>> Announcer: From the SiliconANGLE Media office in Boston, Massachusetts, it's theCube! (bright music) Now here's your host, Dave Vellante. >> Hi everybody, welcome to this special presentation in the Marlborough offices of theCube. My name is Dave Vellante, and I'm here with a friend, a colleague, a mentor of mine, David Moschella who is an author and a Fellow at Leading Edge Forum. Dave, thanks for coming in. It's great to see you. >> Hey, great to see you again. So we're going to talk about your new book, Seeing Digital: A Visual Guide to Industries, Organizations, and Careers of the 2020s. I got it here on my laptop. Got it off of Amazon, so check it out. We're going to be unpacking what's in there today. This is your third book I believe, right? Waves of Power and... >> David: Customer-Driven IT. >> Customer-Driven IT which was under the '03 timeframe coming out of the dot-com, and to me this is your most significant work, so congratulations on that. >> Well, thank you. >> Dave: I know how much work goes into it. >> You bet. >> So what was the motivation for writing this book? >> Well it's a funny thing when books are a lot of work, and during those times you wind up asking yourself why am I (laughing) doing this because they put in so much time. But for the last seven or eight years our group, the Leading Edge Forum, we've been doing a lot of work mostly for large organizations and our clients told us that the work we've been doing in consumerization, in Cloud, in disruption, in machine intelligence was really relevant to not just them but to their wider audiences of their partners, their customers, their employees. And so people are asking can we get this to a wider audience, and really that is what the book is trying to do. >> Yeah, you guys have done some great work. I know when I can get my hands on it I consume it. For those of you who don't know, Dave originally came up with the theory of disintegration to kind of explain the shift from centralized mainframe era to the sort of open distributed competition along different lines which really defined the Wintel era. So that was kind of your work really explaining industry shifts in a way that helped people and executives really understand that. And then the nice thing about this book is you're kind of open-sourcing a decade's worth of research that yourself and your colleagues have done. So talk about the central premise of the book. We're entering a new era. We're sort of exiting the Cloud, Web 2.0 era. We're still trying to figure out what to call this. But what's the central premise of the book? >> Yeah, the central premise is that the technologies of the 2020s will indeed define a new era, and the IT era industry just evolves. We had the mainframe era, the mini era, the PC and the Internet era, the mobility era, and now we're going in this era of intelligence and automation and blockchains and speech and things that are just a entire new layer of intelligence, and that that layer to us is actually more the powerful than any of the previous layers we've seen. If you think back, the first Web was founded around technologies like search and email and surfing the Web, quite simple technologies and created tremendous companies. And then the more recently we have sort of the social era for Facebook and Salesforce. And all these companies, they sort of took advantage of the Cloud. But again, the technologies are relatively simple there. Now we're really looking at a whole wave of just fundamentally powerful technology and so trying to anticipate what that's going to mean. >> So going from sort of private networks to sort of public networks to a Cloud of remote services to now this set of interrelated digital services that are highly accessible and essentially ubiquitous is what you put forth in the book, right? >> Yeah, and we put a lot of emphasis on words. Why do words change? We had an Internet that connected computers and a Web that sort of connected pages and documents and URLs. And then we started talking about Cloud of stuff out there somewhere in cyberspace. But when we look at the world that's coming and we use those words, pervasive, embedded, aware, autonomous, these aren't words that are really associated with a Cloud. And Cloud is just a metaphor, that word, and so we're quite sure that at some point a different word will emerge because we've always had a different word for every era of change and we're going into one of those eras now. >> So a lot of people have questions about we go to these conferences and everybody talks about digital disruption and digital transformation, and it's kind of frankly lightweight a lot of times. It doesn't have a lot of substance to it. But you point out in the book that CEOs are asking the question, "How do I get digital right?" They understand that something's happening, something's changing. They don't want to get disrupted, but what are some of the questions that you get from some of your clients? >> Yeah, that first question, are we getting digital right sort of leads to almost everything. Companies look at the way that a Netflix or Amazon operates, and then they look at themselves and they see the vast difference there. And they ask themselves, "How can we be more like them? "How can we be that vast, that innovative, that efficient, "that level of simple intuitive customer service?" And one of the ways we try to define it for our clients is how do they become a digital first organization where their digital systems are their face to the marketplace? And most CEOs know that their own firm doesn't operate that way. And probably the most obvious way of seeing that is so many companies now feeling the need to appoint a Chief Digital Officer because they need to give that task to someone, and CDOs are no panacea but they speak to this need that so many companies feel now of really getting it right and having a leadership team in place that they have confidence in. And it's very hard work, and a lot of our clients, they still struggle with it. >> One of the other questions you ask in the book that is very relevant to our audience given that we have a big presence in Silicon Valley is can Silicon Valley pull off a dual disruption agenda? What do you mean by that? >> Yeah, if you look at the Valley historically you could see them essentially as arms merchants. They were selling their products and services to whoever wanted to buy them, and companies would use them as they saw fit. But today in addition to doing that they are also what we say is they're an invading army, and they are increasingly competing with the very customers they've traditionally supplied, and of course Amazon being perhaps the best example of that. So many companies dependent on AWS as a platform, but there's Amazon trying to go after them in health care or retail or grocery stores or whatever business they're in. Yeah, content, every business under the sun. And so they're wearing these two dual disruptions hats. The technologies of our time are very disruptive, machine intelligence, blockchains, virtual reality, all these things have disruptive technology. But that second disruptive agenda of how do you change insurance, how do you change health care, how do change the car industry, that's what we mean, those two different types of disruptions. And they're pursuing both at the same time. >> And because it's digital and it's data, that possibility now exists that a company, a technology company can traverse industries which historically haven't been able to be penetrated, right? >> Yeah, absolutely, in our view every industry is going to be transformed by data one way or another. Whether it is disrupted or not is a second question, but the industry'll be very different when all of these technologies come into play, and the tech companies feel like they have the expertise and the vision of it. But they also have the money, and they're going to bet heavily to pursue these areas to continue their growth agenda. >> So one of the other questions of course that IT people ask is what does it mean for my job, and maybe we can, if we have time, we can talk about that. But you answer many of these questions with a conceptual framework that you call the Matrix which is a very powerful, you said words matter, a very powerful concept. Explain the Matrix. >> Okay, yeah. If we start and go back they have this idea that every generation of technology has its own words, Internet, Web, Cloud, and now we're going to a new era, so there will be a new word. And so we use the word Matrix as our view of that, and we chose it for two reasons. Obviously there's the movie which had its machine intelligence and virtual worlds and all of that. But the real reason we chose it is this concept that a matrix as in matrix mathematics is a structure that has rows and columns. And rows and columns is sort of the fundamental dynamic of what's going on in the tech sector today, that traditionally every industry had its own sort of vertical stack of capabilities that it did and it was sort of top to bottom silo. But today those horizontal platforms, the PayPals, the AWSs, the Facebooks, they run this, Salesforce, all these horizontal services that cut across those firms. And so increasingly every industry is leveraging a common digital infrastructure, and that tension between the traditional vertical stacks and these enormously powerful horizontal technology firms is really the structural dynamic that's in play right now. >> And at the top of that Matrix you have this sort of intelligence and automation layer which is this new layer. You don't like the term artificial intelligence. You make the point in the book there's nothing really artificial about it. You use machine intelligence. But that's that top layer that you see powering the next decade. >> Absolutely, if you look at the vision that everybody tends to have, autonomous cars, personalized health care, blockchain-based accounting, digital cash, virtual education, brain implants for the media, every one of those is essentially dependent on a layer of intelligence, automation, and data that is being built right now. And so just as previous layers of technology, the Web enabled a Google or an Amazon, the Cloud enabled AWS or Salesforce, this new layer enables companies to pursue that next layer of capabilities out there to build that sort of intelligent societal infrastructure of the 2020s which will be vastly different than where we are today. >> Will the adoption of the Matrix, in your opinion, occur faster because essentially it's built on the Internet and we have the Internet, i.e. faster than say the Internet or maybe some other major innovations, or is it going to take time for a lot of reasons? >> I think the speed is actually a really interesting question because the technology of the 2020s are extremely powerful, but most of them are not going to be immediate hits. And if you look back, say, to search, when search came out it was very powerful and you could scale it massively quickly. You look at machine learning, you look at blockchains, you look at virtual realities, you look at algorithms, speech and these areas, they're tremendously powerful. But there's no scenario where those things happen overnight. And so we do not see an accelerating pace of change. In fact it might be people often overestimate the speed of change in our business and consistently do that. But what we see is a sort of fundamental transformation over time, and that's why we put a lot of emphasis on the 2020s because we do not see two years from now this stuff all being in place. >> And you have some good examples in the book going back to the early days of even telephony. So it's worth checking that out. I want to talk about, bring it back to data, Amazon, Google, Apple, Microsoft, and Facebook, top five companies, public companies in terms of market cap. Actually it's not true after the Facebook fake news thing. I mean Berkshire Hathaway is slightly past Facebook. >> It'll be back (laughs). But I agree, it'll be back, but the key point there is these companies are different, they've got data at their core. When you compare that to other companies even financial services industry companies that are really data companies but the data's very bespoken, it's in silos. Can those companies, those incumbent companies, can they close that gap? Maybe you could talk about that a little bit. >> Yeah, we do a lot of work in the area of machine intelligence, artificial, whatever you want to call it. And one of the things you see immediately is this ridiculously large gap between what these leading companies do versus most traditional firms because of the talent, the data, the business model, all the things they have. So you have this widening gap there. And so the big question is is that going to widen or is it going to continue, will it narrow? And I think that the scenario for narrowing it I think is a fairly good one. And the message we say to a lot of our clients is that you will wind up buying a lot more machine intelligence than you will build because these companies will bring it to you. Machine intelligence will be in AWS. It'll be in Azure. It'll be in Salesforce. It'll be in your devices. It'll be in your user interfaces. It'll be in the speech systems. So the supply-side innovations that are happening in the giants will be sold to the incumbents, and therefore there will be a natural improvement in today's situation where a lot of incumbents are sort of basically trying to build their own stuff internally, and they're having some successes and some not. But that's a harder challenge. But the supply side will bring intelligence to the market in a quite powerful way and fairly soon. >> Won't those incumbents, though, have to sort of reorganize in a way around those new innovations given that they've got processes and procedures that are so fossilized with their existing businesses? >> Absolutely, and the word digital transformation is thrown around everywhere. But if it means anything it is having an organization that is aligned with the way technology works. And a good example of that is when you use Netflix today there's no separate sales experience, market experience, customer service, it's just one system and you have one team that builds those systems. In a typical corporation of course you have the sales organization and the marketing organization and the IT organization and the customer service organization. And those silos is not the way to build these systems. So the message we send to our clients if you really want to transform yourself you have to have more of this team approach that is more like the way the tech players do it. And that these traditional boundaries essentially go away when you go in the digital world where the customer experience is all those things at the same time. >> So if I'm hearing you correctly it's sort of a natural progression of how they're going to be doing business and the services that they're going to be procuring, but there's probably other approaches. Maybe it's force, but you're seeing maybe M&A or you're seeing joint ventures. Do you see those things as accelerating or precipitating the transformation or do you think it's futile and it really has to be led from the top and at the core? >> It's one of the toughest issues out there. And the reason people talk about transformation is because they see the need. But the difficulty is enormous. Most companies would say this is a three- or four-year process to make significant change, and this in a marketplace that changes every few months. So incumbent firms, they see where they want to go and it's very hard, and this is why this whole thing of getting digital right is so important, that people need to commit to significant change programs, and we're seeing it. And my parent company, DXC, we do a lot of this with clients and they want to embark on this program and they need people who can help them do it. And so leading a transformation agenda in most firms is really what digital leadership is these days and who's capable of doing that which requires tremendous skills in soft skills and hard skills to do right. >> Let's talk about industries and industry disruption. When you looked at the early disrupted industries whether it was publishing, advertising, music, one maybe had the tendency to think it was a bits versus atoms thing, but you point out in the book it's really not the case because you look at taxis, you look at hotels. Those are physical businesses and they've been disrupted quite substantially. Maybe you could give us some thoughts and insight there, particularly with regard to things like health care, financial services which haven't been disrupted. >> And there's a huge part of the work that I've been doing for years. And as you say, if you look at the industries that actually have been disrupted, they're all relatively low-security, low-risk businesses, music, advertising, taxis, retail. All these businesses have had tremendous changes. But the ones that haven't are all the ones where the stakes are higher, banking, insurance, health care, aerospace, defense. They've been hardly disrupted at all. And so you have this split between the low-risk industries that have changed and the high-risk ones that haven't. But what's interesting to me about that is that these technologies of the 2020s are aimed almost directly at those high-risk industries. So machine intelligence is aimed directly at health care and autonomous systems is aimed directly at defense and blockchains are aimed directly at banking and insurance. And so the technologies of the past if you look at Internet and the Web and the Cloud eras, they