity, they can go to the X vantage platform, you know, new products, new solution bundles. They can select, you know, the product that suits the needs to address the risk that that client is facing.

And instead of waiting three days or as long as it takes to fly to the moon, right? They can submit this information in there and have it approved, quote back in minutes. Right?

And that's a game changer, you know, for the partner community. And that's critical. 'cause the end customer doesn't have a lot of tolerance for that process, right?

Right. 'cause they're sitting there saying, I can't really say to the end customer, lemme get back to you in three days with that and Yeah. You know, hopefully nothing bad happens.

Exactly right. Yeah. It, it is all about, you know, speed to execution and, and helping them to respond to those threats faster than ever.

Um, what went into the, uh, measurement side of this equation? So how are you tracking success? How are you looking at working with the partners?

What's on the back end of this thing? Well, you know, I would say that there, there's been a lot of new, right? It's a new process.

It's a new way to go to market with their channel organization. It's a new way to go to market with their commercial sellers. And this whole journey, this whole 10 year journey we've been on, our role has been to be an extension of their channel.

And so for many, many years we would talk about, I can do everything that a Proofpoint channel account manager can do, but I can't approve special pricing. Well, now we can. And so there's been, like I said, new solution bundles like prime threat protection and others.

And, um, you know, for us to be able to bring that to market faster than ever, you know, for, for a solution provider to sit down with a healthcare system and say, Hey, I can turn a quote around for you while we're sitting here having a conversation. You know, those are the types of things and pipeline continues to build for these commercial ready, ready to go SKUs Outta curiosity, I noticed that you guys have invested in AI and around the sales agent and you're taking historical data and market opportunities, correlating that and giving some advice to the partners. I think the partners are also building their own AI agents or will eventually into the purchasing cycle, right?

How do you think this whole ecosystem will evolve in time? Well, that's what Sanjeev is here for, right? We got to see it firsthand and uh, you know, I think we're just in the infancy, right?

When I think about, I've been at Ingram a very long time. I've been in the cyber practice for roughly 25 years. And I think where cyber was 20 so years ago was where we are with the maturity of AI right now.

Meaning, you know, you've got thousands and thousands and thousands of partners that are capable of selling cybersecurity. And our role there is to help them to go to market faster, to be more profitable, how they show up differently as it relates to kind of all these things. Ai, we're in the very, very early stages of partners really formulating their AI strategy.

And certainly as you think about cybersecurity, I mean, the game is changing right in front of our eyes with AI too, right? Yeah, absolutely. Where it's not just the good guys using it, the bad guys are using it just as much.

Which Brings us to our next question. Are you starting to hear from the partners saying, Hey, we think there's gonna be a cybersecurity opportunity around ai, and what can you guys do to help us get them? Yeah, and again, it goes back to being the extension of that channel, right?

And I know as an example, you know, pretty much every cyber vendor out there, Proofpoint included, is, is using AI right? To, to reduce the burden of, you know, day-to-day security practitioners, um, and ultimately respond to these human-centric critical threats faster than ever. And so we'll continue to see that, right?

So, yeah. What, uh, metrics are you guys actually tracking? I mean, how do you measure success?

How do you know what success is? Yeah, well, I would just say, you know, look, we work very closely together and, uh, we work very closely with our commercial sales leader and, uh, Sherry Rhodes who was here yesterday on stage. And, uh, we, we hold each other accountable for some very specific metrics and, uh, and I think we are definitely going in the right direction.

Of course, security requires a bunch of tools and solutions, right? There's no one size fits all. So how do you kinda work that with Proofpoint when you're trying to build a solution where there's multiple right things involved?

And how does that whole process get smoother? Because we're taking out the friction. Yeah.

Um, you know, Microsoft is a great example. They've got a good, you know, integration and partnership with Microsoft, and we wanna make sure that we're bringing the Proofpoint message to the Microsoft community and vice versa. And I think you'll see us continue to build out these things within the X Vantage platform as well to make those recommendations.

Hey, if you're picking up Office 365, are you thinking about Proofpoint threat protection to augment that as an example? So I think there's probably more to come there. We of course, you know, have had the notion of customer relationship management forever and a day, but there's also vendor relationship management, right?

Yeah. Yeah. Is that becoming a discipline in its own right?

And a, and a thing that you and the partners are kinda working through, but it seems like this too can be, uh, a set of best practices. Absolutely. And, um, you know, there are other vendors that each have their own specific channel initiatives that they're trying to accomplish.

And, you know, just thinking back, you know, the, the, how this went with Proofpoint was, it was a design thinking session. What are you trying to accomplish? And how do we go build something unique together?

And that's what we've done. Now every vendor, you know, has a different strategy or slightly different, but I think this is just the starting point for, you know, how to capitalize on this platform to go drive meaningful results and, and ultimately to empower the biggest sales force there is, which is the channel To that. So Proofpoint partners have a lot of choices when they wanna go get something.

So we were talking to them right now. What would you tell them about why Ingram to work with Proofpoint versus anywhere else? Yeah.

Well it's, you know, the platform is important, but it's also the people. And I think we've done a very good job with our people, again, acting as an extension of that Proofpoint team better than anybody. And the, the relationships that we have with the solution providers and those partnerships are super important.

And at the end of the day, you know, people buy from people they like, the platform makes it easier to spend more time having discussions about the right threat protection. And so, you know, we continue to get feedback and learn, right? There's more enhancements coming, you know, as you start on a journey like this, you don't know all the answers.

And we listen to our partners and they say, Hey, have you thought about this? So we're launching deal registration as an example, that will be all there and instantly available on the X Advantage platform. I think from there we start thinking about, Hey, what lifecycle services do we start doing?

How do we help educate the channel on what new solutions should we be upselling as we go down this journey together? So I think the sky's the limit. I think one of the other things I've seen you do is invest more in your own cybersecurity professional services team that has the partners back.

