 From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. Welcome to this CUBE Conversation. My name is Dave Vellante. I'm one of the co-hosts of theCUBE and we're going to have a conversation to really try to explore, does infrastructure matter? You hear a lot today, oh, ever since I've been in this business, I've heard, oh, infrastructure is dead, hardware is dead, but we're going to explore that premise. And with me is Randy Arsenault and Steve Keniston. They're both global market development execs at IBM guys. Thanks for coming in and let's riff on this topic. So here's what I want to do. I want to start with data. We were just recently at the MIT chief data officer event 10 years ago that role didn't even exist. Now data is everything. So I want to start off with, you hear this bromide data is the new oil. And we've said, you know what? Data actually is more valuable than oil. Oil I can put in my car, I can put in my house, I can't put it in both. Data is, it doesn't follow the laws of scarcity. I can use the same data multiple times and I can copy it and I can find new value. I can cut costs, I can raise revenue. So data in some respects is more valuable. What do you think, Randy? Yeah, I would agree. And I think it's also to your point kind of a renewable resource, right? So data has the ability to be reused regularly to be repurposed. So I would take it even further. We've been talking a lot lately about this whole concept that data is really evolving into its own tier. So if you think about a traditional infrastructure model where you've got sort of compute a network and applications and workloads and on the edge you've got various consumers and producers of that data. The data itself as those pieces have evolved the data has been evolving as well. It's becoming more complicated. It's becoming certainly larger and more voluminous. It's better instrumented. It carries much more metadata. It's typically more proximal with code and compute. So the data itself is evolving into its own tier in a sense. So we believe that we want to treat data as a tier. We want to manage it, protect it, wrap the services around it that enable it to reach its maximum potential in a sense. So guys, we want to make this interactive in a way and I'd love to give you my opinions as well if you guys are okay with that. But so I want to make an observation, Steve. If you take a look at the top five companies in terms of market cap in the U.S. Apple, Google, Facebook, Amazon, and of course Microsoft, which is now over a trillion dollars, they're all data companies. They've surpassed the banks, the insurance companies, the exon mobiles of the world as the most valuable companies in the world. What are your thoughts on that? Why is that? I think it's interesting but I think it goes back to your original statement about data being the new oil. And unlike oil, you said you can put it in the house but you can't put it in your car, you also want it to burn, it's gone. But with data, you have it around, you generate more of it, you keep using it and the more you use it and the more value you get out of it, the more value the company gets out of it. And so the reason why they continue to grow in values is because they continue to collect data, they continue to leverage that data for intelligent purposes to make user experiences better, their business better, to be able to go faster, to be able to do new things faster. It's all part of this growth. So data is one of the superpowers. The other superpower of course is machine intelligence or whatever he talks about as AI. You know it used to be that processing power doubling every 18 months was what drove innovation in the industry. Today it's a combination of data which we have a lot of. It's AI and cloud for scale and we're going to talk about cloud but I want to spend a minute talking about AI. When I first came into this business, AI was all the rage but we didn't have the amount of data that we had today. We didn't have the processing power. It was too expensive to store all this data. That's all changed. So now we have this emerging machine intelligence layer being used for a lot of different things but it's sort of sitting on top of all these workloads that's being injected into databases and applications. It's being used to detect fraud, to sell us more stuff in real time, to save lives and we're going to talk about that but it's one of these superpowers that really needs new hardware architectures. So I want to explore machine intelligence a little bit. It really is a game changer, isn't it? It really is and tying back to the first point about sort of the evolution of data and the importance of data. Things like machine learning and adaptive infrastructure and cognitive infrastructure have driven to your point a hard requirement to adapt and improve the infrastructure upon which that lives and runs and operates and moves and breathes. So we always had hardware evolution or development or improvements in networks and the basic components of the infrastructure being driven again by advances in material science and silicon, et cetera. Well now what's happening is the growth and importance and dynamicity of data is far outpacing the ability of the physical sciences to keep pace, that's a reality that we live in. So therefore things like cognitive computing, machine learning, AI are kind of bridging the gap almost between the limitations we're bumping up against in physical infrastructure and the immense unlocked potential of data. So that intermediary is really where this phenomenon of AI and machine learning and deep learning is happening and you're also correct in pointing out that it's