Jeff Baxter Highlights Data Management and Security Solutions at INSIGHT 2025

Hey everyone. Welcome back here to Tech 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 Tech Trunk 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, Hunting mammo, 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, maintain, 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 And they've 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 NetApp. So that's what I've been doing for the last few years.

But, so it's been a fun journey here. Um, love to change 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 sitting there going like, oh man, this is, this is taking me back.

Right. So, yeah. Yeah.

It Is a 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, 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.

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 OnPrem.

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 places to run your workload, um, and will 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? Mm-hmm. Today, when we talk about moving forward, you can't move forward without talking about ai, whether it's generative or agent or whatever comes next.

You know, everybody wants to know kind what's your AI story? How is AI impacting what you're doing? How are you gonna leverage ai, ai, a I AI 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 prepared to, for training, for inferencing, for retrieval, augment 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, at 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. I'm gonna try to play this right down the middle, but you know what, Jeff, A 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 agentic AI era where AI can actually take action unless you can give it 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 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, uh, 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, Cyndi Lauper's song, girls just Wanna Have Fun. LLMs just want to be helpful. Um, 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? Mm.

In what, in what way? Specifically 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 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, a 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 gonna be huge, if this isn't gonna 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, 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 an 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, has it been about a month since Insight now?

Three weeks? Yeah. Well, by the time people see this, it might be closer to a month.

Um, a 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, beared 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 exascale 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 continue 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 an 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 'em 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 w 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 is 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. But talking about, you know, the 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, um, 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. Continued 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 at Tech Drunk tv.