Yep. Um, is that too gonna become something that I can maybe call on a on, on a moment's notice, just like the transaction. Absolutely.

Yeah. And we're already doing some of those services specific with Proofpoint and others. But you know, I've always said that, you know, there are thousands of cybersecurity partners in the United States, right?

Our role is to help the partners buying from Ingram to be more profitable and proactive. And one way we can do that is by injecting services capability to make them look like they're the top security practitioner in the country. And they can do that with Ingram Micro.

So we used to have this line between what we call resellers and managed service providers, but if everybody's leaning on you a little bit for the services, eventually all the partners kind of evolve in the service providers. Yeah. Said another way, right?

I mean, services, you're gonna build it, you're gonna buy it or you're gonna partner, right? And so we want to be that partner that, you know, even if we're not the one delivering some of those services, although we've built it out for Proofpoint, they can tap another partner on the shoulder to deliver, you know, whatever services they need. Or perhaps it's in a adjacent part of the cybersecurity landscape.

Mm-hmm. Yeah. One of the challenges that we hear is, and there's been a lot of talk here at the show about this notion of focusing more on business outcomes, but in security you're kind of trying to prove a negative that something didn't happen.

So therefore how do I kind of measure that or Yeah. Or convince somebody the value of that. So how are the partners kind of measuring the value of their services and how are you helping them figure that Out?

Well, I mean, there's only one right answer, right? You're gonna get breached or you're not. But, um, but I think, you know, it has to be more of an outcome discussion and not about a point product, right?

What are the critical risks that you need to solve for today? Are they using assessments to truly understand what those risks are? And then you kind of map it back to what outcome a technology or a, a multi solution technology stack can go deliver.

Somebody once told me being in security is kinda like being in the army. It's long moments of sheer boredom followed me a few seconds of sheer terror. Uh, so when there is a crisis and there is an incident, um, how do you kinda work with the partners to ring that alarm bell and kind of get all hands on deck and 'cause they're gonna call you?

Well, um, I don't know about that because I don't know, they're, hopefully they're not calling us because we've provided them with the right tools. But, you know, uh, it's a fascinating industry. It's been one that's fun to watch, you know, it makes the headline news pretty much every day with another mass scale breach.

You just hope that it's not your day, right. So, but yeah. Yeah.

Well To a certain degree we've probably all been breached. It's just a question when we figure it out. Exactly right.

Exactly right. And to that point, um, it does feel like the nature of the game has changed a little bit. It's not just like, how many breaches did I stop?

But it's on the assumption that we all know that we have been breached somewhere. And when it goes active, I think the measurement that people are looking at from the end customer side to the partner is saying, how long does it take you to respond to that and close that gap? Exactly.

Right. And for that, I need to have this whole ecosystem in place. Right.

Right, right. Exactly. Right.

And I mean, even like if you think about the cybersecurity insurance industry, right? We're not an insurance broker, but you know, we have partners that can go and help a client to understand not only when you need cybersecurity insurance, but what kind of remediation can you have on retainer, right? And ultimately, what solutions do you need to buy to reduce the amount of that premium that you're paying, right?

So you have to follow the NIST framework as an example and make sure you've got a well architected security defense and then you know, when the catastrophe happens, how quickly can you respond? You mentioned the insurance industry and it seems like they are shifting more end custom words towards consuming managed security services as a result. So have you seen the partners kinda shift the balance of their portfolio?

I think they have to, right? And um, ultimately if you're having a comprehensive discussion with a client, cyber insurance has to be part of it, right? And um, so you've gotta have the right partners to make that happen, but you also have to help them reduce their premiums.

And so yeah, that's all part of the whole integrated ecosystem. Is there something that you see the partners doing today that you would advise them to maybe tweak or change to be more efficient? Is there something on their side that you look at and you go, Hey guys, you know, if this one little thing would change the world would be a little bit better place?

Well, I don't know if I'm answering this directly, but I've been at Ingram a long time and we talked to partners about all the services and all the things that we can do to help them. And without going through a laundry list of 9,000 things, I think it's just simply put, which is if you have an area of need and you don't know where to go, go to Ingram first. There was a guy on main stage with Paul yesterday and his quote was up there.

I wish I remembered what it said, but it was like, if I don't know what to do, I Ingram it, you know, which means I'm gonna go to Ingram and I'm gonna find the right answer, uh, to help, you know, whatever I'm trying to solve for, for my client. To your point about that, one of the issues I hear a lot about is there's just a lot of skews to navigate. Yeah.

And they get confused and it becomes overwhelming. Yeah. So are you guys, we're thinking about anything maybe with Proofpoint or somebody else to reduce those numbers That's ex exactly what was done, which is creating these solution bundles that are designed to stop human-centric threats.

You know, whether you need, you know, obviously there's email security and threat prevention and all these things, but you know, you choose the package that you feel is gonna best suit that client. And so that's what we've done is create very priced to the market and ready to go and ready to solve those critical risks. Alright, we are close to the end of the year.

What are you most excited about going into 2026 Thanksgiving? Oh no. Um, uh, I love turning the calendar.

I love building the plans for next year. And, um, I think we've got some exciting growth opportunities, you know, here in our future with Proofpoint and others. And so it's an exciting time.

You gotta close the year strong, but I really enjoy the planning season and getting ready for next year and setting lofty goals that we're gonna go hit. Alright. Hey folks, you're heard in here.

A good mantra for the coming year. If you're a Proofpoint partner, eliminate the friction. Hey buddy, thanks for being here.

Thanks. Appreciate it. And we'll be back in a minute.

Hey everybody, we're at Ingram Micro one and we're talking about innovation in the channel. My new friend Hope here. Hope.

Welcome to United States hopes from Australia. It's a very competitive marketplace in Australia. What are the challenges that you're dealing with?

I think many of our MSPs are facing a number of challenges in the Australian market, which is highly competitive, as you've mentioned. I think one of the areas that a lot of our MSPs are, are really struggling with is profitability. A recent study suggested that 95% of MSPs in Australia have listed profitability as their number one concern moving into 2026.