everywhere. I mean it's imbuing every single workload, it's transforming every industry at a fairly blistering pace. IBM's been front and center around artificial intelligence and cognitive computing since the beginning. We have a really interesting perspective on it and I think we bring that to a lot of the solutions that we offer as well. Ginny Rametti of a couple of years ago actually used the term incumbent disruptors and when I think of that I think about artificial intelligence and I think about companies like the ones I mentioned before that are very valuable, they have data at their core, most incumbents don't. They have data all over the place, they might have a bottling plant at the core, the manufacturing plant or some human process at the core. So to close that gap, artificial intelligence from the incumbent standpoint is they're going to buy that from companies like IBM. They're going to procure Watson or other AI tools or maybe use open source AI tools but they're going to then figure out how to apply those to their business to do whatever, fraud detection or recommendation engines or maybe even improve security and we're going to talk about this in detail but Steve, there's got to be new infrastructure behind that. We can't run these new workloads on infrastructure that was designed 30, 40 years ago. Exactly, I mean I think, I am truly fascinated by with this growth of data is now getting more exponential and we think about why is it getting more exponential? It's getting more exponential because the ease at which you can actually now take advantage of that data is going beyond the big financial services companies, the big healthcare companies, right? We're moving further and further and further towards the edge where people like you and I and Rainey and I have talked about the maker economy, right? I want to be able to go in and build something on my own and then deliver it to either as a service, as a person, a new application or as a service to my infrastructure team to go then turn it on and make something out of that. That infrastructure, it's got to come down in cost but all the things that you said before, performance, reliability, speed to get there, intelligence about data movement, how do we get smarter about those things? All of the underlying ways we used to think about how we manage, protect, secure that it all has evolved and it's continuing to evolve. Everybody talks about the journey, the journey to cloud, why does that matter? It's not just the cloud, it's also the componentry underneath and it's got to go much broader, much bigger, much faster. Well, and I would just add, just to amplify what Steve said about this whole maker movement. One of the other pressures that that's putting on corporate IT is it's driving, essentially driving product development and innovation out to the very edge, to the end user level. So you have all these very smart people who are developing these amazing new services and applications and workloads. When it gets to the point where they believe it can add value to the business, they then hand it off to IT who is tasked with figuring out how to implement it, scale it, protect it, secure it, et cetera. That's really where I believe IBM plays a key role or where we can play a key role and add a lot of value is we understand that process of taking that from inception to scale and implementation in a secure enterprise way. And I want to come back to that. So we talked about data as one of the superpowers and AI and the third one is cloud. So again, it used to be processor speed. Now it's data plus AI and cloud. Why is cloud important? Because cloud enables scale. There's so much innovation going on in cloud. But I want to talk about cloud 1.0 versus cloud 2.0. IBM talks about the new era of cloud. So what was cloud 1.0? It was largely lift and shift. It was taking a lot of crap locations and putting them in the public cloud. It was a lot of test and dev, a lot of startups who said, hey, I don't need to have IT like us, like theCUBE. We have no IT. So it's great for small companies. A great way to experiment and fail fast and pay for, you know, by the drink. That was 1.0. Cloud 2.0 is emerging as different. It's hybrid, it's multi-cloud, it's massively distributed systems, distributed data on-prem in many, many clouds. And it's a whole new way of looking at infrastructure and systems design. So as Steve, as you and I have talked about, it's programmable, so it's the API economy. Very low latency. We're going to talk more about what that means. But that concept of shipping code to data, wherever it lives, and making that cloud experience across the entire infrastructure, no matter whether it's on-prem or in cloud A, B, or C. It's a complicated problem. It really is. And when you think about the fact that, you know, the big challenge we started to run into when we were talking about cloud 1.0 was shadow IT, right? So folks really wanted to be able to move faster and they were taking data and they were actually copying it to these different locations to be able to use it for them simply and easily. Well, once you broke that mold, you started getting away from the security and the corporate governance that was required to make sure that the business was safe, right? But following the rules slowed business down, so this is why they continued to do it. In cloud 2.0, and I like the way you position this, right, is the fact that I no longer want to move data around. Moving data within the infrastructure is the most expensive thing to do in the data center. So if I can move code to where I need to be able to work on it, to get my answers, to do my AI, to do my intelligent