were not aimed at these industries. But today's are, so you now have at least a highly plausible scenario where those industries might change too. >> When to talk to companies in those industries that haven't been disrupted do you get a sense of complacency that ah well, we haven't been disrupted, We're going to wait and see, or do you see a sense of urgency? >> No, complacency is baked in for years of people saying, "We've heard all this before. "We're doing just fine. "Maybe it's their industry but not ours." >> Dave: You don't buy it. >> Or the main one is, "I'll be (laughing) retired "before any of this stuff matters for the senior execs." And the thing about all four of those is they're probably true. They have heard all this before because there was a lot of excessive hype. Many of them are doing just fine. Well the one about the other industries is a wrong one, but and many of them will be retired before the things really bite if executive's in their late in their career. So the inertia and the complacency is an enormous issue in most traditional companies. >> So let's do a little lightning round if we can. Oh, actually I just want to make a point. In the book you lay out disruption scenarios for each industry which is really worthwhile. We don't have time to go through that here, but let's do a little lightning round here, some of the questions that you ask that I'd love to get your opinion on of which of course there are no right answers but we can maybe frame it. Let's start with retail. Do you think large retail stores are going to disappear? >> Well the first I say is that disruption is never total. There are still bookstores, there are still newspapers, there are still vinyl records. >> Dave: Mainframes, saving IBM. >> (laughing) Indeed, indeed, but real disruption means that the center of gravity is just totally moved on. And when you look at retail from that point of view, absolutely. And will large ones totally disappear? No, but Wal-Mart is teetering. If you go into a large, Best Buy, a company that strong hero locally, you go into there, there's hardly anybody in there. And so those stores are in tremendous trouble. The grocery stores, the clothing stores, they'll have probably a better future, but by and large they will shrink, and the nature of malls will change quite substantially going forward. People are going to have to find other uses for those spaces, and that's actually going on right now. >> It's funny, it is, and certainly some of the more remote malls you find that they're waning. But then some of the higher-end malls, they seem, you can't find a parking space. What's your sense of that, that that's still inevitable or it's because it's more clothing or maybe jewelry? >> And there's some parts of America that have a lot of money, and therefore they fill up malls. But I think if you look at what's going on in the malls, though, they're becoming more like indoor cities full of restaurants and health clubs and movie theaters and sometimes even college courses and health care centers, daycare centers, air conditioning. Think of them as an indoor environment where you might have the traditional anchor stores but they're less necessary over time. Quite a bit less necessary. >> You mentioned college courses. Education's something we haven't talked about which is again ripe for disruption. Machines, will they make better diagnoses than doctors? >> Yeah, you see this already in image processing, anything that has to do with an image, X-rays and mammograms, cancers, anything, tissues. The machine learning progress there has been tremendous and to the point where schools now should be seriously thinking about how many radiologists do they really want to train because those people are not going to be needed as much. However they're still part of the system. They approve things, but the work itself is increasingly done by machines. And it means increasingly that it's not just done by machine, it's done by one machine somewhere else rather than every hospital setting up its own operations to do this stuff. And health care costs are crazy high in every country in the world, especially here in America. But if you're ever going to crack those costs you have to get some sort of scale, and these machine learning-based systems are the way to do it. And so it is to me not just a question of should this happen, it's that this is so what needs to happen. It's really the only sort of economic path that might work. >> You make the point that health care in particular is really ripe for disruption of all industries. The next one's really interesting to me. You talked about blockchain being sort of aimed at banking and financial services and as an industry that has not really yet been disrupted. But do you think banks will lose control of the payment systems? >> Banks have been incredibly good at keeping control through cash and paper checks and credit cards and ATM machines. They've been really good about that and perhaps they will ride this one too. But you can see countries are clearly going to, they're getting rid of cash. They're going to digital currencies. There's the need to be able to send money around as simply as we send emails around, and the banking industry is not really supporting (laughing) those changes right now. So they are at risk, but they are very good at co-opting stuff, and I wouldn't count them out. >> And the government really wants to get rid of paper money. You've made that point, and the government and the financial services-- >> Work together, and yeah. >> They always work together, they have a lot to lose. >> Yeah, and way back when Satoshi Nakamoto, whoever he or she is or it, they, whatever it is, said that bitcoin would either be very, very big or it would vanish altogether. And I think that statement is still true, and we're still in that middle world. But if bitcoin vanishes, something doing a similar thing will emerge because the concepts and the capabilities there are really what people want. >> Yeah, the killer app for blockchain is for right now it's money. (laughing) >> Yeah, it's speculation, (laughing) I mean it's, (laughing) and no one uses it to buy anything. (Dave laughing) That was the original bitcoin vision of using it to go buy pizzas and coffees. It's become gold, it's digital gold. I mean it's all it is. >> The value store... >> It's digital gold that is very good in the dark Web. >> And if anybody does transact in bitcoin they immediately convert it to fiat currency. (laughing) >> Perhaps someday we'll learn that the Russians actually built bitcoin (Dave laughing) and it's Putin's in control. (David and Dave laughing) Stranger things have happened. >> It's possible. >> Hey, why keep it anonymous? >> They are the masters of the dark Web. (Dave laughing) >> Could be Russians, could be a woman. >> David: Right, right, nobody has any idea. >> Robotic process automation is really interesting with software robots, robots. Do you see that reversing sort of offshoring, offshore manufacturing and other services? >> Not really, I think in general people looked at robotics, they looked at 3D printing and said, "Maybe we can bring all this stuff back home." But the reality is that China uses robots and 3D printing too and they're really good at it. If anything's going to bring manufacturing back home it's much more political pressures, trade strategies, and all the stuff you see going on right now because we do have crazy imbalances in the world that probably will have to change. And as Ben Stein the economist once said, "Well if something can't go on forever, it won't." And I think there will be some reversals, but I think they'll be less about technology than they will be about political pressures and trade agreements and those sort of changes. >> Because the technology's widely accessible. So how far do you think we can take machine intelligence and how far should we take machine intelligence? >> Well I make a distinction right now that I think machine intelligence for particular purposes is tremendous if you want to recognize faces or eventually talk to something or have it read something or recognize an activity or read images and do all the things it's doing, it's very good. When they talk about a more general-wise machine intelligence it's actually really poor. But to me that's not that important. And one way we look at machine intelligence, it's almost like the app industry. There'll be an app for that, there'll be a machine learning algorithm for almost every little thing that we do that involves data. And those areas will thrive mightily. And then sort of the bottom line we try to at that as who's got the best data? Facebook is good at facial recognitions because it's got the faces, and Google's good at language translation because it has the books and language pairs better than anybody else. And so if you follow the data and where there's good data machine learning will thrive. And where there isn't it won't. >> The book is called Seeing Digital: A Visual Guide to the Industries, Organizations, and Careers of the 2020s, and part of that visual guide is every single page actually has a graphic. So really a new concept that you've... >> Yeah, and thanks for bringing that in. And the reason the book is called Seeing Digital is that the book itself is a visual book, that every page has a graphic, an image, a picture, and explains itself below. And just in our own work with our own clients people tell us it's just a more impactful way of reading. So it's a different format. It's great in the ebook format because you can use colors, you can do lots of things that the printed world doesn't do so well. And so we tried to take advantage of modern technologies to bring a different sort of book to the market. >> That's great. So Google it and you'll find it easily. Dave, again, congratulations. Thanks so much for coming on theCube. >> David: Thank you, a pleasure. >> All right, and thank you for watching, everybody. We'll see you next time. (bright music)

>> From the theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Welcome to this week's Wikibon, theCUBE's insights powered by ETR. As we exited the isolation economy last year, supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this Breaking Analysis, we address the 10 most frequently asked questions we get around supercloud. Okay, let's review these frequently asked questions on supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out superclouds? We'll try to answer why the term supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that superclouds solve specifically. And we'll further define the critical aspects of a supercloud architecture. We often get asked, isn't this just multi-cloud? Well, we don't think so, and we'll explain why in this Breaking Analysis. Now in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building superclouds? What workloads and services will run on superclouds? And 8-A or number nine, what are some examples that we can share of supercloud? And finally, we'll answer what you can expect next from us on supercloud? Okay, let's get started. Why do we need another buzzword? Well, late last year, ahead of re:Invent, we were inspired by a post from Jerry Chen called "Castles in the Cloud." Now in that blog post, he introduced the idea that there were sub-markets emerging in cloud that presented opportunities for investors and entrepreneurs that the cloud wasn't going to suck the hyperscalers. Weren't going to suck all the value out of the industry. And so we introduced this notion of supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now it turns out, that we weren't the only ones using the term as both Cornell and MIT have used the phrase in somewhat similar, but different contexts. The point is something new was happening in the AWS and other ecosystems. It was more than IaaS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services to solve new problems that the cloud vendors in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level, the supercloud, metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted, love it or hate it. It's memorable and it's what we chose. Now to that last point about structural industry transformation. Andy Rappaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor-based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC Analyst who first introduced the concept in 1987, four years before Rappaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors, and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel, that's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of "The Matrix" that's shown on the right hand side of this chart. Moschella posited that new services were emerging built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term Matrix because the conceptual depiction included not only horizontal technology rose like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D, and production, and manufacturing, and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries, jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple, and payments, and content, and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And supercloud is meant to imply more than running in hyperscale clouds, rather it's the combination of multiple technologies enabled by CloudScale with new industry participants from those verticals, financial services and healthcare, manufacturing, energy, media, and virtually all in any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or supercloud. And we'll come back to that. Let's first address what's different about superclouds relative to hyperscale clouds? You know, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud so they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc, and Google Anthos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, cost, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And of course, the lesser margin that's left for them to capture. Will the hyperscalers get more serious about cross-cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They had a long way to go a lot of runway. So let's talk about specifically, what problems superclouds solve? We've all seen the stats from IDC or Gartner, or whomever the customers on average use more than one cloud. You know, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem because each cloud requires different skills because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data, it's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds, and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out superclouds that solve really specific and hard problems, and create differential value. Okay, let's dig a bit more into the architectural aspects of supercloud. In other words, what are the salient attributes of supercloud? So first and foremost, a supercloud runs a set of specific services designed to solve a unique problem and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, supercloud might be optimized for lowest cost or lowest latency, or sharing data, or governing, or securing that data, or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in a most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery, or data sovereignty, or whatever unique value that supercloud is delivering for the specific use case in their domain. And a supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the supercloud platform to fill gaps, accelerate features, and of course innovate. The services can be infrastructure-related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on-premises. Okay, so another common question we get is, isn't that just multi-cloud? And what we'd say to that is yes, but no. You can call it multi-cloud 2.0, if you want, if you want to use it, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud by design, is different than multi-cloud by default. Meaning to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A, you buy a company and they happen to use Google Cloud, and so you bring it in. And when you look at most so-called, multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud or increasingly a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So if you want to call it multi-cloud 2.0, that's fine, but we chose to call it supercloud. Okay, so at this point you may be asking, well isn't PaaS already a version of supercloud? And again, we would say no, that supercloud and its corresponding superPaaS layer which is a prerequisite, gives the freedom to store, process and manage, and secure, and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that supercloud and will vary by each offering. Your OpenShift, for example, can be used to construct a superPaaS, but in and of itself, isn't a superPaaS, it's generic. A superPaaS might be developed to support, for instance, ultra low latency database work. It would unlikely again, taking the OpenShift example, it's unlikely that off-the-shelf OpenShift would be used to develop such a low latency superPaaS layer for ultra low latency database work. The point is supercloud and its inherent superPaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup and recovery for data protection, and ransomware, or data sharing, or data governance. Highly specific use cases that the supercloud is designed to solve for. Okay, another question we often get is who has a supercloud today and who's building a supercloud, and who are the contenders? Well, most companies that consider themselves cloud players will, we believe, be building or are building superclouds. Here's a common ETR graphic that we like to show with Net Score or spending momentum on the Y axis and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the supercloud