I think one of the other areas of concern is the skill shortage that we face in Australia, particularly in the areas of cybersecurity, cloud and ai. So our partners are having to invest in upskilling their existing teams or invest in capability, which is very expensive and difficult to find. And I think the third area where a lot of our partners are really facing challenges is our market is highly complex.

Our ecosystem, which was once linear, is now evolving rapidly, and it's a 360 ecosystem. So we're seeing far more complexity around licensing models, multi-cloud, multiple vendor solutions, and end customers really expecting higher return on investment and strategic advisory services from our partner community. So given all that, what distinguishes the MSPs who are succeeding in that marketplace?

We've got a number of MSPs that are also thriving despite the competitive challenges and the headwinds that they're facing in the market, where we see our MSPs with a very clear value proposition that they've defined and they can execute against. We're seeing them have a great deal of success where we're seeing MSPs aligned to business outcomes and solving business challenges. We're also seeing a great deal of success with our MSPs.

On the other side of that coin is the ones who are maybe struggling a little bit. What do you see them doing that maybe they should be thinking about fixing? Yeah, absolutely.

So I think the lack of clarity on the value proposition is, is hard. I think customers, MSPs that are struggling with that complexity and not really able to navigate that are finding the market difficult to, to navigate and, and the high degree of competition. So really being able to understand where you add value, where you play, whether that's niche or more broad, more broadly, but really sticking to your capabilities and then expanding upon them within your accounts, I think is a, is a big challenge for many of our partners.

Given those issues, one of the things we talk about at the show a lot is business outcomes and focusing on those business outcomes. And that's a lot more than just providing an IT service per se. Um, what are customers looking for from you?

What are, is that changing? Are the expectations changing? Absolutely.

I think as the market becomes even more complex, I think our role as distribution needs to evolve with the changing needs of our partner and vendor community. My team and I recently this year traveled, traveled all around the country, uh, and our exec connect series, we spoke to 500 C levels within our partner community to really understand what the challenges are that they're facing, where they see the opportunity, and to really lean into areas where we feel Ingram Micro can help support their growth. I think that where we are focused is really around making sure that we're investing in the areas of opportunity to support our partners.

I mentioned the skill shortage issue. We've really invested heavily in some of those areas to ensure that our partners can leverage our skillset and our capabilities to not have to invest in that headcount themselves and to really leverage our expertise. So I think that's one area where we're really supporting our partners to be successful.

Another area of huge focus for us is on enablement. So making sure that our partners and their teams are enabled around what's currently moving the solutions and stacks that are really accelerating, but also future proofing them around ai. We recently launched in Australia enable ai, where we're able to provide assessments for our partners development tracks for our partners to help them capitalize on the AI opportunity that's currently accelerating in market, although a little bit slower than the rest of the world in Australia, but certainly a big mover in the next two years.

How are the partners themselves gonna evolve along that part of the conversation? Are they changing their business models a little bit? Are they getting a little more nuanced about how they deliver something in terms of cost structure?

I mean, you know, walk that through the final equation. I think we're all looking for operation operational efficiencies. We're all looking for productivity gains.

I think our gram micro ourselves through our advantage platform is really leading the way there. It's all about how do we remove operational friction and how do we free up ourselves and our partners and our vendors to do more and sell more and grow more. So I think we're, we are playing a pivotal role in helping our partners evolve their own operating models to be more platform orientated, to be more digital in nature so that we can do more for less essentially.

Mm-hmm. And following up on the skills conversation a little bit, in your professional services, do the partners need to get savvier about who they hire full-time versus what they're leaning on from a distributor partner like yourself? And it seems like the model's a little more fluid.

I would say it is a little more fluid, and I don't think it's a one size fits all. I think that you could have partners, for example, who have got a huge amount of PS capability, professional services capability, whose talent bench may be fully utilized. And so they need capacity and that's where they can lean on distribution to do that.

We have other instances where we have partners who maybe haven't got that skillset right now from a PS perspective around ai, and they can leverage our skillset and our capability not just locally, but also globally to help augment their capability and give them access to opportunities that perhaps they haven't got the skillset to access. Today We started talking about how competitive the marketplace is, but are the partners working with each other? Are they trying to leverage up a little bit, not just Ingram, but each other and there's, that's being facilitated through you guys?

Absolutely. So Trust X Alliance is a, is a global program where we essentially bring together, uh, you know, 150 plus partners into a shared community or ecosystem where knowledge is shared, ideas, uh, are born and ideated and, and that shared knowledge is what makes our ecosystem stronger and we facilitate that. I'm really proud that actually Ingram one this year we've launched our own Australian Trust X.

So we're part of the global community now, and it's really born for the reason, allowing our partners to share ideas, cross-pollinate, uh, leverage each other's skill sets and strengths to do more together. And I think that's a really powerful role that distribution can play. We talked about the skills gap a little bit, but are there particular skills in areas where you're seeing partners need more expertise than others?

Are there? It seems to me there's a lot of emerging technologies these days. I might not have anybody who knows anything about those things, but, um, where's the pressure?

Yeah, I think, um, beside profitability, there was a canal study that was recently done that indicated that the skill shortage was also one or two top priority of concern for partners in the Australian APAC and even global ecosystem today. So I think where we're seeing partners struggle is around those technology stacks that are really accelerating. So cybersecurity, cloud modernization, um, ai, they're really the areas where partners are struggling to fill some of those skills.

The shortage of talent, particularly in Australia is, is exceptionally high. The cost to get that resource into the business is extremely high and they're all looking to do more with less. So it's a little bit of a conundrum that I think many of our partners are facing today.

Mm-hmm. And the type of people you're looking for if you're a partner are kind of a little bit different than if I'm just an internal IT organization where I'm looking to hire somebody. 'cause they have to have some nice ability to have a good experience with a customer, right, right.

