Unleashing Genius
Unleashing Genius is a series hosted by NetApp Chief Marketing Officer Gabie Boko, where executives, innovators, and industry leaders explore what it truly means to unlock human and organizational potential in a world being reshaped by data.
Each episode explores how visionaries turn big ideas into systems and workflows that perform at scale. Guests share what it takes to keep work moving when everything is more distributed and data-dependent, and how the right data infrastructure makes the difference between possibility and execution.
Host: Gabie Boko
Produced By: Kenya Hayes
Unleashing Genius
The Infrastructure That Reaches Everyone
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In this episode of Unleashing Genius, NetApp CMO Gabie Boko sits down with Rob Tiffany, Research Director at IDC and author of Saving the Earth with the Internet of Things, to discuss how technologies originally built for the world's largest organizations can help solve real-world problems in communities, NGOs, agriculture, environmental monitoring, and beyond.
The discussion explores why simplicity often beats complexity, how organizations can avoid carrying old processes into new technology eras, and why ownership, automation, and practical execution matter more than theoretical possibilities. From flood monitoring and water management to AI adoption and infrastructure design, Rob shares lessons from a career spent building technology at global scale and applying those same ideas to challenges that affect people everywhere.
The result is a conversation about turning ideas into action and ensuring technology creates value for the people it is intended to serve.
Host: Gabie Boko
Produced By: Kenya Hayes
Welcome to Unleashing Genius. I'm Gaby Boko, the Chief Marketing Officer at NetApp. And for those of you who've been with us before and those of you who haven't, this is a series, it's a set of executive conversations about what's changing across the any industry is AI, data, and shifting business priorities are reshaping how organizations are running. In each episode, we're exploring how we're looking at patterns that leaders are running into right now, where some of that pressure is building, and what assumptions are breaking because of it. But most importantly, what it takes to respond in a way that holds up over time. What leaders should learn and what they should do differently as these shifts accelerate. So today, I'm really excited about today because we're looking at what happens when infrastructure built for the world's largest companies gets small enough to serve everybody else. Most people talk about the AI era focusing on big deployments inside major companies, but there is a very different test happening at the edge of the market. And these are places without a dedicated IT team or a big budget. I am joined by Rob Tiffany. He's the research director at IDC and he wrote Saving the Earth with the Internet of Things. He's worked on platforms for companies like Microsoft, Hitachi, and Ericsson, all big companies. But this book, he takes those same lessons and applies them at scale to reaches larger communities, NGOs, and individuals who've never had access to enterprise grade tools. So, Rob, this is exciting for me. Thank you so much for joining us.
SPEAKER_00Thanks for having me. It's great to be here. This is good.
SPEAKER_01Good. I'm glad. So listen, let's not let's not dilly-dally, right? You have actually spent your career, as I mentioned up at the top, building infrastructure for some of the world's biggest companies. What made you want to look at it kind of shrinking down and making it accessible?