mix, and we've included the hyperscalers because they are enablers. Now remember, this is a spectrum of maturity it's a maturity model and we've added some of those industry players that we see building superclouds like CapitalOne, Goldman Sachs, Walmart. This is in deference to Moschella's observation around The Matrix and the industry structural changes that are going on. This goes back to every company, being a software company and rather than pattern match an outdated SaaS model, we see new industry structures emerging where software and data, and tools, specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve, and the hyperscalers aren't going to solve. You know, we've talked a lot about Snowflake's data cloud as an example of supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross-cloud services you know, perhaps creating a new category. Basically, every large company we see either pursuing supercloud initiatives or thinking about it. Dell showed project Alpine at Dell Tech World, that's a supercloud. Snowflake introducing a new application development capability based on their superPaaS, our term of course, they don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms, but then we talked to HPE's Head of Storage Services, Omer Asad is clearly headed in the direction that we would consider supercloud. Again, those cross-cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of companies, smaller companies like Aviatrix and Starburst, and Clumio and others that are building versions of superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem specifically, around data as part of their and their customers digital transformations. So yeah, pretty much every tech vendor with any size or momentum and new industry players are coming out of hiding, and competing. Building superclouds that look a lot like Moschella's Matrix, with machine intelligence and blockchains, and virtual realities, and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past, but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in superclouds and what are some examples? Let's start with analytics. Our favorite example is Snowflake, it's one of the furthest along with its data cloud, in our view. It's a supercloud optimized for data sharing and governance, query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift, You can't do this with SQL server and they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data, and bringing open source tooling with things like Apache Iceberg. And so it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix doing it, coming at it from a data science perspective, trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with ARM-based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at MongoDB, a very developer-friendly platform that with the Atlas is moving toward a supercloud model running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into to play. Very clearly, there's a need to create a common operating environment across clouds and on-prem, and out to the edge. And I say VMware is hard at work on that. Managing and moving workloads, and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds, industry workloads. We see CapitalOne, it announced its cost optimization platform for Snowflake, piggybacking on Snowflake supercloud or super data cloud. And in our view, it's very clearly going to go after other markets is going to test it out with Snowflake, running, optimizing on AWS and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a supercloud. You know, we've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And we can bet dollars to donuts that Oracle will be building a supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I, have decided to host an event in Palo Alto, we're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, supercloud, hypercloud, all welcome. So theCUBE on Supercloud is coming on August 9th, out of our Palo Alto studios, we'll be running a live program on the topic. We've reached out to a number of industry participants, VMware, Snowflake, Confluent, Sky High Security, Gee Rittenhouse's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for Breaking Analysis. And I want to thank Kristen Martin and Cheryl Knight, they help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. It publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me @DVellante, or comment on my LinkedIn post. And please do check out ETR.ai for the best survey data. And the enterprise tech business will be at AWS NYC Summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE, it's at the Javits Center. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (bright music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vallante. >> Welcome to this week's Wikibon CUBE Insights powered by ETR. As we exited the isolation economy last year, Supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this "Breaking Analysis," we address the 10 most frequently asked questions we get around Supercloud. Okay, let's review these frequently asked questions on Supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out Superclouds? We'll try to answer why the term Supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that Superclouds solve specifically, and we'll further define the critical aspects of a Supercloud architecture. We often get asked, "Isn't this just multi-cloud?" Well, we don't think so, and we'll explain why in this "Breaking Analysis." Now, in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building Superclouds? What workloads and services will run on Superclouds? And eight A or number nine, what are some examples that we can share of Supercloud? And finally, we'll answer what you can expect next from us on Supercloud. Okay, let's get started. Why do we need another buzzword? Well, late last year ahead of re:Invent, we were inspired by a post from Jerry Chen called castles in the cloud. Now, in that blog post, he introduced the idea that there were submarkets emerging in cloud that presented opportunities for investors and entrepreneurs. That the cloud wasn't going to suck the hyperscalers, weren't going to suck all the value out of the industry. And so we introduced this notion of Supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now, it turns out that we weren't the only ones using the term, as both Cornell and MIT, have used the phrase in somewhat similar, but different contexts. The point is, something new was happening in the AWS and other ecosystems. It was more than IS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services, to solve new problems that the cloud vendors, in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level. The Supercloud metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted. Love it or hate it, it's memorable and it's what we chose. Now, to that last point about structural industry transformation. Andy Rapaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC analyst who first introduced the concept in 1987, four years before Rapaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel. That's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of the matrix that's shown on the right hand side of this chart. Moschella posited that new services were emerging, built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term matrix, because the conceptual depiction included, not only horizontal technology rows, like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that, whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D and production and manufacturing and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple and payments, and content and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And Supercloud is meant to imply more than running in hyperscale clouds. Rather, it's the combination of multiple technologies, enabled by cloud scale with new industry participants from those verticals; financial services, and healthcare, and manufacturing, energy, media, and virtually all and any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or Supercloud. And we'll come back to that. Let's first address what's different about Superclouds relative to hyperscale clouds. Now, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud. So they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc and Google Antos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, costs, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And, of course, the less margin that's left for them to capture. Will the hyperscalers get more serious about cross cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They have a long way to go, a lot of runway. So let's talk about specifically, what problems Superclouds solve. We've all seen the stats from IDC or Gartner or whomever, that customers on average use more than one cloud, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem, because each cloud requires different skills, because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data. It's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations, and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out Superclouds that solve really specific and hard problems and create differential value. Okay, let's dig a bit more into the architectural aspects of Supercloud. In other words, what are the salient attributes of Supercloud? So, first and foremost, a Supercloud runs a set of specific services designed to solve a unique problem, and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, Supercloud might be optimized for lowest cost or lowest latency or sharing data or governing or securing that data or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A Supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud, and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in the most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery or data sovereignty, or whatever unique value that Supercloud is delivering for the specific use case in their domain. And a Supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the Supercloud platform to fill gaps, accelerate features, and of course, innovate. The services can be infrastructure related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on premises. Okay, so another common question we get is, "Isn't that just multi-cloud?" And what we'd say to that is yeah, "Yes, but no." You can call it multi-cloud 2.0, if you want. If you want to use, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud, by design, is different than multi-cloud by default. Meaning, to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A. You buy a company and they happen to use Google cloud. And so you bring it in. And when you look at most so-called multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud. Or increasingly, a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud, with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So, if you want to call it multi-cloud 2.0, that's fine, but we chose to call it Supercloud. Okay, so at this point you may be asking, "Well isn't PaaS already a version of Supercloud?" And again, we would say, "No." That Supercloud and its corresponding super PaaS layer, which is a prerequisite, gives the freedom to store, process, and manage and secure and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that Supercloud and will vary by each offering. OpenShift, for example, can be used to construct a super PaaS, but in and of itself, isn't a super PaaS, it's generic. A super PaaS might be developed to support, for instance, ultra low latency database work. It would unlikely, again, taking the OpenShift example, it's unlikely that off the shelf OpenShift would be used to develop such a low latency, super PaaS layer for ultra low latency database work. The point is, Supercloud and its inherent super PaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup in recovery for data protection and ransomware, or data sharing or data governance. Highly specific use cases that the Supercloud is designed to solve for. Okay, another question we often get is, "Who has a Supercloud today and who's building a Supercloud and who are the contenders?" Well, most companies that consider themselves cloud players will, we believe, be building or are building Superclouds. Here's a common ETR graphic that we like to show with net score or spending momentum on the Y axis, and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the Supercloud mix. And we've included the hyperscalers because they are enablers. Now, remember, this is a spectrum of maturity. It's a maturity model. And we've added some of those industry players that we see building Superclouds like Capital One, Goldman Sachs, Walmart. This is in deference to Moschella's observation around the matrix and the industry structural changes that are going on. This goes back to every company being a software company. And rather than pattern match and outdated SaaS model, we see new industry structures emerging where software and data and tools specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve. And the hyperscalers aren't going to solve. We've talked a lot about Snowflake's data cloud as an example of Supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross cloud services, perhaps creating a new category. Basically, every large company we see either pursuing Supercloud initiatives or thinking about it. Dell showed Project Alpine at Dell Tech World. That's a Supercloud. Snowflake introducing a new application development capability based on their super PaaS, our term, of course. They don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms. (Dave laughing) But then we talked to HPE's head of storage services, Omer Asad, and he's clearly headed in the direction that we would consider Supercloud. Again, those cross cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of smaller companies like Aviatrix and Starburst and Clumio and others that are building versions of Superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem, specifically around data as part of their and their customer's digital transformations. So yeah, pretty much every tech vendor with any size or momentum, and new industry players are coming out of hiding and competing, building Superclouds that look a lot like Moschella's matrix, with machine intelligence and blockchains and virtual realities and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in Superclouds and what are some examples? Let's start with analytics. Our favorite example of Snowflake. It's one of the furthest along with its data cloud, in our view. It's a Supercloud optimized for data sharing and governance, and query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift. You can't do this with SQL server. And they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data and bringing open source tooling with things like Apache Iceberg. And so, it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix, doing it, coming at it from a data science perspective trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with arm based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at Mongo DB. A very developer friendly platform that where the Atlas is moving toward a Supercloud model, running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into play. Very clearly, there's a need to create a common operating environment across clouds and on-prem and out to the edge. And I say, VMware is hard at work on that, managing and moving workloads and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds. Industry workloads, we see Capital One. It announced its cost optimization platform for Snowflake, piggybacking on Snowflake's Supercloud or super data cloud. And in our view, it's very clearly going to go after other markets. It's going to test it out with Snowflake, optimizing on AWS, and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a Supercloud. We've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And you can bet dollars to donuts that Oracle will be building a Supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers, it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I have decided to host an event in Palo Alto. We're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, Supercloud, HyperCloud, all welcome. So theCUBE on Supercloud is coming on August 9th out of our Palo Alto studios. We'll be running a live program on the topic. We've reached out to a number of industry participants; VMware, Snowflake, Confluent, Skyhigh Security, G. Written House's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion, and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for "Breaking Analysis." And I want to thank Kristen Martin and Cheryl Knight. They help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search, breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me directly at david.vellante@siliconangle.com. Or DM me @DVallante, or comment on my LinkedIn post. And please, do check out etr.ai for the best survey data in the enterprise tech business. We'll be at AWS NYC summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE. It's at the Javits Center. This is Dave Vallante for theCUBE Insights, powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (slow music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> Moore's Law is dead, right? Think again. Massive improvements in processing power combined with data and AI will completely change the way we think about designing hardware, writing software and applying technology to businesses. Every industry will be disrupted. You hear that all the time. Well, it's absolutely true and we're going to explain why and what it all means. Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we're going to unveil some new data that suggests we're entering a new era of innovation that will be powered by cheap processing capabilities that AI will exploit. We'll also tell you where the new bottlenecks will emerge and what this means for system architectures and industry transformations in the coming decade. Moore's Law is dead, you say? We must have heard that hundreds, if not, thousands of times in the past decade. EE Times has written about it, MIT Technology Review, CNET, and even industry associations that have lived by Moore's Law. But our friend Patrick Moorhead got it right when he said, "Moore's Law, by the strictest definition of doubling chip densities every two years, isn't happening anymore." And you know what, that's true. He's absolutely correct. And he couched that statement by saying by the strict definition. And he did that for a reason, because he's smart enough to know that the chip industry are masters at doing work arounds. Here's proof that the death of Moore's Law by its strictest definition is largely irrelevant. My colleague, David Foyer and I were hard at work this week and here's the result. The fact is that the historical outcome of Moore's Law is actually accelerating and in quite dramatically. This graphic digs into the progression of Apple's SoC, system on chip developments from the A9 and culminating with the A14, 15 nanometer bionic system on a chip. The vertical axis shows operations per second and the horizontal axis shows time for three processor types. The CPU which we measure here in terahertz, that's the blue line which you can't even hardly see, the GPU which is the orange that's measured in trillions of floating point operations per second and then the NPU, the neural processing unit and that's measured in trillions of operations per second which is that exploding gray area. Now, historically, we always rushed out to buy the latest and greatest PC, because the newer models had faster cycles or more gigahertz. Moore's Law would double that performance every 24 months. Now that equates to about 40% annually. CPU performance is now moderated. That growth is now down to roughly 30% annual improvements. So technically speaking, Moore's Law as we know it was dead. But combined, if you look at the improvements in Apple's SoC since 2015, they've been on a pace that's higher than 118% annually. And it's even higher than that, because the actual figure for these three processor types we're not even counting the impact of DSPs and accelerator components of Apple system on a chip. It would push this even higher. Apple's A14 which is shown in the right hand side here is quite amazing. It's got a 64 bit architecture, it's got many, many cores. It's got a number of alternative processor types. But the important thing is what you can do with all this processing power. In an iPhone, the types of AI that we show here that continue to evolve, facial recognition, speech, natural language processing, rendering videos, helping the hearing impaired and eventually bringing augmented reality to the palm of your hand. It's quite incredible. So what does this mean for other parts of the IT stack? Well, we recently reported Satya Nadella's epic quote that "We've now reached peak centralization." So this graphic paints a picture that was quite telling. We just shared the processing powers exploding. The costs consequently are dropping like a rock. Apple's A14 cost the company approximately 50 bucks per chip. Arm at its v9 announcement said that it will have chips that can go into refrigerators. These chips are going to optimize energy usage and save 10% annually on your power consumption. They said, this chip will cost a buck, a dollar to shave 10% of your refrigerator electricity bill. It's just astounding. But look at where the expensive bottlenecks are, it's networks and it's storage. So what does this mean? Well, it means the processing is going to get pushed to the edge, i.e., wherever the data is born. Storage and networking are going to become increasingly distributed and decentralized. Now with custom silicon and all that processing power placed throughout the system, an AI is going to be embedded into software, into hardware and it's going to optimize a workloads for latency, performance, bandwidth, and security. And remember, most of that data, 99% is going to stay at the edge. And we love to use Tesla as an example. The vast majority of data that a Tesla car creates is never going to go back to the cloud. Most of it doesn't even get persisted. I think Tesla saves like five minutes of data. But some data will connect occasionally back to the cloud to train AI models and we're going to come back to that. But this picture says if you're a hardware company, you'd better start thinking about how to take advantage of that blue line that's exploding, Cisco. Cisco is already designing its own chips. But Dell, HPE, who kind of does maybe used to do a lot of its own custom silicon, but Pure Storage, NetApp, I mean, the list goes on and on and on either you're going to get start designing custom silicon or you're going to get disrupted in our view. AWS, Google and Microsoft are all doing it for a reason as is IBM and to Sarbjeet Johal said recently this is not your grandfather's semiconductor business. And if you're a software engineer, you're going to be writing applications that take advantage of all the data being collected and bringing to bear this processing power that we're talking about to create new capabilities like we've never seen it before. So let's get into that a little bit and dig into AI. You can think of AI as the superset. Just as an aside, interestingly in his book, "Seeing Digital", author David Moschella says, there's nothing artificial about this. He uses the term machine intelligence, instead of artificial intelligence and says that there's nothing artificial about machine intelligence just like there's nothing artificial about the strength of a tractor. It's a nuance, but it's kind of interesting, nonetheless, words matter. We hear a lot about machine learning and deep learning and think of them as subsets of AI. Machine learning applies algorithms and code to data to get "smarter", make better models, for example, that can lead to augmented intelligence and help humans make better decisions. These models improve as they get more data and are iterated over time. Now deep learning is a more advanced type of machine learning. It uses more complex math. But the point that we want to make here is that today much of the activity in AI is around building and training models. And this is mostly happening in the cloud. But we think AI inference will bring the most exciting innovations in the coming years. Inference is the deployment of that model that we were just talking about, taking real time data from sensors, processing that data locally and then applying that training that has been developed in the cloud and making micro adjustments in real time. So let's take an example. Again, we love Tesla examples. Think about an algorithm that optimizes the performance and safety of a car on a turn, the model take data on friction, road condition, angles of the tires, the tire wear, the tire pressure, all this data, and it keeps testing and iterating, testing and iterating, testing iterating that model until it's ready to be deployed. And then the intelligence, all this intelligence goes into an inference engine which is a chip that goes into a car and gets data from sensors and makes these micro adjustments in real time on steering and braking and the like. Now, as you said before, Tesla persist the data for very short time, because there's so much of it. It just can't push it back to the cloud. But it can now ever selectively store certain data if it needs to, and then send back that data to the cloud to further train them all. Let's say for instance, an animal runs into the road during slick conditions, Tesla wants to grab that data, because they notice that there's a lot of accidents in New England in certain months. And maybe Tesla takes that snapshot and sends it back to the cloud and combines it with other data and maybe other parts of the country or other regions of New England and it perfects that model further to improve safety. This is just one example of thousands and thousands that are going to further develop in the coming decade. I want to talk about how we see this evolving over time. Inference is where we think the value is. That's where the rubber meets the road, so to speak, based on the previous example. Now this conceptual chart shows the percent of spend over time on modeling versus inference. And you can see some of the applications that get attention today and how these applications will mature over time as inference becomes more and more mainstream, the opportunities for AI inference at the edge and in IOT are enormous. And we think that over time, 95% of that spending is going to go to inference where it's probably only 5% today. Now today's modeling workloads are pretty prevalent and things like fraud, adtech, weather, pricing, recommendation engines, and those kinds of things, and now those will keep getting better and better and better over time. Now in the middle here, we show the industries which are all going to be transformed by these trends. Now, one of the point that Moschella had made in his book, he kind of explains why historically vertically industries are pretty stovepiped, they have their own stack, sales and marketing and engineering and supply chains, et cetera, and experts within those industries tend to stay within those industries and they're largely insulated from disruption from other industries, maybe unless they were part of a supply chain. But today, you see all kinds of cross industry activity. Amazon entering grocery, entering media. Apple in finance and potentially getting into EV. Tesla, eyeing insurance. There are many, many, many examples of tech giants who are crossing traditional industry boundaries. And the reason is because of data. They have the data. And they're applying machine intelligence to that data and improving. Auto manufacturers, for example, over time they're going to have better data than insurance companies. DeFi, decentralized finance platforms going to use the blockchain and they're continuing to improve. Blockchain today is not great performance, it's very overhead intensive all that encryption. But as they take advantage of this new processing power and better software and AI, it could very well disrupt traditional payment systems. And again, so many examples here. But what I want to do now is dig into enterprise AI a bit. And just a quick reminder, we showed this last week in our Armv9 post. This is data from ETR. The vertical axis is net score. That's a measure of spending momentum. The horizontal axis is market share or pervasiveness in the dataset. The red line at 40% is like a subjective anchor that we use. Anything above 40% we think is really good. Machine learning and AI is the number one area of spending velocity and has been for awhile. RPA is right there. Very frankly, it's an adjacency to AI and you could even argue. So it's cloud where all the ML action is taking place today. But that will change, we think, as we just described, because data's going to get pushed to the edge. And this chart will show you some of the vendors in that space. These are the companies that CIOs and IT buyers associate with their AI and machine learning spend. So it's the same XY graph, spending velocity by market share on the horizontal axis. Microsoft, AWS, Google, of course, the big cloud guys they dominate AI and machine learning. Facebook's not on here. Facebook's got great AI as well, but it's not enterprise tech spending. These cloud companies they have the tooling, they have the data, they have the scale and as we said, lots of modeling is going on today, but this is going to increasingly be pushed into remote AI inference engines that will have massive processing capabilities collectively. So we're moving away from that peak centralization as Satya Nadella described. You see Databricks on here. They're seen as an AI leader. SparkCognition, they're off the charts, literally, in the upper left. They have extremely high net score albeit with a small sample. They apply machine learning to massive data sets. DataRobot does automated AI. They're super high in the y-axis. Dataiku, they help create machine learning based apps. C3.ai, you're hearing a lot more about them. Tom Siebel's involved in that company. It's an enterprise AI firm, hear a lot of ads now doing AI and responsible way really kind of enterprise AI that's sort of always been IBM. IBM Watson's calling card. There's SAP with Leonardo. Salesforce with Einstein. Again, IBM Watson is right there just at the 40% line. You see Oracle is there as well. They're embedding automated and tele or machine intelligence with their self-driving database they call it that sort of machine intelligence in the database. You see Adobe there. So a lot of typical enterprise company names. And the point is that these software companies they're all embedding AI into their offerings. So if you're an incumbent company and you're trying not to get disrupted, the good news is you can buy AI from these software companies. You don't have to build it. You don't have to be an expert at AI. The hard part is going to be how and where to apply AI. And the simplest answer there is follow the data. There's so much more to the story, but we just have to leave it there for now and I want to summarize. We have been pounding the table that the post x86 era is here. It's a function of volume. Arm volumes are a way for volumes are 10X those of x86. Pat Gelsinger understands this. That's why he made that big announcement. He's trying to transform the company. The importance of volume in terms of lowering the cost of semiconductors it can't be understated. And today, we've quantified something that we haven't really seen much of and really haven't seen before. And that's that the actual performance improvements that we're seeing in processing today are far outstripping anything we've seen before, forget Moore's Law being dead that's irrelevant. The original finding is being blown away this decade and who knows with quantum computing what the future holds. This is a fundamental enabler of AI applications. And this is most often the case the innovation is coming from the consumer use cases first. Apple continues to lead the way. And Apple's integrated hardware and software model we think increasingly is going to move into the enterprise mindset. Clearly the cloud vendors are moving in this direction, building their own custom silicon and doing really that deep integration. You see this with Oracle who kind of really a good example of the iPhone for the enterprise, if you will. It just makes sense that optimizing hardware and software together is going to gain momentum, because there's so much opportunity for customization in chips as we discussed last week with Arm's announcement, especially with the diversity of edge use cases. And it's the direction that Pat Gelsinger is taking Intel trying to provide more flexibility. One aside, Pat Gelsinger he may face massive challenges that we laid out a couple of posts ago with our Intel breaking analysis, but he is right on in our view that semiconductor demand is increasing. There's no end in sight. We don't think we're going to see these ebbs and flows as we've seen in the past that these boom and bust cycles for semiconductor. We just think that prices are coming down. The market's elastic and the market is absolutely exploding with huge demand for fab capacity. Now, if you're an enterprise, you should not stress about and trying to invent AI, rather you should put your focus on understanding what data gives you competitive advantage and how to apply machine intelligence and AI to win. You're going to be buying, not building AI and you're going to be applying it. Now data as John Furrier has said in the past is becoming the new development kit. He said that 10 years ago and he seems right. Finally, if you're an enterprise hardware player, you're going to be designing your own chips and writing more software to exploit AI. You'll be embedding custom silicon in AI throughout your product portfolio and storage and networking and you'll be increasingly bringing compute to the data. And that data will mostly stay where it's created. Again, systems and storage and networking stacks they're all being completely re-imagined. If you're a software developer, you now have processing capabilities in the palm of your hand that are incredible. And you're going to rewriting new applications to take advantage of this and use AI to change the world, literally. You'll have to figure out how to get access to the most relevant data. You have to figure out how to secure your platforms and innovate. And if you're a services company, your opportunity is to help customers that are trying not to get disrupted are many. You have the deep industry expertise and horizontal technology chops to help customers survive and thrive. Privacy? AI for good? Yeah well, that's a whole another topic. I think for now, we have to get a better understanding of how far AI can go before we determine how far it should go. Look, protecting our personal data and privacy should definitely be something that we're concerned about and we should protect. But generally, I'd rather not stifle innovation at this point. I'd be interested in what you think about that. Okay. That's it for today. Thanks to David Foyer, who helped me with this segment again and did a lot of the charts and the data behind this. He's done some great work there. Remember these episodes are all available as podcasts wherever you listen, just search breaking it analysis podcast and please subscribe to the series. We'd appreciate that. Check out ETR's website at ETR.plus. We also publish a full report with more detail every week on Wikibon.com and siliconangle.com, so check that out. You can get in touch with me. I'm dave.vellante@siliconangle.com. You can DM me on Twitter @dvellante or comment on our LinkedIn posts. I always appreciate that. This is Dave Vellante for theCUBE Insights powered by ETR. Stay safe, be well. And we'll see you next time. (bright music)