That's requires a lot of patience, usually the first attribute. But, um, what kind of folks are you guys looking for and are there, are there things that you and Ingram are doing to kind of coach and train people that become more suitable for being in a professional services role in a partner organization? Yeah, so I often say we're looking for unicorns Because The skillset is actually really hard to find.

So you're looking for higher levels of technical competence, but you're also looking for people that have an element of charisma and, and a sales bent to, uh, how they engage with customers as well. So being able to be technically competent, but also be able to communicate with partners around business outcomes and partner's, customers around business outcomes, I think is critically important to the success of people in those roles. We do a lot of work with the technical community at Ingram Micro around enablement, uh, sales enablement and technical enablement to really ensure that our partners resources are equipped to be able to go out and successfully manage end user environments.

Mm-hmm. Are you doing anything with the local universities or colleges that, uh, help maybe expand the bench a little bit? We are actually, so we have a relationship with a university in Queensland that we work closely with to really help develop nurture and foster talent coming out of their cybersecurity, um, university courses.

And we incubate that talent into our own organization, but also expand that into our partner base so that our partners can identify talent early and bring them into their own organizations as well. Now we're here at the show, so I've had the benefit of walking around, but people are talking about, um, X Advantage and Growth Tracks. What is that exactly?

Yeah, so we launched Enable AI in Australia in July, August of this year, and it's really about enabling our partners to assess their own skills, understand where they are from a skills perspective on the a on their AI journey, benchmark themselves against other similar partners to understand their readiness. And then for us to build growth tracks bespoke to those partners that help them enable and accelerate their AI capabilities so that they can capture that market opportunity. So we're well on the way there.

We have 25 partners in Australia already that have started that journey. I think really interestingly also with enable AI is the number of use cases by vertical that partners can pull down and learn from, but also use to position, to position with their end users to capitalize on AI opportunity as well. Mm-hmm.

How do you make sure that the self-assessments that people are taking are, shall I say valid? Because, I mean, I know I have a very grandiose opinion of my own capabilities, but they're probably not realistic. So how do you make sure that partners kinda, you know, answer those questions in a way that are truthful In Australia?

I think culturally we're very hard markers and we're very critical of ourselves and others. So I feel as though that the partners probably do a fairly good job of assessing themselves. I mean, at the end of the day, you need to be honest in that process in order to maximize the opportunity and, and the enablement tracks that we have.

So I do think that the partners are, are fairly good critics of themselves and they know that to really make sure they can leverage that program, uh, they have to do an honest self-assessment so that we can help them appropriately. Talent is everything in this business. What's your advice to the partners to make their organization like the most attractive place where the best talent wants to work?

I think it's really important that we nurture our people. Um, I think being a people first organization is super important. You hear Paul Bay talk about it all the time, uh, and we live it.

It's in our DNA at Ingram that, you know, we're, we're customer and people obsessed, um, and we put our people and our customers first. And I think if you can provide that in an organization, I think you become quite sticky and I think you get, uh, you know, a lot of loyalty, your attrition drops and it's somewhere people want to stay. I think providing people with the opportunity to learn and to grow and to adapt to what's happening in our market, um, but enable them to be ready for what's next as well, I think is critically important.

So I think investing in people, being a culture that people are in inspired to, to, to be in each and every day and and to feel purposeful about their work. I think if you can do those things, it's easier said than done. I think you're able to retain top talent and attract top talent, which is super important for us in this industry.

Do you think that it's important for the folks that run the organization to exhibit this one attribute that I think is important, but, um, they seem to be always have an appetite for continuously learning. They're constantly curious and one of the things, you walk around the show floor, everybody here is kind of checking out the latest and the greatest. And I cannot help but wonder if that's like one of the, the core attributes that is overlooked about success in the channel is that everybody in it seems to be fundamentally curious and always learning.

Absolutely. And I think that's what makes our industry amazing. That's why I've been in the industry 25 years and I can't imagine being anywhere else.

I mean, the transformation in this industry from the, you know, the birth of the internet to, to cloud, to ai, um, to, you know, cyber, it, it's constantly evolving and you have to remain curious and you have to be willing to challenge the status quo. And you have to be willing to expand your mind beyond what you ever thought was possible. And I think that's the beauty of working in this industry.

And I think I'm very lucky to be surrounded by partners and vendors who are always curious, always evolving, always ideating and innovating. And I think that's what keeps us young. Maybe not biologically young, but mentally young and, and really, you know, keeps us all super interested and committed to the ecosystem that we operate in.

Keeps us young and second part stressed. Yeah. So it's an missing combination, young And gray.

There you go. So as we've mentioned, you're from Australia, but we're here in DC or just outside of DC and there's a lot of international cooperation. The globalization is still a trend.

I mean, there's a lot of issues in the world, but still people are doing business across countries. How is that playing out in Australia? Are you partnering more with people from overseas?

Are you seeing more of that and what does that look like? Yeah, absolutely. So globalization's absolutely impact in the Australian market, and I think you can look at that positively or negatively depending on how you frame that challenge.

So I think for many of our partners, it means that the competitive landscape has become heightened because we're now not just competing locally, we're competing globally. I think on the flip side, what that presents is an opportunity, however, for partners who are looking to expand into other geographies and locations, and I think Ingram Micro Australia is very, very well placed to be able to support our partners to tap into the globalization opportunity, um, where a, a local distributor with massive global presence, uh, and the backing of a very large global organization, we can scale into 57 countries with our partners out of Australia. So I think for us, it's more of an opportunity collectively to harness that opport, that growth that we see in other parts of the world and in emerging markets.

We're a very mature market in Australia. Organic growth is hard to find, and I think geographic expansion is critical for our success and our partner's success. All right, folks.

Hey, if you are looking to partner with folks in Australia, find hope. Yeah. She knows everybody.

Yeah, we'll be back in a minute. Thanks. Thank you.