SPEAKER_00Yeah, you know, um, you're right. This is a far cry from designing Azure IoT at Microsoft. Maybe a little. Yeah, just a little bit. Um, you know what? It's it's uh I think it's more the problem to be solved than the technology, to be honest with you. Uh after doing all this IoT and AI and analytics and all those things for all these years, um, for commercial purposes, I think over time I started to notice that a lot of some of society's and the world's biggest problems actually not 100% solved, could be partially solved with some of these technologies. Um and somewhere along the way, I found out that sometimes the simplest things and the simplest analytics actually can move the needle on actually the very hardest problems in society. Um, you know, in big tech companies, we spend all our time with huge budgets building the most advanced AI technologies and everything to run on factory floors or whatever. Um, and then, but I think money is the big uh economics kind of overrides everything in this world. And so um, you know, I found myself on a panel discussion when I was leading IoT at Ericsson um over in Sweden, and I remember being on a panel discussion. This is like 2018, 2019, uh, at their offices in Santa Clara. And I was like, hey Rob, you're gonna be on this panel with the top sustainability experts in the whole world, and you're leading IoT for us, and you need to tell us how IoT can play a role with that. And I really freaked out and scrambled, trying to, how in the world is that gonna happen? How do I put that? Yeah. And so I'm like studying and everything to try to get ready. Uh, and then, and then you know, you you do the panel discussion, it worked out well, and then you go, oh wow, you know, there's some of these big societal problems that everybody's not thinking about day to day. They're all at work and doing their thing. And um, and so I think it grew from there. Um, before I knew it, I was doing keynotes and more stuff on that and kind of adding to it. Um I got to do some really cool Internet of Things stuff on farms. Uh, cool. Which was great. It was good to, it was refreshing to get out of the big city and hang out with farmers and uh and have the the look of delight when they could look at a like a wireless iPad and go, oh, my farm's talking to me and everything and helping them, you know, when we started getting worried about water issues on farms like the Colorado River running out of gas, kind of near Yuma, right? Yeah, goes into Mexico, yeah. Yeah, yeah. Um, and so things like that. And so, yeah, you it really hits you. And so uh over time, if you'd ever heard of the United Nations sustainable development goals, and I know not everybody has.
SPEAKER_01Not everybody has. Uh honestly, I'm sure I've heard of it, but I wouldn't be able to reiterate it.
SPEAKER_00Yeah, it was something all the countries signed on to back in 2015. Right. They made they produced this really cool video. You should go check it out. It's like all these movie stars and rock singers talking about how we're gonna save the planet, hunger, poverty, climate, everything. Um, and so yeah, it got started. For me, it helped make create categories. Um, I was like, okay, I figured out how IT can help maybe with hunger and agriculture and you know, stuff like that, or in water or clean water, things like that. And so kind of categorizing over time and saying, well, does this use case fit in this? Like, you don't want to pound a square peg in a round hole with technology. And so it just grew from there. And so lots of keynotes, lots of panels, lots of PowerPoint decks. And over time, I was like, I have got to find a way to put all those into some big book. Um, and so yeah, it's been a book. I know it's the kind of book like if you go to the university bookstore, this is kind of what you would expect to see, right?
SPEAKER_01But that's that's actually the thing, right? So you you basically discovered that like what we all have been saying, we always say it when we're in a big company or even in tech, right? We want to give back, we want to figure out how to do things at a local level. We want to make sure that what we're contributing to is is is human at the end of the day. Um and you do that by maybe designing what you're saying, flipping the switch and designing it small, maybe even from the start. Right.
SPEAKER_00Absolutely. Absolutely.
SPEAKER_01So um my world, we talk a lot about things like unified storage and you know, we get we get locked up in in in big conversations around data centers and um what that looks like in a large company is you know, like what where are you using your storage? What do you need it for? How is your data moving and flowing? But when you're a farmer, let's use your your growing iPad thing, um, what does something like unified data storage look like when you are let's say a big company or a farmer?
SPEAKER_00No, it's totally different. Obviously, you hear about edge computing, you hear about kind of doing the right solution for the right job, you know. Um obviously all of us, like when when I was living and breathing Azure, it was like, well, of course, everything's going to the cloud and we're gonna store it there. And we still do that. Um but multi-cloud, of course. Well, absolutely. Yes, I'm all into multi-cloud these days. Um and so it will it's it's whatever makes sense. And you know, sometimes the storage, you know, there's steps along the way. I may have an IoT device in an apple orchard and it's doing soil moisture to automate an irrigation system and maybe uh also soil nutrients so that you're not overusing too much fertilizer or things like that. And so the device may have a little bit of flash storage on it, right? You know, like in telecom, that whole store and forward kind of thing to make sure you don't lose data along the way. Because you never because the biggest X factor in IoT is connectivity, actually. Um, and so making sure you can get off of there. And so then whether is it cellular, is it LoRaWAN, it's all these, you know, Bluetooth, those kind of technologies, making a hop to another place. Um with farmers, I ended up I did, you know, I built a technology and did use it in Azure actually. Um, because you you can imagine that unified storage and unified global access, right, to things, you know, so that I can view it anywhere in the world or anywhere on my farm, you know.
