AI Space Race Survey with NetApp’s Russell Fishman

Hey, everyone. Welcome back here to Tech Drunk tv. Happy to introduce you to my next guest.

We were talking so much before we got on camera. We almost didn't have time to do this. What, but I wanna introduce you to Russell Fishman of the Stanford Fishman's.

He's Stanford Fisher Fisher. He's the senior director. Yes.

The senior director, global Head of Solutions product management over at NetApp. Russell, welcome to Techstrong tv. How are you?

Very good, and thank you for having me, Alan. My pleasure. Russell, as I mentioned, your senior director solutions, uh, global Solutions product management over at NetApp, but give people a little bit of a sense of your journey.

Yeah, thank you. Yeah, well, um, based in the northeast of the US and you can tell that by my native accent, my native folk. Yeah.

You sound like a Yankee Rocks accent. Actually, I was gonna say it's a, yeah, it's a Yankee accent, but go ahead. It's a Yankee accent.

Yeah. Yeah. So actually no, I, I started off my, uh, career in the uk, uh, originally an economist, uh, long story how I got into, uh, tech, but worked, uh, started off my career at a company called EDS.

Remember Electronic Data Systems, those things. Ross Perot, That's right. Was was there for about 10 years.

Uh, got the opportunity to do lots of different things in lots of different parts of the world. Really great, uh, sort of foundation for my career. Ended up HPE as part of the acquisition, moved to Cisco, um, ended up at NetApp.

I've been, been in NetApp actually 10 years, which is, you never go into these things thinking you're gonna be there for 10 years, loving it. And, and NetApp has, you know, has been such a, an organization that's transformed completely during the time I've been here. But it's, it's been super exciting.

So, yeah. Uh, lived all over the world, but now based in the us, uh, with family, et cetera, et cetera. Um, yeah, and, uh, look, I'm, um, I'm a product manager, but I have a tech background.

I also have a business background, obviously, with the, being an economist by trade. So, um, you know, I, uh, I always, you know, my, my role at NetApp has always been, and I've always been very focused on, on customer. So what does that mean?

Oh, see, everyone says that. But, but solutions at NetApp really, it, it's the glue that connects our product portfolio with, with customer use cases and customer outcomes. So I spent all of my time talking to customers, talking to partners, learning latest trends, working with analysts, um, and, and sort of designing the roadmap on what we're gonna do in terms of bringing our portfolio to help customers in a wide range of different areas.

AI being of course, probably the most interesting right now. Absolutely. Well, everybody's interested in ai, right?

Um, sure. Fantastic. What a great story, Russell.

Great journey. So, NetApp, everyone in our audience has heard of NetApp, unfortunately a lot, not a lot, but there's a good percentage of them who still think of NetApp as network attached storage, right? And, and, you know, that kind of thing.

And of course, NetApp has been so much more for so long, um, including right up, you know, the very current AI stuff. How would you describe today's NetApp, especially your there 10 years? Russell, how would you describe today's NetApp to our audience?

Yeah, it's a great question, Alan, and, and there's so many aspects to what NetApp does, but at its really core, we like to think of ourselves as an intelligent data infrastructure company. Now, that sounds like a marketing blur, but it actually isn't. It really talks about the value that we bring to, to, to, to organizations who are trying to make their data do something for them, right?

And we, we, we firmly believe that customers that are organizations that are sitting on essentially goldmines of data and, and we're, we're looking at new workloads like ai, where the fuel is the data. How do we make that data ready for these new workloads? Where is the data?

How do we bring it together? How do we operationalize it? How do we simplify it?

So NetApp, you know, made a bet, I think maybe 10 years ago, maybe a little bit more on, on cloud. Uh, we, we believed that the future was hybrid. Uh, we went about taking our, our class leading storage os ONTAP and, uh, OEMing it essentially to the hyperscalers.

And that turned out to be prescient. Essentially, what we did is we created these endpoints that allowed us to make it so seamless for organizations to manage their data wherever it needed to be managed. Uh, they didn't have to take it outside of their, uh, you know, their, their own governance domain, their own security domain, but they could make it available for all these new workloads.