Thank you so much. Thank you. Um, just, uh, as an introduction, I run cloud products within ico, so any workload that touches any three hyperscalers, uh, I have portfolio coverage over and, uh, I'm in year two, um, at Haiku as well.

Come from the world of, uh, you know, protecting Kubernetes and, and a lot of the infrastructure elements that, uh, you know, power ai, uh, today as well. Um, I know very often when people talk about ai, they, they, there is a lot of importance given to the, to the shovels, the GPUs, the models. Um, but not a lot of conversations happen on the data that actually drives the intelligence, uh, uh, for ai, right?

So our goal today is to kind of explore, um, you know, how data protection needs to evolve, um, in the age of ai and how do we protect the data sets that are being generated both by AI and for ai, um, uh, these days, right? And also the, all the infrastructure that needs that gets put in place, uh, to make AI possible for a lot of the organizations. And how today, cloud is the default delivery model for a lot of ai, uh, in, in many organizations.

So let's, as we talked about, let's, let's start with, uh, cloud, I call it the home of AI these days, right? This is where very often people start, uh, when they start their AI initiatives, uh, cloud providers have more access to, you know, GPUs than, you know, an average enterprise, uh, in the market. Uh, and, and you get the burst compute you need, uh, for a lot of the model work that you are doing within the organizations, right?

So cloud has become the home of ai, but with cloud, uh, there is a whole bunch of problems that exist when it comes to data protection, right? We call them, uh, the silent killers. We call them the lifestyle diseases, um, with this dataset, right?

You have too many, uh, applications, you have too many as a service, uh, elements that are kind of put together to drive your pipeline, and that creates a lot of the console chaos. You have to use multiple consoles to manage, you know, what's happening with your, with your pipeline, right? And, and that also creates a lot of a p and automation spread all.

And the other part of it, there is a lot of data being generated, but there is not a lot of efficiency built into the cloud. Um, and again, cloud is in compensated for driving a whole balance of efficiency in terms of how you store that data. Um, right?

So we are also seeing that there is this obesity when it comes to the amount of data that you're storing, you know, within, uh, uh, your infrastructure and within your tenants. And despite all of this, there are still blind spots when it comes to AI and AI services. There is a large number of services being built in all of these cloud providers to make AI possible, but the data protection vendors haven't really kept up with, uh, all that dataset and even being able to serve that data back to the customer in an AI friendly fashion.

You know, that's not really possible today with a lot of the solutions and, and what we are looking to solve, uh, with, with heico. That's sat what you, Satya? Satya?

Yeah. Hi, guy Courier with Futurum here. Um, can you talk a little bit more about this first one fragmentation?

Um, one of the big drivers I think, towards data lakes has to do with avoiding this by just having a simple way to throw everything in a single bucket. And that alone, I feel like a sort of a typical data lake or data lake house vendor would say that solves this because that provides you with a single con uh, consult. Maybe it creates the challenge of all the chaos having to be reached out to, to pull everything in.

But how would you, how would you respond to, to that? Yeah, look, uh, it's a great question. In, in a couple of two or three slides down the road, we actually walk you through what a typical stack look like.

I Knew that actually. So, uh, please continue, Right? So in, in what you do see is the lakehouse, which we'll spend a lot of time today talking about, uh, are becoming the unified platform in many ways, right?

Uh, it is doing more and more unification. It's multimodal, it's versatile. You can do both, you know, optimize for both storage and analytics.

You know, all of that is true. Uh, but it is not true that today's data pipeline stores primarily in those workloads. It is, again, even if it is primarily you need to preserve, meaning when you're protecting AI data sets, there is a lot of data that sits around these data lake houses.

Yeah. That needs to be protected as well to protect your full infrastructure. Again, uh, great question, and, and thank you for that.

In a couple of slides will actually show you what a sample AI data stack looks like and, and why that kind of coverage and, uh, uh, for that fragmented workload matters. Well, In, in, in my opinion, I'm actually following with my fellow delegate, uh, Keith Townsend here, uh, his, his guidance on this, it also does create a problem of its own because of data gravity. The more you collect, uh, data, however processed or managed in one place, uh, the stickier it can be there, even, you know, that, that really hasn't been solved.

Yeah. The the good thing is as we are in a world of, you know, providing insurance for that data, right? And when data cavity shifts, it's our job and responsibility to kinda shift along with that and build our applications to make sure, you know, that data gets protected.

And I think we are seeing that with the cloud where the object storage is perhaps now the landing spot, uh, uh, for a lot of the data sets and the lakehouse of the new systems of records. So between object storage and lakehouse, you're seeing more, more and more data sets kind of landing in the cloud. And that means more and more applications and the processes and, and, and, and those things will also have to be built in the cloud, uh, because again, the data is gonna pull you where the data is, uh, right?

And, and, uh, we certainly see that and, and why we think cloud is the new home of, I mean, it's not the new home. It is the home of AI because again, lots of customers' data is, is being stored in those lakehouse and, and object storage in general, right? And, and talking about cloud, one of the things that Haiku is really, really good at, uh, is this coverage in, in, we talk about insurance industry and data insurance industry.

Everybody knows coverage is currency for us, right? How many workloads can you protect? Um, and I'm using, I mean, each of these cloud providers is now what, offering over a hundred to 200 services, uh, uh, for a variety of things that the co the customer wants to do.

How do you keep up with these growing set of diversification of your data sets? What used to be within four walls of your data center is now just spread everywhere, right? And how do you protect your data sets in this context?

And one of the things iku is really good at is the coverage matrix. We have protect over 90 plus workloads today, and we already have the broadest coverage, uh, when it comes to cloud in terms of the number of workloads, uh, uh, we protect in the, in the cloud, right? And so let's talk about how AI, uh, changes the game a bit, uh, in, in the cloud and, and how a lot of our trends kind of carry over in this, in this context.

As you know, uh, AI is not just, I mean, this delegate clearly knows that AI is not just about chat GPT and, and, and your interactions, uh, with with the chat platform, right? And, and there is so much that is happening in the backend, uh, when it comes to, um, what an AI effort in, in entails in an organization. You start with, you know, ingesting training data sets and actually creating training data sets.