learning, that all of a sudden brings a lot more value and a lot more speed and time is money, right? If I can get it done faster, I get more value out of it. And just, you know, people often talk about moving data, but you're right on. The last thing you want to do is move data and just think about how long it takes to back up. The first time you ever backed up your iPhone or how long it took, well, and that's relatively small compared to all the data in a data center. There's another subtext here from a standpoint of cloud 2.0 and it involves the edge. The edge is this kind of new thing. And we have a belief inside of Wikibon and theCUBE that we talk about all the time that a lot of the inference is going to be done at the edge. What does that mean? It means you're going to have factory devices, autonomous vehicles, medical device equipment that's going to have intelligence in there with new types of processors and we'll talk about that. But a lot of the inferences, the conclusions will be made real time. And by the way, these machines will be able to talk to each other. So you'll have machine to machine communication. No humans need to be involved to actually make a decision as to where should I turn or what should be the next move on the factory floor. So again, a lot of the data is going to stay in place. Now, what does that mean for IBM? You still have an opportunity to have data hubs that collect that data, analyze it, maybe push it up to the cloud, develop models, iterate and push it back down. But the edge is a fundamentally new type of approach that we've really not seen before and it brings in a whole ton of new data. Yeah, it's a great point and it's a market phenomenon that has moved and is very rapidly moving from smartphones to the enterprise. So your point is well taken. If you look at the fact, as we talked about earlier, that compute is now proximal to the data as opposed to the other way around and the emergence of things like mesh networking and high bandwidth local communications, peer-to-peer communications. It's not only changing the physical infrastructure model and the best practices around how to implement that infrastructure. It's also fundamentally changing the way you buy them, the way you consume them, the way you charge for them. So it's, that shift is changing and having a ripple effect across our industry in every sense, whether it's from the financial perspective, the operational perspective, the time to market perspective. It's also, and we talk a lot about industry transformation and disruptors that show up in industry Uber being the most obvious example and just got an industry from the bare metal and recreate it. They are able to do that because they've mastered this new environment where the data is king. How you exploit that data cost-effectively, repeatably, efficiently is what differentiates you from the pack and allows you to create a brand new business model that didn't exist prior. So that's really where every other industry is going. You talked about those big five companies in North America that are the top companies now because of data. I often think about Rewind, you know, 25 years. Do you think Amazon, when they built Amazon, really thought they were gonna be in the food service business, the video surveillance business, the drone business, all these other, the book business, right? Maybe the book business, right? But their architecture had to scale and change and evolve with where that's going all around the data because then they can use these data components and all these other places to get smarter, bigger, and grow faster. And that's why they're one of the top five. So this is a really important point, especially for the young people in our audience. So it used to be that if you were in industry, if you were in healthcare, or you were in financial services, or you were in manufacturing, you were in that business for life. Every industry had its own stack. The sales, the marketing, the R&D, everything was wired to that industry and that industry domain expertise was really not portable across businesses because of data and because of digital transformations, companies like Amazon can get into content, they can get into music, they can get into financial services, they can get into healthcare, they can get into grocery. It's all about that data model being portable across those industries. It's a very powerful concept that you would have. And I mean, IBM owns the weather company, right? So I mean, there's a million examples of traditional businesses that have developed ways to either enter new markets or expand their footprint in existing markets by leveraging new sources of data. So you think about a retailer or a wholesale distributor, they have to vary accurately or as accurately as possible, forecast demand for goods and make sure logistically the goods are in the right place at the right time. Well, there are a million factors that go into that. There's weather, there's population density, there's local cultural phenomena, there's all sorts of things that have to be taken into consideration. Previously, that would be near impossible to do. Now you can sit down, again as an individual maker, I can sit down on my desk and I can craft a model that consumes data from five readily available public APIs or data sets to enhance my forecast and I can then create that model, execute it and give it to my IT guide to go scale out. Okay, so I want to start getting into the infrastructure conversation. Again, remember the premise of this conversation does infrastructure matter. We want to explore that. I want to start at the high level with cloud, multi-cloud specifically. We