And of course, that takes us right out to ai. I've been doing AI for about five years, and, you know, essentially all these bets that NetApp made are just perfect for the world of ai. We have, NetApp has about a hundred exabytes of data stored on us today, right?

Um, you know, and you know, our history has definitely been things like unstructured data, but if you look at, we've got a, we, we see a sort of plethora of different data types. It's unstructured, it's semi-structured, it's structured data, it's, it's file, it's block, it's everything you could imagine on the storage side. But again, the focus is really on the data manageability side.

So, yeah, you know, NetApp is not, you know, is not, you know, we, uh, you know, we started off in filers many, many, many years ago, but the company has gone through so many revolutions since then that you could hardly call us that anymore. Absolutely. I agree with you.

A hundred percent crazy. All right, let's, let's shift gears a little bit. Um, NetApp recently did something called the AI Space race survey.

Yeah, yeah. Tell us about it. Yeah, so it was, uh, it was report that we, we commissioned and what we wanted to do.

We, we, listen, we're we're at a really interesting juncture in, uh, in AI in particular. Um, you know, we, we've we're coming out of what I, I like to call the POC wave. And I know that's probably an overs simplistic view, but if you, if you think about what's been going on in organizations and the sorts of organizations that have availed themselves of ai, try to go down that path, um, so many of them have really been trying things out.

So few of those projects have made it into reality, right? But at the same time, what we see is an incredible interest at a nation state level for how important and critical a AI is gonna be to the relative competitiveness of these different economies across the globe, right? So, um, you know, as we've, obviously NetApp's been involved in AI for, you know, seven and a half years we've been building and helping customers in ai.

We wanted to go out beyond the confines of NetApp's installed base, but really to the industry as a whole and, and ask, you know, folks, uh, leaders in, in about, uh, in, in four different countries. That's the us, China, India, and the uk, which we thought was a fairly representative set of, of countries economies. So across both CEOs and IT execs to understand how do you perceive your own I AI readiness, how aligned are you within your organizations?

And, and, and really who thinks they're winning the AI race? Who's actually positioned to lead over these next five years? And trying to understand the role of, of, of, of government and policy in driving this, uh, this huge wave of innovation compared to, of course, the private sector who's obviously seeing a huge opportunity, but maybe thinking about things more through a financial lens.

So it, it, it is super interesting, and I hope we get into some of the details about what we learned. It really, the, the, some of the data we got back was intriguing. Well, that sounds like a setup, if I ever heard one.

That's great. Do tell, Do tell Russell. Yeah, Yeah, yeah, yeah, yeah.

So, so, I mean, I'll, I'll give you, I'll give you a sort of an interesting, one interesting example, this other data points we're gonna talk about, right? So, uh, you know, I talked about this idea of the CEOs and IT execs, and we asked both of them. And, and the reason for that is because, you know, the perception of the CEOs and the reality of the IT execs aren't necessarily the same thing, right?

And sure enough, that's exactly what we found. So in the US for example, both CEOs and IT leaders found themselves around the same percentage of readiness. So 61% of both CEOs and IT leaders in the US found themselves ready for ai.

Now, of course, you know, that's, that's their own perception, but, but it was, it was stark that both sets of these groups said 61%. About 61% said they were, they were ready. But then we looked to China, the, the, an extreme here, 92% of CEOs in China believed that they were rep or actually deploying ai.

They were deploying ai, but only 74% of the IT leaders believed they were deploying ai. So, just to be clear, CEOs believe that they all, essentially, 92%, the vast majority were deploying ai, were only actually less than three quarters actually, were actually doing that, that those deployments today. So, you know, I, I think that, you know, this is one of the things we we'll talk about as we go through it, you know, um, it turns out that this idea that, that that intelligent data infrastructure I mentioned, which is kind of scalable and it's secure data, actually turns out to be one of the biggest and most important, the most decisive factors in whether AI ambitions translate into lasting advantage.

And, and, and we, you know, you wouldn't think that, I've gotta tell you this one thing, Alan, you know, the, um, if you, if you go out and in the industry and you go to a bunch of folks, a bunch of customer organizations that haven't started the area AI journey yet, right? They're, they're just getting started. They've read what they've read, they think there's opportunity there, there's potentially money to go do, to go and do something.