>> From theCUBE Studios in Palo Alto, and Boston bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Microsoft CEO, Satya Nadella sees a different future for cloud computing over the coming decade. And as Microsoft Ignite keynote, he laid out the five attributes that will define the cloud in the next 10 years. His vision is a cloud platform that is decentralized, ubiquitous, intelligent, sensing, and trusted. One that actually tickles the senses and levels the playing field between consumers and creators by placing tools in the hands of more people around the world. Welcome to this week's wiki buns cube insights, powered by ETR. In this Breaking Analysis we'll review the highlights of Nadella's Ignite keynote share our thoughts on what it means for the future of cloud specifically, and the tech industry generally. We'll also give you a more tactical view of Microsoft and compare its performance within the ETR's dataset to its peers. Satya Nadella's forward-looking cloud attributes comprised five key vectors that he talked about. The first was ubiquitous and decentralized computing, Nadella made the statement that we've reached peak centralization today that we're witnessing radical changes in computing architecture from the materials used to semiconductors software, and that is going to serve a new frontier that's forming at the edge. Nadella envisions a world where there will be more sovereignty and decentralized control. We couldn't agree more. The cloud universe is expanding and the lines are blurring between what's being done on-prem, across public clouds and the cloud experience which is going to extend everywhere, including the edge. And of course, data is going to be flowing through this hyper decentralized system. Next was sovereign data and ambient intelligence. To us data sovereignty means that whatever the local laws are the system is going to have the intelligence to govern privacy, ensure data provenance, and adhere to corporate edicts. Ambient intelligence is a field of research that leverages pervasive sensor networks and AI to respond to and anticipate humans and machines. Nadella sees the future where a business logic will move from being code that is written to code that is actually learned from data, pretty interesting. He sees this autodidactic system if you will, as fundamental to tackling big problems like personalized medicine or even climate change. Third, he talked about empowered creators and communities everywhere. Nadella said, there'll be increasingly a balance between consumption and creation. His talking about an economic balance essentially he's predicting that creation will be democratized and his vision is to put tools in the hands of people to allow them to tip the scales toward knowledge workers, frontline employees, students, everyone, essentially creating content, applications, code, et cetera power to the people if you will. And underneath this vision is a new form of or emerging new forms of Silicon operating systems and entirely transformative digital experiences. Next was economic opportunity for the global workforce. So picking up on the accelerated themes of remote work that were catalyzed by COVID, Nadella emphasize that the future has to accommodate flexibility in how, when and where people work. He sees a new model of productivity emerging, not necessarily defined by corporate revenue per employee for example, but by the economic advantages that become accessible to everyone through better access to technology, collaboration tools, education, and healthy lifestyles, all enabled by this ubiquitous cloud. Finally, trust by design, Nadella said that ethical principles must govern the design, development and deployment of AI. The system he said must be secure by design with zero trust built in to protect business assets and personal privacy. So this was a big vision that Nadella put forth it, connects the dots between bits and atoms and sets up Microsoft to extend its reach well beyond office productivity tools and cloud infrastructure. He cited the Microsoft cloud as the underpinning of its future and specifically called out Teams, he mentioned 365, HoloLens 2 and the announcement of Microsoft Mesh, a new mixed reality platform. Nadella said Mesh will do for virtual reality what X-Box live did for gaming. Take the experience from single person to multi-person imagine holographic images with no screens, empowering advances in medicine, science, technology, and very importantly social interactions. Now, one of the things that we took away from his talk was this notion of Microsoft as a technology arm's dealer. No, we're not, Nadella avoided slamming the competition directly by name one statement that he made, stood out. He said, " No customer wants to be dependent on a provider that sells them technology on one end and competes with them on the other" And to us this was a direct shot at Amazon, Google and Apple. How so you ask? And what does it tell us? In his book "Seeing Digital" author David Moschella said, "that Silicon Valley broadly defined as a duel disruption agenda." What does that mean? Not only are large tech companies disrupting horizontal layers of the tech stack like compute, storage, networking, database, security, applications, and so forth. But they're also disrupting industries Amazon and media, grocery, logistics, for example. Google and Amazon on healthcare, Google and Apple on automobiles, all three in FinTech. And it's likely this is just the beginning but Nadella's posture suggests that Microsoft for now anyway, is content being mostly a horizontal technology provider, aka arms dealer. Now, there are some examples where you could argue that Microsoft sort of crosses the line maybe as a games developer or as a SAS competitor. Do you really want to, if you're a SAS player do you want to run your system on Azure and compete with Microsoft? Well, it depends if you're vertically oriented or maybe horizontal in their swim lanes, but anyway, these are more natural cohorts to technology than say for example, Amazon's retail business. So I thought that was something that was worth taking a look at. All right, let's take a quick look at how Microsoft compares to a couple of the great tech giants of the past several decades. Here's a financial snapshot of Microsoft compared to Oracle a highly profitable software company and IBM an industry legend. The first two things that jumped right out of Microsoft, size and it's growth rate. Microsoft is twice the revenue of IBM and nearly four extent of Oracle. And yet Microsoft is growing in the mid-teens compared to low single digits for Oracle and IBM continues to shrink so extensible you can grow. Microsoft's gross margin model has been pulled down by its hardware business but its operating margins are unbelievable. Meanwhile, the cash on its balance sheet is immense much larger than Oracles, which is very impressive. It's certainly dwarfs that of IBM, a company that had to take on a lot of debt to acquire Red Hat and has a balance sheet, that increasingly looks more like Dell's than it's historical self. And then on the last two rows Oracle and IBM, both owners of their own cloud have been lapped by Microsoft in terms of CapEx and research & development investment. Ironically, as we pointed out, IBM's R & D spend in 2007 the year after AWS launched the modern era of cloud was comparable to that of Microsoft. Let's now pivot it to some of the ETR survey data and see how Microsoft fares. We'll start by sharing a fundamental basis of the ETR methodology, that is the calculation of net score. Net score is a measure of spending momentum and here's how it's derived. This chart shows the components of Microsoft's net score. It comprises five parts and represents the percentage of customers within the ETR survey with specific spending profiles. The lime green is new adoptions, the forest green is increased spend of 6% or more for 2021 relative to 2020, the gray is flat spend, the pinkish slice is spend declining by more than 6% or 6% or more relative to last year and the bright red is replacing the platform. You subtract the reds from the greens and you get net score. As you can see, Microsoft's net score is 53% which is very high for $150 billion Company. Now let's put that in context and expand the scope here a little bit. This chart shows how Microsoft fares relative to its peers, the vertical axis shows net score against spending velocity and the horizontal axis shows market share. Market share measures pervasiveness in the survey. In the table insert, you can see the vendors they're sorted by net score and the shared end column is there as well, which represents the number of shared accounts in the dataset. On both accounts bigger is better. Now note the red dotted line, that's the 40% watermark which is my personal indicator of an elevated net score anything above that in our view is really solid. Microsoft is as usual off the charts strong well to the right with it's market presence and then an overall net score of 53% as we showed earlier. And then there's Azure, separate from Microsoft overall. We wanted to plot that specifically which of course it doesn't have the presence of Microsoft overall, no surprise, but it's still prominent on the x-axis and it has a net score approaching 70%, which is quite amazing. AWS not surprisingly is highly elevated with a presence that's even larger than Azure. And you can see Zoom, Salesforce and Google Cloud all above the 40% line. Google as we've reported is well off the pace in the horizontal axis and even though its net score is elevated, we would like to see it even higher, given its smaller size relative to AWS and Azure. You know, SAP always stands out because it's a large company and it's got a net score that's hovering just under 30%. It's not above that 40% line, but it's solid. And you can see IBM and Oracle now we're showing here IBM and Oracle overall so it's the whole kitchen sink comparable to Microsoft that turquoise dot, if you will. So you can see why those two are valued much lower Microsoft. The large base of its business that's declining is much, much larger than the pieces of their business that are growing. Now Oracle has some momentum, the Back Aaron's article on February 19th, which declared Oracle a cloud giant and it declared its stock a buy combined with some earnings upgrades including one today from Ramo Lyncho of Barclays has catapulted the stock to all time highs and a valuation over $200 billion. IBM is a different story as we've discussed frequently Arvind has a lot of work to do to get this national treasure back to what's prominent itself. Okay, let now unpack Microsoft's vast portfolio a bit and see where it's doing well and where it's making moves and maybe where it's struggling, some. This graphic shows Microsoft's net score across its entire product portfolio within the ETR taxonomy. And you can see it's pretty much killing it across the board. Microsoft plays in almost every sector in the ETR taxonomy and you can see the 40% red line and how many of its offerings are above that line. The yellow bar being the most recent survey and while there's quite a bit of gray, i.e. flat spend relative to 2020, we're talking about some very tough compares from last year. And yet there's still a huge chunk of the portfolio in the green meaning spending momentum is actually up from last year and some of Microsoft's most important sectors like Cloud and Teams and Analytics. Look only Skype and Microsoft Dynamics are lagging, so really nice story there in our view. Now let's come back and take a look at Microsoft's cloud business specifically as compared to its peers. So Satya basically said that Microsoft's future will build on top of its cloud and looking at this picture it's pretty encouraging for the company. This chart, again, shows net score or spending momentum inside specifically Fortune 500 customers and it's a key bellwether in the ETR dataset, and you can see Azure and Azure functions well above the 40% red line and extremely well positioned relative to AWS and GCP. Importantly, the yellow bar tells us that compared to previous surveys Microsoft's cloud business is actually gaining momentum in this very important sector. Now, other notable call-outs on this chart VMware Cloud, which, it's on-prem hybrid cloud and VMware Cloud on AWS, which is reportedly doing well but off from the momentum of its highs last spring. You can see Oracle jumped up indicating cloud momentum, but still well below the performance of the largest cloud players. The IBM Cloud appears to be a non-factor in the survey and as we previously stated, we'd like to see IBM recalibrate the financials for its cloud business and come up with a reporting framework that better represents the prevailing mental model of cloud computing. We think a cleaner number would allow IBM to build on the Red Hat momentum. I'm not sure what to make of the HPE boost, it looks significant, but in digging into the data it's only 17 data points, but look 17 within the Fortune 500 companies is not terrible. And HPE net score in that sector is more than double its overall cloud net score so that's positive we think. Okay, let's wrap by looking at how customers are thinking about multi-cloud adoption and really this data that we're about to show you simply asking customers about clouds they're using versus any type of long-term vision. So it's a good representation of what's happening today and what CIO is are thinking about in the near future particularly over the next 12 months. The survey asks customers to describe their cloud provider usage and strategy. You can see that only 14% of the survey respondents have exclusively a mono-cloud strategy, but now add in another 22% who were predominantly single cloud and you now have more than a third of the customer base gravitating toward mono-cloud. Another 14% say they're concentrating cloud providers more narrowly. Now on the flip side, you've got a big group, 29% that are moving toward multi-cloud and if you add in the additional 16% who say they are and will continue to be evenly spread, 45% of the survey is solidly headed in that direction so it's a mixed picture. What's the takeaway? Well, we think Andy Jassy is right when he says that while many customers use more than one cloud, they tend to have a primary provider and have something like a 70,30 or even 80,20 split between primary and secondary clouds. Now we think, however that this will change, but only to the extent that the vendor community is adding value on top of the existing hyperscale clouds. What we're saying and have been saying is that there is a real opportunity to create value on top of the cloud infrastructure that's being built out by AWS, Google and Microsoft. Instead of fearing cloud, the vendor community should be embracing it creating a layer on top, abstracting away the underlying complexities associated with cloud native, exploiting cloud native, and then building on top of that. Snowflake's data cloud vision is right on in my view, we can envision virtually every layer of the stack following suit. Even within database there are opportunities to identify more granular segments across clouds. For example, despite Snowflakes early multi-cloud lead you're seeing competitive firms like Teradata begin to architect a system across clouds that can query data warehouses from distributed locations, including on-prem as part of what they refer to as a data fabric, sounds kind of like Snowflakes global data mesh, or maybe better Zhamak Dehghani's data mesh. Yeah, sure but Teradata has capabilities that Snowflake doesn't for example, the ability to do complex joins and we can see plenty of market for both companies to differentiate. And why shouldn't similar vision extend from on-prem, across clouds to the edge for data protection, security, governance, hybrid compute ,analytics, federated applications, its a huge market that the hyperscale providers are likely too busy worrying about their own walled gardens to start building across on top of their competitors clouds. So Dell, HPE, VMware, Cisco, Palo Alto Fortunate, Zscaler or Cohesity, Veeam and hundreds of other tech companies, including by the way IBM and Oracle should be saying thank you to AWS, Google and Microsoft for spending all that money to build out great infrastructure on which they can build value, tap for future growth. And many of you will say, Hey, we're already doing this. Okay, I'll be watching to see the ratio of real versus slideware because generally today, in my opinion the denominator is much larger than the numerator. So when that ratio hits 1X we'll know it started to become real. Okay, that's it for today remember, all these episodes are available as podcasts wherever you listen so please subscribe. I publish weekly on wikibun.com and siliconangle.com. Please comment on my LinkedIn post or you can tweet me @DVellante or feel free to email me at David.Vellante@siliconangle.com. And don't forget to check out etr.plus for all the survey and data science action. This is Dave Vellante for the Cube Insights powered by ETR. Be well, thanks for watching and we'll see you next time. (relaxing music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone and welcome this week's episode of theCUBE Insights powered by ETR. And welcome to the Trillionaire's Club. In this Breaking Analysis, I want to look at how the big tech companies have really changed the recipe for innovation in the Enterprise. And as we enter the next decade, I think it's important to sort of reset and re-look at how innovation will determine the winners and losers going forward, including not only the sellers of technology but how technology applied will set the stage for the next 50 years of economic growth. Here's the premise that I want to put forth to you. The source of innovation in the technology business has been permanently altered. There's a new cocktail of innovation, if you will, that will far surpass Moore's Law in terms of it's impact on the industry. For 50 years we've marched to the cadence of that Moore's Law, that is the doubling of transistor counts every 18 months, as shown in the left-hand side of this chart. And of course this translated as we know, into a chasing of the chips, where by being first with the latest and greatest microprocessor brought competitive advantage. We saw Moore's Law drive the PC era, the client server era, and it even powered the internet, notwithstanding the effects of Metcalfe's Law. But there's a new engine of innovation or what John Furrier calls the "Innovation Cocktail," and that's shown in the right-hand of this slide where data plus machine intelligence or AI and Cloud are combinatorial technologies that will power innovation for the next 20 plus years. 