said cloud 2.0 is about hybrid multi-cloud. I'm going to make a statement so if you guys chime in. My assertion is that multi-cloud has largely been a symptom of multi-vendor. Shadow IT, different developers, different workloads, different lines of business, saying, hey, we want to do stuff in the cloud. It's happened so many times in the IT business. And then how is going to govern it? How is this going to be secure? Who's got access control? On and on and on. What about compliance? What about security? Then they throw it over to IT and they say, hey, help us fix this. And so IT has said, look, we need a strategy around multi-cloud. It's horses for courses. Maybe we go to cloud A for our collaboration software, cloud B for the cognitive stuff, cloud C for the cheap and deep storage. Different workloads for different clouds. But there's got to be a strategy around that. So I think that's kind of point number one. And IT is being asked to kind of clean up this stuff. But the future, today the clouds are loosely coupled. There may be a network that connects them. But there's not a really good way to take data or rather, to take code, ship it to data wherever it lives and have it be a consistent, what you were talking about, an enterprise data plane. That's emerging. And that's kind of really where the opportunity is. And then you maybe move into the control plane and the management piece of it and then bring in the edge. But envision this mesh of clouds, if you will, whether it's on-prem or in the public cloud or some kind of hybrid, where you can take metadata and code, ship it to wherever the data is, leave it there. Much smaller, ship five megabytes of code to a petabyte of data, as opposed to waiting three months to try to ship petabytes over the network. It's not going to work. So that's kind of the spectrum of multi-cloud. Moosely coupled today, going to this tightly coupled mesh. Your guys' thoughts on that? Yeah, that's a great point. And I would add to it or expand it even further to say that it's also driving behavioral, fundamental behavioral and organizational challenges within a lot of organizations and large enterprises. Cloud and this multi-cloud proliferation that you spoke about, one of the other things it's done that we talk about, but probably not enough, is it's almost created this inversion situation where in the past you'd have the business saying to IT, I need this, I need this supply chain application, I need this vendor relationship database, I need this order processing system. Now, with the emergence of this cloud and how easy it is to consume and how cost-effective it is, now you've got the IT guys and the engineers and the designers and the architects and the data scientists pushing ideas to the business. Hey, we can expand our footprint and our reach dramatically if we do this. So you get this much more bi-directional conversation happening now, which frankly, a lot of traditional companies are still working their way through, which is why you don't see 100% cloud adoption everywhere. But it drives those very productive, full-duplex conversations at a level that we've never seen before. I mean, we encounter clients every day who are having these discussions, they're sitting down across the table and IT doesn't just have a seat at the table, they are often deriving the go-to-market strategy. So that's a really interesting transformation that we see as well, in addition to the technology. So there are some amazing things happening, Steve, underneath the covers and the plumbing and infrastructure. And look, we think infrastructure matters, that's kind of why we're here, we're infrastructure guys. But I want to make a point. So for decades, this industry is marked to the cadence of Moore's law. The idea that you can double processing speeds every 18 months, disk drive, processors, disk drives, they followed that curve. You could plot it out. The last 10 years, that started to attenuate. So what happened is chip companies would start putting more cores onto the real estate. Well, they're running out of real estate now. So now what's happening is we've seen this emergence of alternative processors. Largely came from mobile, now you have ARM, doing a lot of offload processing, a lot of the storage processing that's getting offloaded, those are ARM processors. And video with GPUs, powering a lot of AIs. You're even seeing FPGAs, they're simple, they're easy to spin up. A6, making a big comeback. So you're seeing these alternative processors powering things underneath where the x86 is, and of course they're still running applications on x86. So that's one sort of big thing, big change in infrastructure to support this distributed systems. The other is Flash. We saw Flash basically take out spinning disk for all high speed applications. We're seeing the elimination of SCSI, which is a protocol that sits in between the disk, and the rest of the network, that's going away. You're hearing things like NVMe and Rocky and PCIe, basically allowing storage to directly talk to the processor. Now, envision this multi-cloud system where you want to ship metadata and code anywhere. These high speed capabilities interconnects, low latency protocols are what sets that up. So there's technology underneath this, and obviously IBM is an inventor of a lot of this stuff, that is really going to power this next generation of workloads, your comments. Yeah, I think all that 100% true, and I think the one component we're feeding a little bit about even in the infrastructure is the infrastructure software. There's hardware, we talked about a lot of hardware components that are definitely evolving to get us