One of the things that we are learning is there is a huge increase in spike in storage, in, in, in storage around data lake houses as well as object storage, because everybody's creating one additional copy of all their data sets to kind of feed AI to drive those insights, right? People didn't have a centralized repository of all their data sets in one place. You know, now AI is kind of pushing you to create that set of training data sets, right?

But raw data set doesn't actually get the job done, or doesn't get get, uh, doesn't optimize. Uh, for those model training, you do have to enrich the data and make the data look, you know, friendly to AI so that it's easier for AI to ingest. There's a lot of enrichment, uh, that happens.

And then you go through training, it's multiple runs, uh, and at each point you're creating weights, you are grading logs, you, you create a lot of the, uh, tunability that happens in, in, in that case. And then you finally kind of serve that to your users. And, and, and then there is a full feedback loop in terms of just making that, again, part of what you learn from, uh, in general.

The key thing to notice, uh, this is ai uh, implementation in a lot of organization is a relay race, right? The baton gets passed from one to another to another to actually drive the outcomes you need, uh, uh, in the organization. And if losing the baton once, you know, in this real areas, you kind of break the traceability in ai, the trust in ai.

And, and this is really where the challenge is when it comes to data protection. If you don't follow this relay race and ensure the baton gets passed around from one to the other, and you're fully capturing that, uh, particular race, you know, you're, you're not really in the race in that, in that context, right? Um, the Quick question on, 'cause there's several, this is Keith Townsend advisory bench.

There's several challenges around data protection as you hand off from one process to another. And the challenge is not just protecting the data, but the transformation. So let's, you know, uh, focus on a rag process that ingests the data, however we're getting it, and then we go to enrich the data for ai, whether that's serving up via inference or we're serving it up for training.

A lot of the mistakes happen at this level, and the visibility of being able to, uh, trace back the issue and revert back to state is a legitimate challenge. Are you guys helping with that problem or just the ability to recover the underlying dataset? Yeah, look for each of those service.

Like for example, if you are storing your vector databases in, uh, in all I DB as an example in Google Cloud, right? Um, what we do have is the ability to capture that state, kind of bring that back to the state. One of your model runs, runs a Mac, and, and it's, uh, uh, uh, it's, it's not the data set you want.

And, and a lot of filtering happens in, in, again, AI model training as well. You have a data set that is potentially poisoned, right? In all of those cases, we do allow you to kind of bring back, uh, to a state, um, where your previous models ran from potentially, right?

So we make that possible to be able to restore back to an earlier state. But a lot of the times it's also about restoring the data in a format that AI itself can understand, uh, as well. So it's about being able to bring back the states, but also being able to produce that data back for future analysis to see if there was a problem with it, right?

Yeah. So for case in point, like if I have, if I'm enriching the data with JSON so that it's better, uh, injected into my, uh, data pipeline for, uh, my vector database process, what happens two or three revisions down a line, I discovered that a added that I made to the JSON structure actually broke something else. And reverting back to that clean state is not simple.

Yeah. Yeah. Schema drift, pipeline corruption, you know, all of these are, are, are alleged problems.

Um, and, and my, my, uh, the, the good thing about I two is you can go back to an earlier bit, but you can also produce an offline copy of the data you already had so that you can understand what went wrong. So both those elements are, are supported and, and enabled through ico. So, Satya, this is, uh, Ray Lui, Silverton Consulting.

Do you plug into the checkpointing process during training? I mean, so I mean, data protection historically has been driven on a, a time basis or a change basis or something like that. I'm just trying to figure out where you play in training checkpointing.

Yeah, it's, it's not so much that we plug into checkpoints, but it is a natural point for you to take your backups, right? A checkpoint is usually integrating with your backup application to make sure that, hey, I have this model checkpoint, I've stored all the, you know, sampling weights and, and, and, and all of my metrics around my model implementation that's been stored in the registry. And that is a perfect time to kick off a backup so that you have, um, you know, all the data up until the checkpoint.

So whether you are your models as a new, new run, or whether you're taking a checkpoint to make sure that, you know, I have this state captured. So that's when they plug in with backup solution to make sure all these services that make the checkpoint protected as well. So if I have, and this is, that's not necessarily a bad thing, it's, that's just if I have a custom data pipeline, just as long as you're storing the metadata that I need to do the recovery that I need, and this you're work working with a sophisticated group, this is different than enterprise IT backup.

This is usually someone working specifically with data pipeline, and they're going to have specific requirements around how they recover and what, what, what they're recovering within the data. So it'll be interesting on the kind of the brick level, what I'm able to recover, uh, in this environment versus what I'd be traditionally looking at in haiku's traditional type of customer. So just to jump in here, this is Dave Graham from Men Commons.

So what moved to asynchronous checkpointing where you're in the entirety of your workflow is driven, you know, in a different direction where checkpoints aren't just fixed points in time anymore. There's something that is streamed. How does this obviate some of what you were talking about?

Because there is no return to zero anymore. There is a, an ongoing practice. You're ongoing and you're flowing through these models and through these training epochs in a different sort of way.

So your return to zero, your return to a known good becomes best, best state y Yeah. It becomes, it comes a little bit more ephemeral than it ever was before, right? So I think it was Carl that might have brought it up before, but you know, you can act on a checkpoint file, you know, 80 ks plus whatever or whatever dump comes outta that.

But how are you integrating above that stack then into the command and the command and control center of that training system or whatever ends up being used, whether it be cuda or otherwise Metadata, uh, to control where you Off, stuff like that. It is, this becomes the hydra problem, right? It become, there's so many different aspects of things that you would want to control because a lot of those checkpoint files are just there.

It's garbage anyway in the end, right? It's just there for, you know, cover your ass type moments, right? The metadata ends up becoming the driving point.

Yeah. You wanna make sure you maintain that. So anyway, so yeah.