SPEAKER_01Yeah.
SPEAKER_00And so things like that uh is a big deal.
SPEAKER_01So it basically means I think what I'm hearing you saying, c correct me if I'm wrong, is that what what it really is is about the flex and the ability to move. Um, it doesn't mean that it's uh it doesn't mean that it's identical for everybody. It means that it's flexible for the need and the moment and the time, right? That's what you're saying.
SPEAKER_00Absolutely. Absolutely. Yeah, like when I was doing, when I was at Hitachi building industrial IoT and digital twin systems, yeah, we would roll a whole rack into onto a factory floor. Um, you know, and because that's what made sense for them and the storage right there, you know. You know, I think my first light bulb, when I remember we started talking about edge computing and what in the world, and it wasn't because it was some cool new idea, it was actually a pragmatic thing. Um, I absolutely remember it after we just launched Azure IoT and I'm over at Boeing, just north of Seattle, talking to these guys and about the machine learning and streaming analytics and all this great stuff. And they're like, Yeah, that sounds great, Rob. Show me the version of that that runs right here on our factory floor because we have machines that build aircraft and they're spitting out terabytes of data per hour, and I need millisecond response times, and I don't have the time or the money to send it to your distant data center along the Columbia River and come back. And so you're like, okay, I guess we need to, I guess not everything goes to the cloud right away or ever, you know?
SPEAKER_01You know, but you're describing is simplicity, right? You're looking at the thing that makes you scale is simplicity. That's I love that.
SPEAKER_00Absolutely.
SPEAKER_01Absolutely. So let's get back to your book. Your book is actually full of examples where let's say a small sensor network turns into a real early warning system. Yes. Describe that for me, but then maybe apply it to how does what has to be true of the data behind that to make that work?
SPEAKER_00Right. Absolutely. So you can imagine, uh, and boy, I th I I think about that flood they had along the Guadalupe River in Texas with the summer camps. You know. And so what is early warning on a flood? We all know that we have things called flood gauges and sensors to measure the height of a river, for instance. We also have sensors to measure the how rapid the flow of the water is. And so you probably better be checking that maybe every second or every minute, you know. Uh, because like on a farm, there might be things that are good enough once an hour, or some things it's once a day. And so each each use case is tuned right differently to how soon you need that responsiveness. And so for f flooding, yeah, you're checking it rapidly. Um, and then yeah, you're sending that little, and you know what, it's not a lot of data. Obviously, you're just sending a little blip of information, a few bytes. And so you might be sending that once a second or once a minute, probably is fine. Um, and it may go to like if you're using, you know, when I think about the book and I think about all these real world problems, I I certainly keep coming back to cost. Uh, because all the people who are out there doing it, whether it's NGOs or just people trying to do some great work in their community, you know, they don't have tons of cash like they would at a big company. And so there's technologies like this LoRaWAN that's almost free, you know, to send little tiny bites of data. And so it may go to a nearby server or something, gateway, and it might pass it on to a cloud. Um, the other thing I think about a lot of times when we talk about data, we usually think about insights from data. That's very common. And we're gonna derive insights from this data using AI, some machine learning, or whatever, or simple analytics. But I'm just as big a fan of using data for automation. Um, in fact, I have to say I'm actually a bigger fan of automation than I am insights, which sounds horrific.
SPEAKER_01Um, I I want to hear more about that. Do why? In the balance of in the balance of let's maybe say the cost factor that you've already raised. And is that is that what's driving your favoritism to automation?