better, stronger, faster, more secure, more reliable, and that sort of thing. And then there's also infrastructure software, so not just the application databases or that sort of thing, but software to manage all this. And I think in a hybrid multi-cloud world, you've got these multiple clouds. For all practical purposes, there's no way around it anymore. Marketing gets more value out of the Google analytic tools in Google's cloud, and developers get more value out of using the tools in AWS. They're going to continue to use that. At the end of the day, I, as a business, though, need to be able to extract the value from all of those things in order to make different business decisions to be able to move faster and surface my clients better. There's hardware that's going to help me accomplish that, and then there are software things about managing that whole set of componentry so that I can maximize the value across that entire stack. And that stack is multiple clouds, plus internal clouds, external clouds, everything. Yeah, so it's a great point. And you're seeing clear examples of companies investing in custom hardware. You see Google has its own chip, Amazon has its own chip, IBM's got Power 9 on and on. But none of this stuff works if you can't manage it. So like I talked before about programmable infrastructure, we talked about the data plane and the control plane. That software that's going to allow us to actually manage these multiple clouds as at least a quasi single entity, something like a logical entity, certainly within workload classes and in Nirvana across the entire network. Well, and the principle, or one of the principle drivers of that evolution, of course, is containerization, right? So the containerization phenomenon, and obviously with our acquisition of Red Hat, we're now very keenly aware and acutely plugged into the whole containerization phenomenon, which is great. You're seeing that becoming almost the, I can't think of a good metaphor, but you're seeing containerization become the vernacular that's being spoken in multiple different types of reference architectures and use case environments that are vastly different in their characteristics. Whether they're high throughput, low latency, whether they're large volume, whether they're edge specific, whether they're more consolidated or hub and spoke models. Containerization is becoming the standard by which those architectures are being developed and with which they're being deployed. So we think we're very well positioned working with that emerging trend and that rapidly developing trend to instrument it in a way that makes it easier to deploy, easier to instrument, easier to develop. So that's key. And I wanna sort of focus now on the relevance of IBM. Can I just tie one thing they're saying? Because that whole containerization thing back to your original point Dave about moving data being very expensive and the fact that the fact that you want to move code out to the data now with containers, microservices, all of that stuff gets a lot easier, development becomes a lot faster and you're actually pushing the speed of business faster. Well, and the other key point is we talked about moving code to the data. As you move the code to the data and run applications anywhere, wherever the data is, using containers, the Kubernetes, et cetera, you don't have to test it. It's going to run assuming you have the standard infrastructure in place to do that and the software to manage it. That's huge because that means business agility. It means better quality and speed. All right, let's talk about IBM. The world is complex. This stuff is not trivial. The more clouds we have, the more edge we have, the more data we have, the more complex it gets. IBM happens to be very good at complex. Three components of the innovation. Cocktail, data, AI and cloud. IBM, your customers have a lot of data. You guys are good with data. It's a very strong analytics business. Artificial intelligence, machine intelligence, you've invested a lot in Watson. That's a key component of your business and cloud. You have a cloud. It's not designed to compete knock heads on the race to zero with the cheap and deep storage clouds. It's designed to really run workloads and applications, but you've got all three ingredients. As well, you're going hard after the multi-cloud world. For you guys, you've got infrastructure underneath. You've got hardware and software to manage that infrastructure, all the modern stuff that we've talked about. That's what's going to power the customer's digital transformations. And we'll talk about that in a moment. But maybe you could expand on that in terms of IBM's relevance. Sure, so again, using the kind of maker, the maker economy metaphor. Bridging from that individual level of innovation and creativity and development to a broadly distributed, globally available workload or information source of some kind. The process of that bridge is about scale and reach. How do you scale it? So that it runs effectively, optimally, is easily managed, all looks and feels the same, falls under the common umbrella of services. And then how do you get it to as many endpoints as possible, whether it's individuals or entities or agencies or whatever, scale and reach. IBM is all about scale and reach. I mean, that's kind of our stock and trade. We are able to take solutions from small, kind of departmental level or kind of skunkworks level and make them large, secure, repeatable, easily managed services and make them as turnkey as possible. Our services organization has been doing it for decades, exceptionally well. Our product portfolio supports that. You talked about Watson and