So From a practical problem, I've run into this. So the, I've transformed my data, but I need to recover not the entire state there, your, your com your comment of returning to zero. I'm not trying to return to zero.

I've, I want to save the work that I've done up to this point and, uh, return a portion of my data pipeline to this original or to a different version of the state. I don't know how to, I don't know how to solve the problem if you guys are solving the problem. That is really interesting.

Well, if you have an entire NDL 72 that's chunking away at this stuff, you're gonna return to a known good state. You're gonna now have to pull 72 GPUs back to a known good state. I'm gonna recalculate that epoch, right?

Right. That's a big lift if you're gonna try to do that. So consistency wise, again, and it's not, I, I think the idea here is noble.

Absolutely. I just, I'm more curious to like, how do you now embed yourself into a process that is literally changing week on week, uh, as we, as we discuss these technologies? Yeah.

And, and, and what's really, uh, cool to hear is, I mean, what you are talking about in terms of, hey, being able to know go, go back to last good state and, and so on. I mean, this is what backup windows have been doing for years for any data set, right? Any data set that we protect is always changing.

Any data set that we recover is because we need to go back to a known good spot, uh, in majority of the cases, whether it's because of disaster, whether it's because of cyber attacks, whatever the reason may be, we go back. In this case it's because of experimentation, right? So at the end of the day, it's, we allow, we capture state and we allow you to go back to state.

And when, when it comes to capturing state, we'll also talk about how you capture that state in a more consistent fashion. And that's also things that, you know, we're, we're investing in and, and, and actually, you know, build some patent pending technology around how do you drive consistency among the data sets that you're actually protecting, that when you come back, it's not going to look at the data set that just came back as, Hey, what is this that I don't recognize? Right?

You need to be able to put back and expect things to run, uh, normally, and there is a lot of work to be done. We're not saying that we've solved every problem there, but we have solved some problems that are, you know, very key. And, and we'll walk you through those, uh, uh, consistency element as well, uh, when we, when we go through the solution in more detail, right?

So again, the point is lots of hearing data sets. Yeah, yeah. So lots of living data sets being produced, and again, those data sets need to be protected.

Uh, you drop the baton once, you know, you kind of lose a lot of the traceability and, and trust, you know, that AI generates, you don't want to be producing results later saying, Hey, we don't know how, why my models behave the way they behave. Uh, you need to be able to again, trace back and say, what, what actually happened? Uh, to an earlier question that came up.

You know, let's take the example of, uh, a a customer building an AI pipeline in, uh, in, in Google Cloud, right? You start with a workbench, um, that act that has, you know, direct integration into GitHub and, and BigQuery and so on. You deal with your training data sets, you create your feature, uh, data sets where your digital twin are, are stored.

You create prediction data sets if both what you send inside and, and what it comes out of it. And you generate a lot of model artifacts in, in, you know, vertex AI pipelines, right? So you, what you see is dataset is crawled across multiple services.

People use the best of breed solutions for, you know, each of this dataset. Yes, you are gonna see BigQuery, which is the lakehouse platform that Google has. Uh, right?

You see this in multiple call-ups. And, and to an earlier comment, yes, lakehouse are becoming that unified central platform, and we're really happy about it, but you're also seeing that it's not the only platform people use. At the end of the day, BigQuery can't be your workbench.

We got it can't be where your so store your source codes, and it is not the best platform for, you know, real time insights or, or, or feature data in, in, in general. So there is a lot of cloud elements that customers use to be able to create an AI pipeline. And what we are doing is to ensure protection across all of these services, uh, uh, with Haiku so that more and more of your AI infrastructure is covered, protected, and you have confidence in, in those capabilities, right?

So, and that's why this graph where we talk about, uh, all of our, uh, uh, coverage matters because you wanna be able to capture everything from VMs, which could be your workbench, all the way to vector embeddings, right? So all of that data needs to be protected and why, you know, our coverage really matters, uh, in the, in this context, right? And let's talk, uh, Lakehouse, right?

Um, and, and again, glad that group is already tuned in and, and, and, and, and kind of see this change happening, uh, like we see it, uh, as well, Lakehouse, um, I like to call this, they're kind of bringing together AI BI and what I call ci, right? The, the continuous insight for an organization, right? So the late houses are becoming the new system of record, and it is growing at a phenomenal pace in in, in, in customer organization.

We've worked with customers, you know, who said they have a near a hundred percent growth year over year on some of their lakehouse datasets. Um, and, and these are not a hundred percent growth when they are 70 gb, they're a hundred percent growth when they're at 700 terabytes, right? They're talking about, uh, uh, massive growth even at scale.

Um, and, and, and why you're seeing Lakehouse platforms like BigQuery and, and Databricks and Snowflake, each multi-billion dollar businesses, they're all around, you know, three to $5 billion businesses on their own, but each of them growing that nearly 50% year over year, um, they're creating, uh, uh, a nearly a $2 billion business every year and kinda adding to their, uh, uh, balance sheet at this point. And that's because again, customers are adopting Lakehouse, that single unified platform where they can store all this, uh, uh, dataset, right? And, and as we talked about, that creates gravity when more and more data set lands in, in, in a specific platform, you know, you start building solutions that protect that particular platform, that deals well with that particular platform and, and integrates first party integration into some of these lakehouse architectures with a lot of solutions that, you know, are built around these data sets.

Right? So, can Can I interrupt and ask you a question on this? Yeah, please do.

So for mere customer interaction, Scott Roon was al, um, you know, it seems like there's a spread of very mature customers who are really disciplined and have gravity in one place, but a very long tail and continuum of customers that don't and maybe will never need to. Where do you work best on that spectrum? Because like when you say lake house, it's like, well, I have a place at the lake, but I also have a place at the beach, and I've got a place in the mountains where there's streams, not lakes or oceans.

And I think that's more a real representation of where many enterprises are today. Yeah. And, and, and, and you're truth, the, the, the workload, the, the customer environments are often, you know, on both ends of the spectrum, right?