10 years of gathering big data have put us in a position to now apply AI. Data is plentiful but insights are not and AI unlocks those insights. The Cloud brings three things, agility, scale, and the ability to fail quickly and cheaply. So, it's these three elements and how they are packaged and applied that will in my view determine winners and losers in the next decade and beyond. Now why is this era now suddenly upon us? Well I would argue there are three main factors. One is cheap storage and compute combined with alternative processor types, like GPUs that can power AI. And the era of data is here to stay. This next chart from Dave Moschella's book, "Seeing Digital," really underscores this point. Incumbent organizations born in the last century organized largely around human expertise or processes or hard assets like factories. These were the engines of competitive advantage. But today's successful organizations put data at the core. They live by the mantra of data driven. It is foundational to them. And they organize expertise, processes and people around the data. All you got to do to drive this point home is look at the market caps of the top five public companies in the U.S. Stock Market, Apple, Microsoft, Google, Amazon, and Facebook. I call this chart the Cuatro Comas! as a shout out to Russ Hanneman, the crazy billionaire supporting, was a supporting character in the Silicon Valley series. Now each of these companies, with the exception of Facebook, has hit the trillion dollar club. AWS, like Mr. Hanneman, hit the trillion dollar club status back in September 2018 but fell back down and lost a comma. These five data-driven companies have surpassed big oil and big finance. I mean, the next closest company is Berkshire at 566 billion. And I would argue that if it hadn't been for the fake news scandal, Facebook probably would be right there with these others. Now, with the exception of Apple, these companies, they're not highly valued because of the goods they pump out, rather, and I would argue even in the case of Apple, their highly valued because they're leaders in digital and in the best position to apply machine intelligence to massive stores of data that they've collected. And they have massive scale, thanks to the Cloud. Now, I get that the success of some of these companies is largely driven by the consumer but the consumerization of IT makes this even more relevant, in my opinion. Let's bring in some ETR data to see how this translates into the Enterprise tech world. This chart shows market share from Microsoft, AWS, Apple iPhone, and Google in the Enterprise all the way back to 2010. Now I get that the iPhone is a bit of a stretch here but stick with me. Remember, market share in ETR terms is a measure of pervasiveness in the data set. Look at how Microsoft has held it's ground. And you can see the steady rise of AWS and Google. Now if I superimpose traditional Enterprise players like Cisco, IBM, or Hewlett or even Dell, that is companies that aren't competing with data at the core of their business, you would see a steady decline. I am required to black out January 2020 as you probably remember, but that data will be out soon and made public shortly after ETR exits its self-imposed quiet period. Now Apple iPhone is not a great proxy but Apple, they're not an Enterprise tech company, but it's data that I can show but now I would argue again that Apple's real value and a key determinate of their success going forward, lies in how it uses data and applies machine intelligence at scale over the next decade to compete in apps and digital services, content, and other adjacencies. And I would say these five leaders and virtually any company in the next decade, this applies. Look, digital means data and digital businesses are data driven. Data changes how we think about competition. Just look at Amazon's moves in content, grocery, logistics. Look at Google in automobiles, Apple and Amazon in music. You know, interestingly Microsoft positions this as a competitive advantage, especially in retail. For instance, touting Walmart as a partner, not a competitor, a la Amazon. The point is, that digital data, AI, and Cloud bring forth highly disruptive possibilities and are enabling these giants to enter businesses that previously were insulated from the outsiders. And in the case of the Cloud, it's paying the way. Just look at the data from Amazon. The left bar shows Amazon's revenue. AWS represents only 12% of the total company's turnover. But as you can see on the right-hand side, it accounts for almost half of the company's operating income. So, the Cloud is essentially funding Amazon's entrance into all these other businesses and powering its scale. Now let's bring in some ETR data to show what's happening in the Enterprise in the terms of share shifts. This chart is a double-Y axis that shows spending levels on the left-hand side, represented by the bars, and the average change in spending, represented by the dots. Focus for a second on the dots and the percentages. Container orchestrations at 29% change. Container platforms at 19.7%. These are Cloud-native technologies and customers are voting with their wallets. Machine learning and AI, nearly 18% change. Cloud computing itself still in the 16% range, 10 plus years on. Look at analytics and big data in the double digits still, 10 years into the big data movement. So, you can see the ETR data shows that the spending action is in and around Cloud, AI, and data. And in the red, look at the Moore's Law techs like servers and storage. Now, this isn't to say that those go away. I fully understand you need servers, and storage, and networking, and database, and software to power the Cloud but this data shows that right now, these discreet cocktail technologies are gaining spending momentum. So, the question I want to leave you with is, what does this mean for incumbents? Those that are not digital-natives or not born in the Cloud? Well, the first thing I'd point out is that while the trillionaires, they look invincible today, history suggests that they are not invulnerable. The rise of China, India, open-source, peer-to-peer models, open models, could coalesce and disrupt these big guys if they miss a step or a cycle. The second point I would make is that incumbents are often too complacent. More often than not, in my experience, there is complacency and there will be a fallout. I hear a lot of lip service given to digital and data driven but often I see companies that talk the talk but they don't walk the walk. Change will come and the incumbents will be disrupted and that is going to cause action at the top. The good news is that the incumbents, they don't have to build the tech. They can compete with the disruptors by applying machine intelligence to their unique data sets and they can buy technologies like AI and the Cloud from suppliers. The degree to which they are comfortable buying from these supplies, who may also be competitors, will play out over time but I would argue that building that competitive advantage sooner rather than later with data and learning to apply machine intelligence and AI to their unique businesses, will allow them to thrive and protect their existing businesses and grow. These markets are large and the incumbents have inherent advantages in terms of resources, relationships, brand value, customer affinity, and domain knowledge that if they apply and transform from the top with strong leadership, they will do very, very well in my view. This is Dave Vellante signing out from this latest episode of theCUBE Insights powered by ETR. Thanks for watching everybody. We'll see you next time and please feel free to comment. In my LinkedIn, you can DM me @dvellante and don't forget we turned this into a podcast so check that out at your favorite podcast player. Thanks again.

(techno music) >> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to MIT CDOIQ. The CDO Information Quality Conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host, Paul Gillin. This is our day two of our two day coverage. Jean Ross is here. She's the principle research scientist at MIT CISR, Jean good to see you again. >> Nice to be here! >> Welcome back. Okay, what do all these acronyms stand for, I forget. MIT CISR. >> CISR which we pronounce scissor, is the Center for Information Systems Research. It's a research center that's been at MIT since 1974, studying how big companies use technology effectively. >> So and, what's your role as a research scientist? >> As a research scientist, I work with both researchers and with company leaders to understand what's going on out there, and try to present some simple succinct ideas about how companies can generate greater value from information technology. >> Well, I guess not much has changed in information technology since 1974. (laughing) So let's fast forward to the big, hot trend, digital transformation, digital business. What's the difference between a business and a digital business? >> Right now, you're hoping there's no difference for you and your business. >> (chuckling) Yeah, for sure. >> The main thing about a digital business is it's being inspired by technology. So in the past, we would establish a strategy, and then we would check out technology and say, okay, how can technology make us more effective with that strategy? Today, and this has been driven a lot by start-ups, we have to stop and say, well wait a minute, what is technology making possible? Because if we're not thinking about it, there sure are a lot of students at MIT who are, and we're going to miss the boat. We're going to get Ubered if you will, somebody's going to think of a value proposition that we should be offering and aren't, and we'll be left in the dust. So, our digital businesses are those that are recognizing the opportunities that digital technologies make possible. >> Now, and what about data? In terms of the role of digital business, it seems like that's an underpinning of a digital business. Is it not? >> Yeah, the single biggest capability that digital technologies provide, is ubiquitous data that's readily accessible anytime. So when we think about being inspired by technology, we could reframe that as inspired by the availability of ubiquitous data that's readily accessible. >> Your premise about the difference between digitization and digital business is interesting. It's more than just a sematic debate. Do companies now, when companies talk about digital transformation these days, in fact, are most of them of thinking of digitization rather than really transformative business change? >> Yeah, this is so interesting to me. In 2006, we wrote a book that said, you need to become more agile, and you need to rely on information technology to get you there. And these are basic things like SAP and salesforce.com and things like that. Just making sure that your core processes are disciplined and reliable and predictable. We said this in 2006. What we didn't know is that we were explaining digitization, which is very effective use of technology in your underlying process. Today, when somebody says to me, we're going digital, I'm thinking about the new value propositions, the implications of the data, right? And they're often actually saying they're finally doing what we thought they should do in 2006. The problem is, in 2006, we said get going on this, it's a long journey. This could take you six, 10 years to accomplish. And then we gave examples of companies that took six to 10 years. LEGO, and USAA and really great companies. And now, companies are going, "Ah, you know, we really ought to do that". They don't have six to 10 years. They get this done now, or they're in trouble, and it's still a really big deal. >> So how realistic is it? I mean, you've got big established companies that have got all these information silos, as we've been hearing for the last two days, just pulling their information together, knowing what they've got is a huge challenge for them. Meanwhile, you're competing with born on the web, digitally native start-ups that don't have any of that legacy, is it really feasible for these companies to reinvent themselves in the way you're talking about? Or should they just be buying the companies that have already done it? >> Well good luck with buying, because what happens is that when a company starts up, they can do anything, but they can't do it to scale. So most of these start-ups are going to have to sell themselves because they don't know anything about scale. And the problem is, the companies that want to buy them up know about the scale of big global companies but they don't know how to do this seamlessly because they didn't do the basic digitization. They relied on basically, a lot of heroes in their company to pull of the scale. So now they have to rely more on technology than they did in the past, but they still have a leg up if you will, on the start-up that doesn't want to worry about the discipline of scaling up a good idea. They'd rather just go off and have another good idea, right? They're perpetual entrepreneurs if you will. So if we look at the start-ups, they're not really your concern. Your concern is the very well run company, that's been around, knows how to be inspired by technology and now says, "Oh I see what you're capable of doing, "or should be capable of doing. "I think I'll move into your space". So this, the Amazon's, and the USAA's and the LEGO's who say "We're good at what we do, "and we could be doing more". We're watching Schneider Electric, Phillips's, Ferovial. These are big ole companies who get digital, and they are going to start moving into a lot of people's territory. >> So let's take the example of those incumbents that you've used as examples of companies that are leaning into digital, and presumably doing a good job of it, they've got a lot of legacy debt, as you know people call it technical debt. The question I have is how they're using machine intelligence. So if you think about Facebook, Amazon, Microsoft, Google, they own horizontal technologies around machine intelligence. The incumbents that you mentioned, do not. Now do they close the gap? They're not going to build their own A.I. They're going to buy it, and then apply it. It's how they apply it that's going to be the difference. So do you agree with that premise, and where are they getting it, do they have the skill sets to do it, how are they closing that gap? >> They're definitely partnering. When you say they're not going to build any of it, that's actually not quite true. They're going to build a lot around the edges. They'll rely on partners like Microsoft and Google to provide some of the core, >> Yes, right. >> But they are bringing in their own experts to take it to the, basically to the customer level. How do I take, let me just take Schneider Electric for an example. They have gone from being an electrical equipment manufacturer, to a purveyor of energy management solutions. It's quite a different value proposition. To do that, they need a lot of intelligence. Some of it is data analytics of old, and some of it is just better representation on dashboards and things like that. But there is a layer of intelligence that is new, and it is absolutely essential to them by relying on partners and their own expertise in what they do for customers, and then co-creating a fair amount with customers, they can do things that other companies cannot. >> And they're developing a software presumably, a SAS revenue stream as part of that, right? >> Yeah, absolutely. >> How about the innovators dilemma though, the problem that these companies often have grown up, they're very big, they're very profitable, they see disruption coming, but they are unable to make the change, their shareholders won't let them make the change, they know what they have to do, but they're simply not able to do it, and then they become paralyzed. Is there