Most of these folks think that the problem is a, what we call an accelerated compute problem, like GPUs, essentially, right? Mm-hmm. And, and for this, uh, so much credit to Nvidia for doing what they've done, right?

They have, they have made this all about GPUs, and God bless, that's a fantastic thing for them. And by the way, our GPUs are not insignificant part of the problem, right? But it's quite telling that, um, you know, in the last GTC, which is NVIDIA's public conference, what we heard was that their CEO Jensen talk about data for about 40, 45% of his time.

Just to think about that. This is a company that doesn't sell data solutions, right? They sell AI GPUs, but more importantly, they sell an incredibly rich framework and software stack to make AI real.

And they were talking about their data management partners. Why do we think that is? It's because About the data, what's Holding them back is the data, right?

Mm-hmm. That's how important it's become to them. So anyway, yeah.

We, we should dig into a, a few more things, uh, around the report, but I just thought I'd give you that background. Yeah, no, it is very interesting. Russell, a couple of thoughts on it.

It is, first of all, you know, the AI is as good as the LLM it was trained on, and the LLM is as good as the data that, that it's made up of, right? And, you know, the, the first L in LLM is large, Right? And, and the, the larger, you know, at some level, the larger the better.

Though there's also people saying, no, let's go for small language modules, right? That are more keyed in, into a very specific narrow field, if you will. But here's what I really find interesting.

Russell and I actually spoke about this last week on a LinkedIn post or video I put up, which is, I agree with your findings. Hmm. I think most companies have sort of, they're either dipping their toes in the water or talking about dipping their toes in the water, but here they are, and some of the biggest companies, you know, doing announcing layoffs and saying, oh, well, it's due to we're gonna, we're gonna replace some of these workers with ai.

We may very well replace some of these workers with ai, but not today. I, I don't think today, I'd be interested to see if your survey found that We, we, you know, that wasn't an area we, we, we focused on massively. But, but, but let me give you a couple of things that might help sort of talk about that to get into a little bit more detail.

So, I, one thing that was, I, I thought was really interesting was you, you've heard a lot about, um, particularly geopolitical tension where obviously that's very, you know, that that's a big topic for, for right now. And obviously there's been a lot of conversations about, you know, um, you know, what, GPUs, for example, accelerated computes gonna be a made available in which countries, et cetera, et cetera. So one thing that we, we found that was pretty interesting was that, um, you know, in terms of how people, how different countries rate themselves, again, looking at the two extremes, so in a, you know, asking who is going to win the AI race?

So we asked that question, who is gonna win the AI race? We asked these IT execs, we asked these CEOs, and two thirds of them in the US thought they were gonna win, right? Right.

But, but only about 40% in China thought they were gonna win win, right? And it, uh, that, that, that was, that we found that really interesting because we, we, we've actually seen is a huge drive in places like China towards very fast scaling of their, uh, operational capability. Actually, that's one of the questions we asked.

We asked, um, you know, what is the, what is the most important capability as you go and build that AI in China? 35% of their leaders ranked scalability as the top capability that they're going after versus less than a quarter globally. So, so what that tells us is that China has a focus on rapid deployment and early impact, whereas I think there's much more development going on in other places.

So that's places like the us, the UK and India, essentially the, they're taking a, a sort of a more integration approach as in how do we integrate this with existing systems rather than kind of get making some big bangs, uh, really early. And by the way, you know, that comment you made about whether it's gonna replace what AI is gonna replace workers, you know, what we've seen, and this wasn't necessarily directly in this report, but I think it was, was supported by this report, was that, um, very few of these, uh, productivity enhancements that we hear in ai, the ones that you would've heard more in the predictive and generative AI wave, actually delivered much in terms of real world savings for customers. It definitely gave people more time in the day.

It didn't necessarily mean they needed less workers. But that is changing. That is changing.

We're seeing AG agentic in particular being a huge driver of fundamental shifts in the, uh, the, the sort of ratio between capital and so a sort of physical capital and human capital, right? We, you know, so, so, so we are absolutely seeing that with the agentic, but AG agentic has a whole other range of challenges around it, and we're just really getting started on that. Excellent.