kind of the cognitive computing story. We've been a thought leader in this space for decades. I mean, we didn't just arrive on the scene two years ago when machine learning and deep learning and IOC started to become prominent and say, this sounds interesting, we're gonna plant our flag here. We've been there. We've been there for a long time. So I kind of, from an infrastructure perspective, I kind of like to use the analogy that our whole technology ethos is built on AI. It's built on cognitive computing and sort of adaptive computing. Every one of our portfolio products is imbued with that same capability. So we use it internally. We're kind of built from AI for AI. So maybe that's the answer to this question, but so what do you say to somebody who says, well, I wanna buy my flash storage from Pure. I wanna buy my database from Oracle. I wanna buy my Intel servers from Dell, whatever. And I wanna control them. And I gotta go build it myself. Why should I work with IBM? Do you get that a lot and how do you respond to that, Steve? I think this whole new data economy has opened up a lot of places for data to be stored anywhere. I think at the end of the day, it really comes down to management. And one of the things that I was thinking about as you guys were conversing is the enterprise class or enterprise need for things like security and protection and that sort of thing that rounds out the software stack in our portfolio. One of the things we can bring to the table is, sure, you can buy piece parts and componentry from different people that you want, right? And in that whole notion around fail fast, sure, you can get some new things that might be a little bit faster, that might be here first. But one of the things that IBM takes a lot of pride into is the quality of their delivery of both hardware and software, right? So to me, even though the infrastructure does matter quite a bit, the question is how much and to what degree? So when you look at our core clients, the global 2000, right, they want to fail fast. They want to fail fast securely. They want to fail fast and make sure they're protected. They want to fail fast and make sure they're not accidentally giving away the keys to the kingdom. At the end of the day, a lot of the large vendor, a lot of the large clients that we have need to be able to protect their IP, their brain trust. They're, but also need the flexibility to be creative and create new applications that gain new customer bases. So the way I look at it and when I talk to clients and when I talk to folks is, is we want to give you them that while also making sure they're protected and secure. Now that said, I would just add that and 100% an accurate depiction. The data economy is really changing the way, not only infrastructure is deployed and designed, but the way it can be. I mean, it's opening up possibilities that didn't exist and there's new ones cropping up every day. To your point, if you want to go kind of best of breed or you want to have a solution that includes multi-vendor solutions, that's okay. I mean, the whole idea of using, again, for instance, containerization, thinking about Kubernetes and Docker, for instance, as a protocol standard or a platform standard across heterogeneous hardware, that's fine, like we will still support that environment. We believe there are significant additive advantages to looking at IBM as a full solution or a full stack solution provider and our largest, you know, most mission critical application clients are doing that. So we think we can tell a pretty compelling story and I would just finally add that we also often see situations where in the journey from the kind of maker to the largely deployed enterprise class workload, there's a lot of pitfalls along the way and there's companies that will occasionally, you know, bump into one of them and come back six months later and say, okay, we encountered some scalability issues, some security issues, let's talk about how we can develop a new architecture that solves those problems without sacrificing any of our advanced capabilities. All right, let's talk about what this means for customers. So everybody talks about digital transformation and digital business. So what's the difference in a business and a digital business, it's how they use data. In order to leverage data to become one of those incumbent disruptors using Jenny's term, you've got to have a modern infrastructure. If you want to build this multi-cloud, you know, connection point enterprise data pipeline to use your term, Randy, you've got to have modern infrastructure to do that. That's low latency that allows me to ship data to code that allows me to run applications anywhere, leave the data in place, including the edge and really close that gap between those top five data value companies and yourselves. Now, the other piece of that is you don't want to waste a lot of time and money managing infrastructure. You've got to have intelligence infrastructure, you've got to use modern infrastructure and you've got to redeploy those labor assets toward higher value, more productive for the company activities. So we all know IT labor is a choke point and we spend more on IT labor, managing loans, provisioning servers, tuning databases, all that stuff. That's got to change in order for you to fund digital transformations. So that to me is the big takeaway as to what it means for customers. And we talk about that, sorry, we talk about that all the time and specifically in the context of the enterprise data pipeline and within that pipeline, kind of the newer generation, machine learning, deep learning, cognitive workload phases. The data scientists who