a -- I mean, looking at some of the companies you just mentioned, how did they get over that mindset? >> This is real leadership from CEO's, who basically explain to their boards and to their investors, this is our future, we are... we're either going this direction or we're going down. And they sell it. It's brilliant salesmanship, and it's why when we go out to study great companies, we don't have that many to choose from. I mean, they are hard to find, right? So you are at such a competitive advantage right now. If you understand, if your own internal processes are cleaned up and you know how to rely on the E.R.P's and the C.R.M's, to get that done, and on the other hand, you're using the intelligence to provide value propositions, that new technologies and data make possible, that is an incredibly powerful combination, but you have to invest. You have to convince your boards and your investors that it's a good idea, you have to change your talent internally, and the biggest surprise is, you have to convince your customers that they want something from you that they never wanted before. So you got a lot of work to do to pull this off. >> Right now, in today's economy, the economy is sort of lifting all boats. But as we saw when the .com implosion happened in 2001, often these breakdown gives birth to great, new companies. Do you see that the next recession, which is inevitably coming, will be sort of the turning point for some of these companies that can't change? >> It's a really good question. I do expect that there are going to be companies that don't make it. And I think that they will fail at different rates based on their, not just the economy, but their industry, and what competitors do, and things like that. But I do think we're going to see some companies fail. We're going to see many other companies understand that they are too complex. They are simply too complex. They cannot do things end to end and seamlessly and present a great customer experience, because they're doing everything. So we're going to see some pretty dramatic changes, we're going to see failure, it's a fair assumption that when we see the economy crash, it's also going to contribute, but that's, it's not the whole story. >> But when the .com blew up, you had the internet guys that actually had a business model to make money, and the guys that didn't, the guys that didn't went away, and then you also had the incumbents that embrace the internet, so when we came out of that .com downturn, you had the survivors, who was Google and eBay, and obviously Amazon, and then you had incumbent companies who had online retailing, and e-tailing and e-commerce etc, who thrived. I would suspect you're going to see something similar, but I wonder what you guys think. The street today is rewarding growth. And we got another near record high today after the rate cut yesterday. And so, but companies that aren't making money are getting rewarded, 'cause they're growing. Well when the recession comes, those guys are going to get crushed. >> Right. >> Yeah. >> And you're going to have these other companies emerge, and you'll see the winners, are going to be those ones who have truly digitized, not just talking the talk, or transformed really, to use your definition. That's what I would expect. I don't know, what do you think about that? >> I totally agree. And, I mean, we look at industries like retail, and they have been fundamentally transformed. There's still lots of opportunities for innovation, and we're going to see some winners that have kind of struggled early but not given up, and they're kind of finding their footing. But we're losing some. We're losing a lot, right? I think the surprise is that we thought digital was going to replace what we did. We'd stop going to stores, we'd stop reading books, we wouldn't have newspapers anymore. And it hasn't done that. Its only added, it hasn't taken anything away. >> It could-- >> I don't think the newspaper industry has been unscathed by digital. >> No, nor has retail. >> Nor has retail, right. >> No, no no, not unscathed, but here's the big challenge. Is if I could substitute, If I could move from newspaper to online, I'm fine. You don't get to do that. You add online to what you've got, right? And I think this right now is the big challenge. Is that nothing's gone away, at least yet. So we have to sustain the business we are, so that it can feed the business we want to be. And we have to make that transition into new capabilities. I would argue that established companies need to become very binary, that there are people that do nothing but sustain and make better and better and better, who they are. While others, are creating the new reality. You see this in auto companies by the way. They're creating not just the autonomous automobiles, but the mobility services, the whole new value propositions, that will become a bigger and bigger part of their revenue stream, but right now are tiny. >> So, here's the scary thing to me. And again, I'd love to hear your thoughts on this. And I've been an outspoken critic of Liz Warren's attack on big tech. >> Absolutely. >> I just think if they're breaking the law, and they're really acting like monopolies, the D.O.J and F.T.C should do something, but to me, you don't just break up big tech because they're good capitalists. Having said that, one of the things that scares me is, when you see Apple getting into payment systems, Amazon getting into grocery and logistics. Digital allows you to do something that's never happened before which is, you can traverse industries. >> Yep. >> Yeah, absolutely >> You used to have this stack of industries, and if you were in that industry, you're stuck in healthcare, you're stuck in financial services or whatever it was. And today, digital allows you to traverse those. >> It absolutely does. And so in theory, Amazon and Apple and Facebook and Google, they can attack virtually any industry and they kind of are. >> Yeah they kind are. I would certainly not break up anything. I would really look hard though at acquisitions, because I think that's where some of this is coming from. They can stop the overwhelming growth, but I do think you're right. That you get these opportunities from digital that are just so much easier because they're basically sharing information and technology, not building buildings and equipment and all that kind of thing. But I think there all limits to all this. I do not fear these companies. I think there, we need some law, we need some regulations, they're fine. They are adding a lot of value and the great companies, I mean, you look at the Schneider's and the Phillips, yeah they fear what some of them can do, but they're looking forward to what they provide underneath. >> Doesn't Cloud change the equation here? I mean, when you think of something like Amazon getting into the payments business, or Google in the payments business, you know it used to be that the creating of global payments processing network, just going global was a huge barrier to entry. Now, you don't have nearly that same level of impediment right? I mean the cloud eliminates much of the traditional barrier. >> Yeah, but I'll tell you what limits it, is complexity. Every company we've studied gets a little over anxious and becomes too complex, and they cannot run themselves effectively anymore. It happens to everyone. I mean, remember when we were terrified about what Microsoft was going to become? But then it got competition because it's trying to do so many things, and somebody else is offering, Sales Force and others, something simpler. And this will happen to every company that gets overly ambitious. Something simpler will come along, and everybody will go "Oh thank goodness". Something simpler. >> Well with Microsoft, I would argue two things. One is the D.O.J put some handcuffs on them , and two, with Steve Ballmer, I wouldn't get his nose out of Windows, and then finally stuck on a (mumbles) (laughter) >> Well it's they had a platform shift. >> Well this is exactly it. They will make those kind of calls . >> Sure, and I think that talks to their legacy, that they won't end up like Digital Equipment Corp or Wang and D.G, who just ignored the future and held onto the past. But I think, a colleague of ours, David Moschella wrote a book, it's called "Seeing Digital". And his premise was we're moving from a world of remote cloud services, to one where you have to, to use your word, ubiquitous digital services that you can access upon which you can build your business and new business models. I mean, the simplest example is Waves, you mentioned Uber. They're using Cloud, they're using OAuth.in with Google, Facebook or LinkedIn and they've got a security layer, there's an A.I layer, there's all your BlockChain, mobile, cognitive, it's all these sets of services that are now ubiquitous on which you're building, so you're leveraging, he calls it the matrix, to the extent that these companies that you're studying, these incumbents can leverage that matrix, they should be fine. >> Yes. >> The part of the problem is, they say "No, we're going to invent everything ourselves, we're going to build it all ourselves". To use Andy Jassy's term, it's non-differentiated heavy lifting, slows them down, but there's no reason why they can't tap that matrix, >> Absolutely >> And take advantage of it. Where I do get scared is, the Facebooks, Apples, Googles, Amazons, they're matrix companies, their data is at their core, and they get this. It's not like they're putting data around the core, data is the core. So your thoughts on that? I mean, it looks like your slide about disruption, it's coming. >> Yeah, yeah, yeah, yeah. >> No industry is safe. >> Yeah, well I'll go back to the complexity argument. We studied complexity at length, and complexity is a killer. And as we get too ambitious, and we're constantly looking for growth, we start doing things that create more and more tensions in our various lines of business, causes to create silos, that then we have to coordinate. I just think every single company that, no cloud is going to save us from this. It, complexity will kill us. And we have to keep reminding ourselves to limit that complexity, and we've just not seen the example of the company that got that right. Sooner or later, they just kind of chop them, you know, create problems for themselves. >> Well isn't that inherent though in growth? >> Absolutely! >> It's just like, big companies slow down. >> That's right. >> They can't make decisions as quickly. >> That's right. >> I haven't seen a big company yet that moves nimbly. >> Exactly, and that's the complexity thing-- >> Well wait a minute, what about AWS? They're a 40 billion dollar company. >> Oh yeah, yeah, yeah >> They're like the agile gorilla. >> Yeah, yeah, yeah. >> I mean, I think they're breaking the rule, and my argument would be, because they have data at their core, and they've got that, its a bromide, but that common data model, that they can apply now to virtually any business. You know, we're been expecting, a lot of people have been expecting that growth to attenuate. I mean it hasn't yet, we'll see. But they're like a 40 billion dollar firm-- >> No that's a good example yeah. >> So we'll see. And Microsoft, is the other one. Microsoft is demonstrating double digit growth. For such a large company, it's astounding. I wonder, if the law of large numbers is being challenged, so. >> Yeah, well it's interesting. I do think that what now constitutes "so big" that you're really going to struggle with the complexity. I think that has definitely been elevated a lot. But I still think there will be a point at which human beings can't handle-- >> They're getting away. >> Whatever level of complexity we reach, yeah. >> Well sure, right because even though this great new, it's your point. Cloud technology, you know, there's going to be something better that comes along. Even, I think Jassy might have said, If we had to do it all over again, we would have built the whole thing on lambda functions >> Yeah. >> Oh, yeah. >> Not on, you know so there you go. >> So maybe someone else does that-- >> Yeah, there you go. >> So now they've got their hybrid. >> Yeah, yeah. >> Yeah, absolutely. >> You know maybe it'll take another ten years, but well Jean, thanks so much for coming to theCUBE, >> it was great to have you. >> My pleasure! >> Appreciate you coming back. >> Really fun to talk. >> All right, keep right there everybody, Paul Gillin and Dave Villante, we'll be right back from MIT CDOIQ, you're watching theCUBE. (chuckles) (techno music)

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

(electronic music) >> Narrator: From the SiliconANGLE Media office in Boston Massachusetts, it's the CUBE. Now here are your hosts, Dave Vellante and Stu Miniman. >> Cloud is dead, it's all going to the edge. Or is it? Hi everybody, this is Dave Vellante and I'm here with Stu Miniman. Stu, where does this come from, this narrative that the cloud is over? >> Well Dave, you know, clouds had a good run, right? It's been over a decade. You know, Amazon's dominance in the marketplace but Peter Levine from Andreessen Horowitz did an article where he said, cloud is dead, the edge is killing the dead. The Edge is killing the cloud and really we're talking about IoT and IoT's huge opportunity. Wikibon, Dave we've been tracking for many years. We did you know the original forecast for the Industrial Internet and obviously there's going to be lots more devices at the edge so huge opportunity, huge growth, intelligence all over the place. But in our viewpoint Dave, it doesn't mean that cloud goes away. You know, we've been talking about distributed architectures now for a long time. The cloud is really at the core of this building services that surround the globe, live in just hundreds of places for all these companies so it's nuanced. And just as the cloud didn't overnight kill the data center and lots of discussion as to what lives in the data center, the edge does not kill the cloud and it's really, we're seeing some major transitions pull and push from some of these technologies. A lot of challenges and lots to dig into. >> So I've read Peter Levine's piece, I thought was very thought-provoking and quite well done. And of course, he's coming at that from the standpoint of a venture capitalist, all right. Do I want to start you know, do I want to pour money into the trend that is now the mainstream? Or do I want to get ahead of it? So I think that's what that was all about but here's my question Stu is, in your opinion will the activity that occurs at the edge, will it actually drive more demand from the cloud? So today we're seeing the infrastructure, the service business is growing at what? Thirty five percent? Forty percent? >> Sure, sure. Amazon's growing at the you know, 35 to 40 percent. Google, Microsoft are growing double that right now but overall you're right. >> Yeah, okay and so, and then of course the enterprise players are flat if they're lucky. So my question is will the edge actually be a tailwind for the cloud, in your opinion? >> Yeah, so first on your comment there from an investment standpoint, totally can understand why edge is greenfield opportunity. Lots of different places that I can place bets and probably can win as opposed to if I think that today I'm going to compete against the hyperscale cloud guys. You know, they're pouring 10 billion dollars a year into their infrastructure. They have huge massive employment so the bar to entry is a lot higher. I'm sorry, the second piece was? >> So will the edge drive more demand for the cloud? >> Yeah, absolutely. I think it does Dave because you know, let's take something like autonomous vehicles. Something that we talk about. I need intelligence of the edge. I can't wait for some instruction to go back to the cloud before my Tesla plows into an individual. I need to know that it's there but the models themselves, really I've got all the compute in the cloud. This is where I'm going to train all of my models but I need to be able to update and push those to the edge. If I think about a lot of the industrial applications. Flying a plane is, you know, things need to happen locally but all the anomalies and new things that we run into there's certain pieces that need to be updated to the cloud. So you know, it's kind of a multi-layer. If we look at how much data will there be at the edge, well there's probably going to be more data at the edge than there will be in the central cloud. But how much activity, how much compute do I need, how much things do I need to actually work on. The cloud is probably going to be that central computer still and it's not just a computer, as I said, a distributed architecture. That's where, you know. When we've looked at big data in the early days Dave, when we can put those data lines in the cloud. I've got thousands or millions of compute cycles that I can throw at this at such a lower price and use that there as opposed to at the edge especially. What kind of connectivity do I have? Am i isolated from those other pieces? If you go back to my premise of we're building distributed architectures, the edge is still very early. How do I make sure I secure that? Do I have the network? There's lots of things that I'm going to build in a tiny little component and have that be there. And there's lots of hardware innovation going on at that edge too. >> Okay, so let's talk about how this plays out a little bit and you're talking about a distributed model and it's really to me a distributed data model. The research analysts at Wikibon have envisioned this three-tier data model where you've