SPEAKER_00Absolutely. Absolutely. People want people spend money or spend time doing a commercial solution or something like this. And a lot of times I get feedback from them, like, that's great. You gave us a bunch of dashboards and we can see the data and everything, and then we're gonna make a decision. And sometimes that's appropriate. And a lot of people do want that visibility. Right. But what they really want is I want your thing that I just paid a lot of money for to just do the thing. You know, when the soil moisture gets low on the farm, I want you to automatically activate the irrigation system. And then when we're back in the green zone, turn it off. Just simple things like that. And so anytime you can do automation, and guess what? You don't need AI for all this stuff. There's only a few things that you actually need AI for. The simplest branching logic actually sometimes will get the job done. Um and I know it's a buzzkill. All we I know we're talking about AI all the time these days, but when I wrote the book, I went out of my way to make sure because if I'm gonna hand this to people who are working in a village in Africa for a water pump to keep water going for the village, I don't want them to have to know be techies or no AI or things like that. And so it's purposely designed so that I can give it to anybody almost.
SPEAKER_01You know what though? I I think that that's a fair point, though. And I think that sometimes we lose that when you're solving a small life or death problem. I don't know if life or death problems are small, right? But right, but water and flooding, those all seem like very real problems. Um sometimes simplest is best. And automation is the answer, but really looking at the flow. Sometimes we might in a larger company or or in a larger kind of data set look to over-engineer something. And what you're basically saying is is that you know, good sensors with bad data are just as costly, right? So, you know, think like the farmer, right? Go solve the small problem and solve it smart.
SPEAKER_00Absolutely. Right? Solve it smart. Absolutely, solve it smart. Don't over-engineer things. Um, you know, you're right. We always say keep it simple all the time, and you really should. And it but and so it's ironic that the hardest things in society actually can be solved with some of the simplest analytics you can imagine. Um, so maybe I throw in a little machine learning, like if I'm I've got use cases, life on land use cases where we're you and some of these you've heard a lot of these use cases before. Deforestation in the Amazon. What am I listening for? I've got I'm listening for a chainsaw somewhere in a particular geofenced area. Right. And I probably have a little tiny uh machine learning model um that knows what that sound sounds like. And so, but a lot of times those are pre-built and you don't have to actually know how it works. And and it's amazing what we can do even on little edge devices. Uh, this whole, you know, there's a whole edge AI whole thing going on, a tiny ML where you can put machine learning on little tiny devices. Um, or likewise, uh if it's uh biodiversity or an endangered species in an area, you might be listening for a gun or something like that, you know, that kind of thing. Or the engine sound of a truck coming into an area where they're not supposed to be. Um, you know, IoT is all about measuring. It's really it is. Whether it's use cases measuring the thickness of an ice sheet on glaciers that are getting smaller or sea level rise or mangroves or whatever, it's about measuring, it's about sending those measurements somewhere and it's applying some analytics to it to derive an insight and if and if possible, do automation to just take care of the problem, if that makes sense.
SPEAKER_01I mean, you this now that you're speaking my language, because this is everybody always wants to know like why do you love your job? And and I'm because you don't really say like, I love data storage. I mean, it just wakes me up in the morning. But you know what I do love is the application of it in this kind of environment is it can be big and life-changing, it can be small and life-changing, it can be world-changing. I think that that's really I think that that's what your book speaks to, and I think that that's what we're seeing here. Hey, I haven't got a got an like uh maybe a squirrel question. It might not be a squirrel question, because we are talking a lot about data, and especially when you're when you are taking in like flood data or when you're taking in farm data or like the data for the Amazon or whatever, right? Who owns that? Like is when the user is like a farmer or like an NGO instead of a large enterprise, where who controls that data that's getting collected?
SPEAKER_00You know, it it stays with the people, the constituents, the people you're going to help.
SPEAKER_01Interesting. Okay.