Let's dive a little deeper. Tell me some other findings in the survey that you found very interesting. Yeah.

Um, so one of the other things that we, we found pretty interestingly interesting was around the cloud. Um, so, so we, we, you know, NetApp has long since believed that AI is probably the most hybrid workload that we've ever seen as, as a, as an industry. And it, there's a lot of reasoning for that.

Uh, you know, it, it, the, the, the primary reason is that the amount of investment that's going on in the hyperscalers, their ability to, to, to provide quickly scalable, but also burstable workloads, but also the very rich, not just ias, but PAs and SaaS services that the hyperscalers have built. It means that, uh, cloud, um, it, the ability to seamlessly leverage cloud services as part of an overall AI workflow is becoming a, a, a significant, uh, uh, opportunity, uh, for customers. Now, um, what's interesting of course is that yes, we have the, the, the big hyperscalers, but they're American, right?

To be clear, they're American, right? And I just mentioned, uh, we, we, we, this, this, uh, survey covered four countries, right? Covered China, it covered India, it covered the uk, us now UK and us, probably UK doesn't have quite as much concern about using US cloud services.

But if you go to other places, obviously with some of the recent, uh, trade disputes that we've seen, we, we have seen a lot of nationalism come into this, you know, can I run this locally? And suddenly the over basically considered over alliance on, on US based cloud services is starting to become a problem. So we're starting to see nations, uh, uh, build their own cloud capabilities, not just rely No, Absolutely.

I, I see sovereignty is, is the buzzword of the day, and whether it's checked Or not, deeps, Alan, you saw deep seek, I mean, that was the Yeah. You know, that was the shot across the bow if there were, if ever was one, right? Sure.

Was. It was a Sputnik kind of moment, but Yes. Yeah.

You know, but it, this is not, I would posit that this is not just limited to, let's say China, you know, in the obvious economic tensions with the us given today's Balkanized world, and given the administration and and outlook here in the us, their are many, many countries looking to have sovereignty over their data, over their IT resources, over their infrastructure. And I think this is a huge, a huge driver of, is going to be a huge driver of, of budgets in, in the coming cycles. And I, I think the recent Iran, US Israel triangle of, of violent of war has, is only gonna drive that, accelerate it A a hundred, a hundred percent.

And I think that you obviously, you know, what, what the, what the survey was showing us was that it was a combination of different factors. One of it is one of those factors is absolutely not wanting to be over reliant on, on US services. And some of that's because will those services be, you know, will they be a lever that's used in trade negotiations?

Um, there, there's issues around, you know, just, just kind of, uh, where the money's flowing. But actually one of the biggest challenges isn't any of that. It's actually to do with the fact that you mentioned LLMs and you mentioned data and, and just predominantly English.

I mean, you know, all the big LLMs, all the lot of the investment was in, it was in English. And of course, you're going to countries where English isn't necessarily the first language or the only language, and suddenly you're seeing, uh, significant investments now from a range of different countries in, in, uh, including China in particular, but also India, which has a lot of regional, uh, um, obviously regional languages, uh, to, to build out LMS that can communicate in, in, in, in, in Native tongues. So, so that's another reason I think we think that's actually driving, uh, um, this investment.

But you know, that there is one thing I thought was, was super interesting. Um, the perception of, of most of these organizations is that they're ready. So, so this, I found this absolutely fascinating, right?

So 88% of the organizations that we surveyed felt that they were mostly or fully ready for ai, and we were just like, we were like blown away by that number, right? Because, you know, I, I, you know, that that is so far from what we are seeing in the real world. I'll, there's another number that's interesting about this.

The number of AI projects. We asked how many projects were failing, right? How many projects were failing.

And the, uh, what we found is that 79% of these organizations felt that they were lacking the risk mitigation and ethical practices required. So About the same number who said they were ready. Because I, and I think, I think again, it's back to, you know, do I have physical boxes that can run AI workloads versus do I have the environment necessary to actually run that, that those workloads, those work, and it, that, you know, the, it's all about, you know, having, ensuring that you have clean, secure, and auditable data pipelines that can feed those AI systems with trustworthy, traceable, bias aware data.