are involved at various stages along the process are obviously kind of scarce resources, they're very expensive. So you can't afford for them to be burning cycles, managing environments, spinning up VMs and moving data around and creating working sets and enriching metadata. That's not the best use of their time. So we've developed a portfolio of solutions specifically designed to optimize them as a resource, as a very valuable resource. So I would vehemently agree with your premise. We talk about the rise of the infrastructure developer. So at the end of the day, I'm glad you brought this topic up because it's not just customers, it's personas. IBM talks to different personas within our client base or our prospect base about why is this infrastructure important to them? And one of the core components is skill. When we talk about this rise of the infrastructure developer, what we mean is I need to be able to build composable, intelligent, programmatic infrastructure that I as IT can set up, not have to worry about a lot of risk about it breaking and have to do a lot of troubleshooting, but turn the keys over to the users now. Let them use the infrastructure in such a way that helps them get their job done better, faster, stronger, but still keeps the business protected. So don't make copies into production and screw stuff up there. But if I want to make a copy of the data, feel free, go ahead and put it in a place that's safe and secure and it won't get stolen and it also won't bring down the enterprise as it's trying to do its business. Very key, key components to, we talk about AI infused data protection, AI infused storage. At the end of the day, what is an AI infused data center? It needs to be an intelligent data center and I don't have to spend a lot of time doing it. The new IT person doesn't want to be troubleshooting all day long. They want to be in looking at things like ARM and VME. What's that going to do for my business to make me more competitive? That's where IT wants to be focused. Yeah, and it's also, just to kind of again, build on this whole idea. We haven't talked a lot about it, but there's obviously a cost element to all this, right? I mean, enterprises are still very cost conscious and they're still trying to manage budgets and they don't have an unlimited amount of capital resources. So things like the ability to do fractional consumption. So buy, you know, paper drink, right? Buy small bits of infrastructure and deploy them as you need. And also to Steve's point, and this is really Steve's kind of area of expertise and where he's a thought leader is kind of data efficiency. You also can't afford to have copy sprawl, excessive data movement, poor production schemes, slow recovery times and recall times. You've got to, especially as data volumes are ramping, you know, geometrically, the efficiency piece and the cost piece is absolutely relevant. And that's another one of the things that often gets lost in translation between kind of the maker level and the deployment level. So I wanted to do a little thought exercise. For those of you who think that this is all, you know, bromide and BS. Cloud 2.0 is also about, we're moving from a world of remote cloud services to one where you have this mesh, which is ubiquitous of digital services. You talked about intelligence, Steve, you know, the intelligent data center. So all these digital services, what am I talking about? AI, blockchain, 3D printing, autonomous vehicles, edge computing, quantum, RPA and all the other things in the Gardner hype cycle. You'll be able to procure these as services. They're part of this mesh. So here's the thought exercise. When do you think that owning and driving your own vehicle is no longer going to be the norm? All right. Interesting thesis question. Okay, answer if you like. Why do you ask the question? Well, because these are some of the disruptions. So the questions are designed to get you thinking about the potential disruptions. You know, is it possible that our children's children aren't going to be driving their own car? It's a cultural change. When I was 16 years old, I couldn't wait. But you're starting to see a shift their quasi-autonomous vehicles. It's all sort of the rage. Personally, I don't think they're quite ready yet, but it's on the horizon. Okay, I'll give you another one. When will machines be able to make better diagnoses than doctors? Actually, both of those are, so let's hit on autonomous and self-driving vehicles first. I agree that they're not there yet. I will say that we have a pretty thriving business practice and competency around working with ADAS providers. And there's an interesting perception that ADAS autonomous driving projects are like there's 10 of them around the world, right? Maybe there's 10 metal level ADAS projects around the world. What people often don't see is there is a gigantic ecosystem building around ADAS, all the data sourcing, all the telemetry, all the hardware, all the network support, all the services. I mean, building around this is phenomenal and it's growing at a ridiculous rate. So we're very hooked into that. We see tremendous growth opportunities there. If I had to guess, I would say within 10 to 12 years, there will be functionally capable, viable autonomous vehicles, not everywhere, but they will be, you will be able as a consumer to purchase one. Yeah, that's good. Okay, and so that's good. I agree, the timeline is not within the next three to five years. All right, how about retail stores? Will retail stores largely disappear? Where, Randy and I were just someplace the other day and I said, then there used to be a brick and mortar there and we were