SPEAKER_00For the yes, it's not coming back to the ranch. It's not going back to HQ or or whoever's doing the work, it's not going back to them. Um now, is that asking for people to be a little more self-sufficient on this stuff a bit? Sure, it is. Um, but if yeah, if it's uh if it's borehole pumps that are pulling out well water for a village somewhere uh for people to drink or to keep their crops going or whatever like that, absolutely it's on them. And so you can imagine that kind of scenario, that's a local deal. Um, they may not be able to reach a cloud, actually, potentially in that area, depending on connectivity, right? Right, right. And so could be a little box, a little PC, a little server, something there powered by whatever intermittent power, solar. Uh, but the data resides with the people who are benefiting from the solution. They they they are the owners of the data in this case.
SPEAKER_01I think that that's a big heady question in this era of AI, right? I think that it's also as we as we talk about what let's call it the economics of data, right? It's it's not tokens, it's not cost, it's literally who owns the data. And I I love how you've just equated it to the the people, right? They ha it has to stay with the people whose data it is, or otherwise they're dependent, right? They're not actually doing anything with it. They're so you need they need to own the data and they need to have infrastructure that is something they can actually run themselves, right? So their data is their currency at that point.
SPEAKER_00That's exactly right. And so when you think about stuff that they own and they have to manage it and all that kind of stuff, the whole point of there's literally a hundred use cases. It took forever to do this across 11 of the categories of the sustainable development goals. And so trying to simplify that, it's you know, every use case, whatever it is, it has to have a valid outcome that's helpful. Um, and they're all there are no one-off scenarios. They all, the pattern is exactly the same. For this use case, I'm gonna use these sensors, this device, this connectivity, this kind of power, this kind of enclosure around the device, the simple analytics. We all and we probably people make fun of us for talking about KPIs uh in this AI world. Um so uh the whole book is designed around green, yellow, red. And I try to, you know, because you this the book is made for everyone, not techies. And so they understand street lights and stuff like that. And so green is good, yellow is kind of caution, we're heading in the wrong direction, and red's bad. Most companies ran off of KPIs for decades. Uh, and actually, spacecraft going to the move the moon did that too. You know, I've got green lights across the board, right? And so, and so the way the whole system works, the way that built the analytics is data comes in and you measure it, you do pattern matching, which is what we do in AI. Um, you know, I'm a total digital twin person, and so you model the thing because a digital twin is like a data structure that lives in storage. Of course. And you and so you model what's coming at it, um, you know, the properties of this thing, and I come in there and uh and then I have KPIs against all those kind of properties, you know, like soil moisture or whatever. Is your right front tire on your car low on PSI or something? And so you measure green is good, don't do anything. I don't need to need to see it, I don't need to care about it. Cautions going in the wrong direction in red. And so the whole every all hundred use cases do pattern matching, assign it a color, and then each color for the use case is assigned an automation. And so it's like if you get this, it does this. If you get red, you're gonna do this, you know.
SPEAKER_01Simple is best.
SPEAKER_00Yeah. Yeah.
SPEAKER_01Listen, if we've been talking about the data and and with the red, yellow, green, do you build security into that? Like clearly, I mean, I don't think that they're gonna have a security team like you do in a big company. How do you how do they make sure it's actually protected?
SPEAKER_00I can't depend on them to make sure it's protected, right? That's okay. So and so kind of like what we've done over the years is you know, you're doing security, you know, obviously it's a cliche, you know, encrypted data at rest and in transit and all that kind of stuff. So the the device side, you know, the way all these sensors work, you uniquely identify each device. Device out there. Um, and then they have their own little security ID, a security token that says this is who I am when I'm sending data. Uh, we can actually rotate those the same way, you know, how employees are annoyed when they're had to change their password once a quarter or whatever it is. Um, it works the same way with these devices. The platform actually will tell it, I need you to keep updating your security token over time. Keep the bad guys guessing, right? And so on the device itself, you can imagine just some Arduino or some little device and the storage there. It's encrypted there on its first little jaunt. Um, the password, the the security token and who it is is, you know, maybe base 64 or whatever. It goes over. Obviously, you're using TLS as you're going over the internet, right? When your platform catches the data, it authenticates it. Who are you? Oh, yeah, I see. I know who you are, and here's your security token, you're good. And then, you know, you're putting it into a database or whatever, and all that's encrypted at rest. So every step along the way, you're encrypting things, you're changing things all the time to make sure you're in good shape. Um, because yeah, the the bad guys are never sleeping.