Because if you don't have that, I mean, to be clear that at a society level, the trust level for AI is, is, is not clear, right? That the society is still struggling with how much they're willing to trust ai. And the reality is, is you can only, you can only fault AI for doing something wrong when the data is correct and it makes a mistake.

But most of the time, that's not the issue. The issue is it's being fed bomb data and you get fed bad data, and that's all, you know, Then it's bad out Then it's bad out. So, so there is a, you know, I think as a, not just as, uh, as organizations, as, as Net obviously has a huge role to play in creating a data environment that does all those things really, really well, that's a huge focus for the company.

We can do that on a highway basis. So, so, so, so we love that, right? So whether it's data integrity or data lineage, trustworthy infrastructure, data infrastructure is a huge part of what we do and what we've been building, uh, honestly for, for the last, you know, uh, certainly since the inception of AI has been a huge, uh, focus for us.

But, um, I, I think that, um, in general, uh, society, um, you know, we're, we're right. We're walking this tightrope, and we, there's two ways this can go down. You know, we, we can really focus on making AI trustworthy or we can focus on speed.

I, I don't know that it's easy to do both. I think NetApp is probably your best bet to do both if you, if you wanted to go do both. But I think what we're seeing is, is that China's trying to get ahead, some of the other countries are taking a much more reserved position.

Regulation has a lot to do with this, right? So we're seeing the AI Act in the European Union come out, you know, uh, some folks that seem to think it's a data privacy thing. It really isn't.

It's really trying to explain what is acceptable in terms of the use and reliance on AI in, in society. I think it's a good thing. I, I, most folks tend to think of regulation as holding stuff back.

Um, I actually have a completely o opposite view of, of this, right? When I go out and talk to customers, I'll tell you this, Alan, when I go out and talk to customers, I hear one thing consistently, which is that lack of certainty about what is okay, what is acceptable, what will society Bear is actually holding organizations back. If society states through, through regulation, Hey, this is acceptable, and you've got clear lines, you know what, that gives me carte blanche.

I, I now understand what the context is. I don't have to second guess myself. As long as I know what those, the, those, uh, requirements are, I can stay within them.

So, so we're seeing a lot of that sort of, uh, I think that's a, a natural sort of next level of maturity that we're starting to see in the market. I love it. Hey Russell, we're, we're probably over time, but, um, for people who wanna maybe go have a look at the survey, is there a place on the NetApp website they can go get the, the, uh, survey results or report?

Yeah, I'm sure we'll add it as a, as a, as a link to the, uh, to, to, to this video? com where people can go, go read the, uh, report site, which I highly recommend. Um, it, it has got full of really interesting, really interesting data, but at the end of the day, if I was just to kind of, kind of wrap it up from my perspective, right?

So there, there are roadblocks to making AI impact meaningful, right? And, and most of it's relating to data, right? And, you know, if you can build a, a, a, a, an intelligent data infrastructure that manages that data, that also secures it, um, ensures it is trustworthy, makes it seamless OnPrem in the cloud, then you are in a really fantastic position to take advantage of whatever's coming at you.

Because that's the last thing I'll tell you, Alan, anyone that tells you they know what's gonna happen in AI in two years, no, no one knows. So all you can do at this point is take advantage of what's in front of you and build an environment that is adaptable and agile so that when things come at you, you are quick and able to take advantage of them before your competition is. So that, that would be my, my key recommendation to your, to your listeners, your viewers.

Um, uh, but yeah, go, go read the report. I think it's, it's really an interesting report and, uh, it's definitely worth your time. Absolutely.

Look, if you're like me and you don't like to go through the notes on the videos, you could probably just google NetApp's AI space race survey and get it it from there as well. I, I, I, most people just start on Google. Fair enough.

Yep. Russell, thank you so much for coming here on Text Drunk tv. Come back anytime and keep us posted.

All right, Thanks Alan. Appreciate it. All right.

Good luck. Russell Fishman, senior director, global Head of Solutions product Management at NetApp here on Tech Drunk tv. We'll take a break.

We'll be back in a moment.