got data at the edge, which you may or may not persist. You've got some kind of consolidation or aggregation layer where it's you know, it's kind of between the edge and the deep data center and then you've got the cloud. Now that cloud can be an on-prem cloud or it could be the public cloud. So that data model, how do you see that playing out with regard to the adoption of cloud, the morphing of cloud and the edge and the traditional data center? >> Yeah we've been talking about intelligent devices at the edge for a couple decades now. I mean, I remember I built a house in like 1999 and the smart home was already something that people were talking about then. Today, great, I've got you know. I've got my Nest if I have, I probably have smart assistants. There's a lot of things I love-- >> Alexa. >> Saw on Twitter today, somebody's talking like I'm waiting for my light bulbs to update their firmware from the latest push so, some of its coming but it's just this slow gradual adoption. So there's the consumer piece and then there's the business aspect. So, you know, we are still really really early in some of these exciting edge uses. Talk about the enterprise. They're all working on their strategy for how devices and how they're going to work through IoT but you know this is not something that's going to happen overnight. It's they're figuring out their partnerships, they're figuring out where they work, and that three-tiered model that you talked about. My cloud provider, absolutely hugely important for how I do that and I really see it Dave, not as an or but it's an and. So I need to understand where I collect my data, where it's at certain aspects are going to live, and the public cloud players are spending a lot of time working on on that intelligence, the intelligence layer. >> And Stu, I should mention, so far we're talking about really, the infrastructure as a service layer comprises database and middleware. We haven't really addressed the the SAS space and we're not going to go deep into that but just to say. I mean look, packaged software as we knew it is dead, right? SAS is where all the action is. It's the highest growth area, it's the highest value area, so we'll cover that in another segment. So we're really talking about that, the stack up to the middleware, the database, and obviously the infrastructures as a service. So when you think about the players here, let's start with AWS. You've been to I think, every AWS re:Invent maybe, with the exception of one. You've seen the evolution. I was just down in D.C. the other day and they have this chart on the wall, which is their releases, their functional releases by year. It's just, it's overwhelming what they've done. So they're obviously the leader. I saw a recent Gartner Magic Quadrant. It looked like, I tweeted it, it looked like Ronnie Turcotte looking back on Secretariat from the Belmont and whatever it was. 1978, I think it was. (laughs) 31 lengths. I mean, massive domination in the infrastructure as a service space. What do you see going on? >> Yeah so, Dave, absolutely. Today the cloud is, it's Amazon's market out there. Interestingly if you say, okay what's some of the biggest threats in the infrastructure as a service? Well, maybe China, Dave. You know, Alibaba was one that you look at there. But huge opportunity for what's happened at the edge. If you talk about intelligence, you talk about AI, talk about machine learning. Google is actually the company that most people will talk about it, can kind of have a leadership. Heck, I've even seen discussion that maybe we need antitrust to look at Google because they're going to lock things up. You know, they have Android, they have Google Home, they have all these various pieces. But we know Dave, they are far behind Amazon in the public cloud market and Amazon has done a lot, especially over the last two years. You're right, I've been to every Amazon re:Invent except for the first one and the last two years, really seen a maturation of that growth. Not just you know, devices and partnerships there but how do they bring their intelligence and push that out to the edge so things like their serverless technology, which is Lambda. They have Lambda Greengrass that can put to the edge. The serverless is pervading all of their solutions. They've got like the Aurora database-- >> And serverless is profound, not just that from the standpoint of application development but just an entire new business model is emerging on top of serverless and Lambda really started all that but but carry on. >> Yeah and when you look in and you say okay, what better use case than IoT for, well I need infrastructure but I only need it when I need it and I want to call it for when it's there. So that kind of model where I should be able to build by the microsecond and only use what I need. That's something that Amazon is at the forefront, clear leadership position there and they should be able to plug in and if they can extend that out to the edge, starting new partnerships. Like the VMware partnerships, interesting. Red Hat's another partnership they have with OpenShift to be able to get that out to more environments and Amazon has a tremendous ecosystem out there and absolutely is on their radar as to how their-- >> They're crushing it So we were at Google Next last year. Big push, verbally anyway, to the enterprise. They've been making some progress, they're hiring a lot of people out of formerly Cisco, EMC, folks that understand the enterprise but beyond sort of the AI and sort of data analytics, what kind of progress has Google made relative to the leader? >> So in general, enterprise infrastructure service, they haven't made as much progress as most of us watching would expect them to make. But Dave, you mentioned something, data. I mean, at the center of everything we're talking about is the data. So in some ways is Google you know, come on Google, they're smarter than the rest of us. They're skating to where the puck is Dave and infrastructure services, last decades argument if it's the data and the intelligence, Google's got just brilliant people. They're working at the some of these amazing environments. You look at things like Google's Spanner. This is distributed architecture. Say how do I plug in all of these devices and help the work in a distributed gradual work well. You know, heck, I'd be reading the whitepapers that Google's doing in understanding that they might be really well positioned in this 3D chess match that were playing. >> Your eyes might bleed. (laughs) I've read the Google Spanner, I was very excited about it. Understood, you know, a little bit of it. Okay, let's talk about Microsoft. They're really of the big cloud guys. They're really the one that has a partnership strategy to do both on-prem and public cloud. What are your thoughts on that now that sort of Azure stack is starting to roll out with some key partners? >> Yeah absolutely, it's the one that you know. Dave, if you use your analogy looking back, it's like well the next one, it's gaining a little bit, gaining a little bit but still far back. There is Microsoft. Where Microsoft has done best of course is their portfolio of business applications that they have. That they've really turned the green light on for enterprises to adopt SAS with Office 365. Azure stack, it's early days still but companies that use Microsoft, they trust Microsoft. Microsoft's done phenomenal working with developers over the last couple of years. Very prominent like the Kubernetes shows that I've been attending recently. They've absolutely got a play for serverless that we were talking about. I'm not as up to speed as to where Microsoft sits for kind of the IoT edge discussions. >> But you know they're playing there. >> Yeah, absolutely. I mean, Microsoft does identity better than anyone. Active Directory is still the standard in enterprises today. So you know, I worry that Microsoft could be caught in the middle. If Google's making the play for what's next, Microsoft is still chasing a little bit what Amazon's already winning. >> Okay and then we don't have enough time to really talk about China, you mentioned it before. Alibaba's you know, legit. Tencent, Baidu obviously with their captive market in China, they're going to do a lot of business and they're going to move a lot of compute and storage and networking but maybe address that in another segment. I want to talk about the traditional enterprise players. Dell EMC, IBM, HPE, Cisco, where do they stand? We talk a lot at Wikibond about true private cloud. The notion that you can't just stick all your data into the public cloud. Andy Jassy may disagree with that but there are practical realities and certainly when you talk to CIOs they they underscore that. But that notion of true private cloud hasn't allowed these companies to really grow. Now of course IBM and Oracle, I didn't mention Oracle, have a different strategy and Oracle's strategy is even more different. So let's sort of run through them. Let's take the arms dealers. Dell EMC, HPE, Cisco, maybe you put Lenovo in there. What's their cloud strategy? >> Well first of all Dave I think most of them, they went through a number of bumps along the road trying to figure out what their cloud strategy is. Most of them, especially let's take, if you take the compute or server side of the business, they are suppliers to all the service providers trying to get into the hyperscalers. Most of them have, they all have some partnership with Microsoft. There's a Assure stack and they're saying, okay hey, if I want an HPE server in my own data center and in Azure, Microsoft's going to be happy to provide that for you. But David, it's not really competing against infrastructure as a service and the bigger question is as that market has kind of flattened out and we kind of understand it, where is the opportunity for them in IoT. We saw, you know Dave. Last five years or so, can I have a consumer business and an enterprise business in the same? HPE tore those two apart. Michael Dell has kept them together. IBM spun off to Lenovo everything that was on the more consumer side of the business. Where will they play or will companies like Google, like Apple, the ones that you know, Dave. They are spending huge amounts of money in chips. Look at Google and what they're doing with TP use. Look at Apple, I believe it was, there was an Israeli company that they bought and they're making chips there. There's a different need at the edge and sure, company like Dell can create that but will they have the margin, will they have the software, will they have the ecosystem to be able to compete there? Cisco, I haven't seen on the compute side, them going down that path but I was at Cisco Live and a big talk there. I really like the opening keynote and we had a sit down on the CUBE with the executive, it said really if I look out to like 2030. If Cisco still successful and we're thinking about them, we don't think of them as a network company anymore. They are a software company and therefore, things like collaboration, things like how it's kind of a new version of networking that's not on ports and boxes. But really as I think about my data, think about my privacy and security, Cisco absolutely has a play there. They've done some very large acquisitions in that space and they've got some deep expertise there. >> But again, Dell, HPE, Cisco, predominantly arms dealers. Obviously don't have, HPE at one point had a public cloud, they've pulled back. HP's cloud play really is cloud technology partners that they acquire. That at least gives them a revenue stream into the cloud. Now maybe-- >> But it's a consultancy. >> It's a consultancy, maybe it's a one-way trip to the cloud but I will say this about CTP. What it does is it gives HPE a footprint in that business and to the extent that they're a trusted service provider for companies trying to move into the cloud. They can maybe be in the catbird seat for the on-prem business but again, largely an arms dealer. it's going to be a lower margin business certainly than IBM and Oracle, which have applications. They own their own public cloud with the Oracle public cloud and IBM cloud, formerly SoftLayer, which was a two billion dollar acquisition several years ago. So those companies from a participation standpoint, even a tiny market share is compared to Amazon, Google, and Microsoft. They're at least in that cloud game and they're somewhat insulated from that disruption because of their software business and their large install base. Okay, I want to sort of end with, sort of where we started. You know, the Peter Levine comment, cloud is dead, it's all going to the edge. I actually think the cloud era, it's kind of, it's here, we're kind of. It's kind of playing out as many of us had expected over the last five years. You know what blew me away? Is Alexa, who would have thought that Amazon would be a leader in this sort of natural language processing marketplace, right? You would have thought it would come from, certainly Google with all the the search capability. You would have thought Apple with Siri, you know compared to Alexa. So my point is Amazon is able to do that because it's got a data model. It's a data company, all these companies, including Apple, Google, Microsoft, Amazon, Facebook. The largest market cap companies in the world, they have data at the core. Data is foundational for those companies and that's why they are in such a good position to disrupt. So cloud, SAS, mobile, social, big data, to me still these are kind of the last 10 years. The next 10 years are going to be about AI, machine intelligence, deep learning, machine learning, cognitive. We're trying to even get the names right but it starts with the data. So let me put forth the premise and get your commentary. and tie it back in the cloud. So the innovation, in the next 10 years is going to come from data and to the extent that your data is not in silos, you're going to be in a much better position than if it is. Number two is your application of artificial intelligence, you know whatever term you want to use, machine intelligence, etc. Data plus AI, plus I'll bring it back to cloud, cloud economics. If you don't have those cloud economics then you're going to be at a disadvantage of innovation. So let's talk about what we mean by cloud economics. You're talking about the API economy, talking about global scale, always on. Very importantly something we've talked about for years, virtually zero marginal costs at volume, which you're never going to get on-prem because this creates a network effect. And the other thing it does from an innovation context, it attracts startups. Or startups saying, hey I want to build on-prem. No, they don't want to build in the cloud. So it's data plus artificial intelligence plus cloud economics that's going to drive innovation in the next ten years. What are your thoughts? >> Yeah Dave, absolutely. Something I've been saying for the last couple of years, we watched kind of the the customer flywheel that the public clouds have. Data is that next flywheel so companies that can capture that. You mentioned Amazon and Alexa, one of the reasons that Amazon can basically sell that as a loss is lots of those people, they're all Amazon Prime customers and they're ordering more things from Amazon and they're getting so much data that drive all of those other services. Where is Amazon going to threaten in the future? Everywhere. It is basically what they see. The thing we didn't discuss there Dave, you know I love your premise there, is it's technology plus people. What's going to happen with jobs? You and I did the sessions with Andy McAfee and Eril Brynjolfsson, it's racing with the machine. Where is, we know that people plus machines always beat so we spent the last five years talking about data scientist, the growth of developers and developers and the new king makers. So you know what are those new jobs, what are those new roles that are going to help build the solutions where people plus machine will win and what does that kind of next generation of workforce going to look like? >> Well I want to add to that Stu, I'm glad you brought that up. So a friend of mine David Michelle is just about to publish a new book called Seeing Digital. And in that book, I got an advance copy, in there he talks about companies that have data at their core and with human expertise around the data but if you think about the vast majority of companies, it's human expertise and the data is kind of bolted on. And the data lives in silos. Those companies are in a much more vulnerable position in terms of being disrupted, than the ones that have a data model that everybody has access to with human expertise around it. And so when you think about digital disruption, no industry is safe in my opinion, and every industry has kind of its unique attributes. You know, obviously publishing and books and music have disrupted very quickly. Insurance hasn't been disrupted, banking hasn't been disrupted, although blockchain it's probably going to affect that. So again, coming back to this tail-end premise is the next 10 years is going to be about that digital disruption. And it's real, it's not just a bunch of buzzwords, a cloud is obviously a key component, if not the key component of the underlying infrastructure with a lot of activity in terms of business models being built on top. All right Stu, thank you for your perspectives. Thanks for covering this. We will be looking for this video, the outputs, the clips from that. Thanks for watching everybody. This is Dave Vellante with Stu Miniman, we'll see you next time. (electronic music)