SPEAKER_01Well, and I think what you're saying is that it's easier to build it into the infrastructure or to the sensor or to the security is something that can't just be attack on. We all know that at an enterprise level, that you can't just have something extra uh around it, right? You actually have to build it into the structure of the system itself. So yeah, right. It's like you you and especially in a small environment, right? Or with a small person, that's probably gonna be they're dependent on that.
SPEAKER_00Right. Because we can't send all those people in the village to training on they're certainly not getting a security stuff.
SPEAKER_01That's correct. Right. I agree with that. Okay, so here's a here's uh here's an interesting question then. Um what did doing all of this work teach you about enterprise, like what worked and didn't work in an enterprise? So we've been talking about the other directions, so large going to small, um, and what it takes to kind of simplify and run that, but what did it teach you specifically about going backwards and going back into enterprise work?
SPEAKER_00It taught me that people overthink things.
SPEAKER_01Did you need to do all of this work to know that though? Did you know that instinctively?
SPEAKER_00People spend too much time overthinking everything in the enterprise and they never get anything done. And and so, you know, I we don't want to talk about theory and here's how you know, it's like do the thing, action, act, you know, actionable, get stuff done, roll up your sleeves and get stuff done. You know, you think about all the great inventions, all the amazing things that's happened in history, people just rolled up their sleeves and they just did it. And so when I see so many things in the enterprise, so many meetings, so many, you know, get-togethers, you know, where we're pontificating and whatever forever, um that that was my big takeaway, you know. And so the whole world has different needs, individuals have needs, and then the enterprise has needs. And um, and I'm seeing this right now with AI, you know, I do the research uh at IDC on stuff going on and all the big companies like that. And it's like there's there's certainly people every time there's a new technology, we're gonna spin up a new COE, you know, and we're gonna spin up this group, and we're gonna have a working group, and we're all gonna spend six months talking about it.
SPEAKER_01It's a tiger team. Come on, Rob, it's a tech team.
SPEAKER_00I definitely know about tiger teams. Absolutely, yeah. And and people do that. Um I I think like AI, sit back and go, you know, there's lots of uncertainty, but there's some things we really do know about AI. Uh, you know, and when I say that, everybody generative AI. Um, there's things you know that it really does well, and maybe you should focus on the things that it does well that you know for sure. You know, when you talk hear people talk about first principles, what is it, something I really know that works? I know that AI can summarize things, okay? Not rocket science. I know AI can write really good code, okay. There's a few things that we feel pretty concrete about, and all the other stuff will get figured out over time. But I know a lot of people say we can't get started until we figure out this really hard thing out here or how it's gonna be 10 years from now. And we won't get started. And it's like, no, don't wait. Get get after it, you know? Get um don't go crazy.
SPEAKER_01But I think you also talk about not carrying bad habits with you, right? Because Absolutely. That's a that's a thing that I think, and I wouldn't even say in the enterprise world, maybe it's not about bad habits, but it's um don't carry old process to solve new problems.