walking through the Cambridge side of Galleria and now the third floor, there's no more stores, right? Up there is gonna be all offices. They've shrunk it down to just two floors of stores and I highly believe that it's because the retailers online are doing so well. I mean, think about, it used to be tricky and how do you get in and I need the Walmart mentality, go get with Amazon and that became very difficult. Look at places like Bombus or Casper or all the luggage plate, all this little individual boutique selling online, selling quickly, never having to have to open up a store, speed of deployment, speed of product, I mean, it's phenomenal. Yeah, and frankly, if Amazon could, and they're investing billions of dollars and they're trying to solve the last mile problem, if Amazon could figure out a way to deliver 95% of their product catalog prime within four to six hours, brick and mortar stores would literally disappear within a month and I think that's a factual statement. Okay, I'll give you another one. Well, banks lose control, traditional banks lose control of the payment systems. You, Venmo, Zell, you see the banks are smart, they're buying up, you know, fintech companies, but right, these are entrenched. Yeah, that's another one with an interesting philosophical element to it, because people and some of it's generational, right? Like our parents' generation would be horrified by the thought of taking a picture of a check or using blockchain or some kind of a fintech payment service. Or any Bitcoin. Right, Bitcoin. Or any kind of, yeah, exactly. You guys own Bitcoin? I do, my dad asked me. You own Bitcoin? I do. I own Bitcoin too. We're here. So we're here. We're old. And we're hip. We're waiting it out though, it's fine. By the way, I just wanted to mention that we don't hang out in the mall that's actually right across the street from our office. I want us to just add that to the previous comment. So there is a philosophical piece of it though. Like our generation, we're fairly comfortable now because we've grown up in a sense with these technologies being adopted. Our children, the concept of going to a bank for them will be foreign. I mean, it will make, if they'll have no context for the context, for the process of going to speak face to face to another human that just won't exist. Well, will automation, whether it's robotic process automation, other automation, 3D printing, will that begin to swing the pendulum back to onshore manufacturing? Maybe Terrace will help too. But again, the idea that machine intelligence increasingly will disrupt businesses, there's no industry that's safe from disruption because of the data context that we talked about before. Randy and I put together a, you know, IBM loves to use big words, transformation, agile. And as a sales rep, you're in the field and you're trying to think about, okay, what does that mean? What does that mean for me to explain to my customer? So we put together this whole thing about what does transformation mean? One was the taxi service, right? And the other one was retail. So another one was FinServe. I mean, you're hitting on all the core things, right? But this transformation, I mean, it goes so deep and so wide. When you think about exactly what Randy said before about Uber just transforming just the taxi business, retailers and taxis now and hotel chains and that sort of thing. The know your customer, they're getting all of that from data, data that I'm putting in. Not that they're doing work to extract out of me that I'm putting in. So that autonomous vehicle comes to pick up Steve Keniston. It knows that Steve likes iced coffee on his way to work, gives me a coupon on a screen. I hit the button. It automatically stops at Starbucks for me and it pre-ordered it for me. You're talking about that whole ecosystem wrapped around just autonomous vehicles and data now? It's unbelievable. Yeah, and we're not far off from the minority report era of anthropomorphic advertising targeted at an individual in real time. I mean, that's gonna happen. It's almost there now. I mean, to Steve's point, you will get, if I walk into Starbucks, my phone says, hey, why don't you use some points while you're here, Randy? So that's happening. And facial recognition, it's all coming together. And again, underneath all this is infrastructure. So infrastructure clearly matters. If you don't have the infrastructure to power these new workloads, you're in trouble. Yeah, and I would just add, and I think we're all in agreement on that. And from my perspective, from an IBM perspective, through my eyes, I would say, we're increasingly being viewed as kind of an arms dealer and that's probably a horrible analogy, but we're being viewed as a supplier to the providers of those services, right? So we provide the raw materials and the machinery and the tooling that enables those innovators to create those new services and do it quickly, securely, reliably, repeatedly at a reasonable cost, right? So it's a step back from direct engagement with customers and clients and architects, but that's where our whole industry is going, right? We are increasingly more abstracted from the end consumer. We're dealing with the sort of assembly. We're dealing with the assemblers. They take the pieces and assemble them and deliver the services. So we're not as often doing the assembly as we are providing the raw materials. Guys, great conversation. I think we set a record in terms of the industry. I'd like to say thank you very much for coming on. No problem, thanks, Dave. Yeah, this was great. Thank you so much. Thank you for watching, everybody. We'll see you next time. You're watching The Cube.