You have a very long tail, uh, in, in, in, in, in this curve. You have a whole bunch of customers who have datasets that are kind of spread everywhere, and you've got a lot of customers who are also going through consolidation of all that dataset into single platform, so they could drive outcomes, their AI outcomes a lot faster, right? And, and, and the good thing about our solution is that we were already built for the customers that had data everywhere.

Okay? Right? We had 90 plus workloads already supported, um, right?

Whether you keep your CRM data in Salesforce and GitHub data, uh, your source codes in, in, in GitHub and, uh, your databases in, in cloud sql, it didn't really matter. We provided support for all of those workloads individually through a platform of, uh, uh, through a platform. What we are seeing now is not just a group of customers that have data everywhere, but also a group of customers who are consolidating and creating these massive data sets that are feeding into ai.

And that's the journey we're seeing, and we wanna be there, uh, to, to support those customers who are doing that, uh, on this front as well, because these are workloads that have not historically been talked about in the context of data protection, right? Backup vendors didn't talk about BigQuery, backup windows, didn't talk about Databricks, um, and still don't. Um, and, and, and we're seeing that shift and, and, and are adapting our solutions to kind of become the best in breed for those workloads as well.

Okay. Okay. Um, and, and, and, and the thing that I also wanna highlight is, you know, this data set is yes, it's special, it can handle scale, um, but there is also potential for, you know, data loss in this context.

Uh, we talked about a couple of scenarios already. I think Keith mentioned, Hey, I could have a corrupted JSON file completely screwing up my, my mar my models. And, and that's certainly possible.

Schema drift is, is a consistent problem where models break. Um, you can also have poison dataset mean, we, uh, the first thing I hear from cloud vendors when they talk about these, you know, BigQuery and Databricks and so on, they say, Hey, there is no, we don't, don't give you access to a compute. Somebody can't just come in and run, uh, uh, uh, uh, or corrupt your dataset there.

But what you do give is access to APIs. And with those APIs, you could poison datasets that your models are being trained on. And that could be the face of AI in this model, but whatever is the issue, we do need to understand that any data, any service has chances of going wrong, you could lose them, and you wanna have the ability to go back and protect them.

And, and, and that's one of the key elements, uh, to really think through. Again, our job is to kind of follow where the data is going. We're seeing that the data is going to these lakehouse and object storage predominantly in the context of ai, and we wanna make sure that we have the best in class solution.

So you could get back those data sets if you ever need for whatever reason, uh, uh, that is just saying, Should update that last to include Amazon, because US East going down is kinda legendary at this point. Might as well make, make, make him note of that. Yeah.

And, and, and, and, and look, I, I think, uh, pointing that out is a little bit of like ambulance chasing as well. Uh, but, but in this context and, and, and, and, and absolutely. Um, and, and we'll talk about why that problem also is permeated across all these enterprises is because, you know, AI is, uh, the cloud is the home of ai, but you know, cloud is also puts a lot of people under house arrest, uh, right?

And, and people don't really have a way to create that cross-cloud resilience, uh, across these data sets. So yes, it's your home, but it's, you're also under arrest to, to some extent. And, and, and that again, needs to change.

And, uh, you'll see that we have some solutions that helps provide that cross-cloud resilience and, and shareability of dataset from one cloud provider to another. Um, as well. This is just to show that, um, data recreation, if you lost your datasets, is very expensive.

Yes, it's possible. It's possible in some cases I'll explain the scenario where it's not possible. But generally when it's possible, if you add infinite time, infinite resources in infinite APIs, of course you can build a lot of that data set back.

Very often what sits in Lakehouse is your second copy of a data set. It's already sitting, uh, uh, someplace else. And you need to transform that and store that in a lakehouse before you feed it to ai.

So one of the reasons people think, don't think about backups immediately, but what we are also seeing, uh, is these lakehouse are really good at streaming data, right? And, and customers or feeding, uh, whether it's for sentimental analysis based on Twitter messages, right? Or, or any social media messages that that may be, or ai, I mean, IO OT data sets and sensor data sets that are coming in from multiple places.

Those data sets are getting streamed directly into these lakehouse. So it's your only copy of data sets there, and if you lose them, you know, the sensor is not gonna regenerate those, those, those information for you. And Twitter is not going to give you back all of those old tweets without charging you for, you know, a spike in API usage, right?

I think at this point, what they charge, uh, almost 500 KA year, uh, to search through and, and collect about 50,000 tweets. And, and that's the kind of plan you're gonna be paying for. And, and, and in Salesforce, uh, as well, like, so what we deliver, uh, to this ecosystem, we talked about why it's important.

We talked about what it would cost to bring back the data set if you didn't have a reliable mechanism. Um, and, and now I want to kind of talk about what we do, uh, uh, uh, for this data set, right? And, and the key thing to highlight is we don't want to be just the FedEx that takes data from point A and store it in, in, in a different spot, right?

We're not just trying to move data blindly from point data point B. Um, one of the things that we want to do when you protect AI is not just protect the raw data behind, uh, that, but also protect the meaning, protect the intelligence that feeds into, uh, uh, uh, the, these pipelines. So when, for example, you protect, uh, a table, a raw data set that you're trying to protect, what Haiku does is that it has the mechanism to capture not just the raw data sets, but also the views, the models, the routines, the access policies of the schema of, so we are protecting all of that dataset and not just your raw tables, uh, that are coming in.

Again, a lot of the times you can get the raw table out and you can get the raw table in, but if you don't know how that raw table is processed and, and then you don't have good visibility into that dataset, why I think we're extending beyond just protecting raw dataset, but also everything that kind of sits around it, the views, the, the models and the routines and so on. And there was a question on, hey, consistency, uh, right in, in workloads like data lake houses, you know, take BigQuery as an example, right? People store data, sets the data.

It could be a couple of petabytes of dataset, but they're.