SPEAKER_00Absolutely. Absolutely. People people do that all the time. I mean, think about just the whole digitization, digitalization, all these things that all started with somebody doing something manual on paper and pencil and all that kind of stuff. And then when and then what did they do? When they digitized that, they just made they used the exact same process but made it digital. Um and and it but you know what? I don't want to beat up on those people. There was actual real value in doing the same, maybe not great process on digital because it actually did speed it up. Then you hear digitalization or other things where they go, well, now it's now that it's digital, is there a better way to do this? Or should we have ever done this thing in the first place, or whatever? Stuff like that. Um, and so you know, it's a process. That's so true. It's a process. And you know, and again, you you meet all kinds of people. There's all kinds of most companies are small businesses, they're not giant enterprises. Gen enterprises, that's all we think about, but they're actually the smallest group of businesses in the world. And so you have lots of regular good folks trying to do good work and and do good for their families and their employees. And so you gotta start somewhere, right? Yeah. Um, and but you're right, it's good to let go of those bad habits. Gosh, that takes you back to remember when consulting firms in the 90s were doing like business process reorder engineering kind of things.
SPEAKER_01I don't want to use their names out loud. I'm afraid that they're worried for their jobs right now.
SPEAKER_00But um Yeah, I think I know exactly who you're talking about.
SPEAKER_01Yeah, exactly. Exactly.
SPEAKER_00It was a thing.
SPEAKER_01Hey, listen, last question.
SPEAKER_00Sure.
SPEAKER_01Because you've been on a journey with this, and I I I just am so appreciative of the journey. It's something it makes it feel real, right? It makes it feel centered and like like there's a a real need for what we're doing if we can think outside of our our day-to-day and apply it to the world. But that's what I took away from from you, your book, the conversation. But what do you peep what do you hope people take away from the book and from just even this conversation?
SPEAKER_00Yeah. The book is to empower people. And the book is people talk about things like climate or that or whatever the problems they might have in their world or the community, and they and when they're big ones, like you saw with these sustainable, they're big. And you know what happens is a lot of people assume someone else is gonna take care of it, or the govern or the government's gonna take care of it. Right. Right. You know, this mountain's too good for me to climb. I couldn't possibly. Uh, and that's common and everything. And I just remember people being excited early on, and then it's kind of faded. And so it's like, okay, well, I'm gonna write this book. This is a handbook. This is your instruction manual. It's not theory, it's actually go do the thing. Every every use case is do the thing. Here's a shopping list, even to get your shopping cart full of stuff to go do it. And so it's actionable. Actually, it's also while it the reason it looks like a textbook, it's also a textbook. There's questions and answers. Uh, professors, professors could use it for curriculum. Uh high school teachers could use this for classroom projects. Uh, university students could do this for like their capstone project. Um, sometimes, you know, when you feel jaded about things and you think, well, gosh, we're almost to 2030, which was the deadline for all these goals, and you're kind of worried that we really aren't going to hit any of them. And maybe we didn't get it done. Maybe the younger people will be the ones who get it done. I don't know. Um, I want to make it and so I wanted to make sure that school teachers in classrooms could give this to their kids and they could do classroom projects and get kids excited about things like this.
SPEAKER_01I I absolutely love that. And thank you so much for that. I mean, honestly, Rob, this is a great conversation. Uh, this is about, like I said, unleashing genius is about solving real-world problems. It's not just about the technology. And this conversation has been a huge reminder that infrastructure only earns that name if it can go wherever the people who need it are, right? It's not just about, it's not just about who spends the money. So thank you. Thank you so much for today. Um, that's is as I said, that's the this whole thing is ownership is easy to skip and hard to walk back later. If the data doesn't stay with the people using it, like you said, then the tech really isn't serving them. And honestly, that's I think the through line for this whole series that I'm doing is really turning possibility into execution. Um, and and something that makes infrastructure hold up everywhere it works, not just where it's designed for. So, Rob, thank you again for joining us. Rob Tiffany from IDC and everybody listening, thank you for joining us. Thank you for turning into Unleashing Genius again. If you'd like to continue the conversation, I encourage you to join us at NetApp Insight 2026, where the technology leaders, customers, and partners come together to explore what's next across AI, data, cloud, and cyber resilience. Just a small plug for you. But to learn more, please visit netapp.com forward slash insight. But outside of that, thank you for joining Rob and myself on this episode of Unleashing Genius.