>> 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, in Boston, bringing you data driven insights from "theCUBE" and ETR. This is "Breaking Analysis" with Dave Vellante. >> There are very few political issues that get bipartisan support these days, nevermind consensus spanning geopolitical boundaries. But whether we're talking across the aisle or over the pond, there seems to be common agreement that the power of big tech firms should be regulated. But the government's track record when it comes to antitrust aimed at big tech is actually really mixed, mixed at best. History has shown that market forces rather than public policy have been much more effective at curbing monopoly power in the technology industry. Hello, and welcome to this week's "Wikibon CUBE" insights powered by ETR. In this "Breaking Analysis" we welcome in frequent "CUBE" contributor Dave Moschella, author and senior fellow at the Information Technology and Innovation Foundation. Dave, welcome, good to see you again. >> Hey, thanks Dave, good to be here. >> So you just recently published an article, we're going to bring it up here and I'll read the title, "Theory Aside, Antitrust Advocates Should Keep Their "Big Tech" Ambitions Narrow". And in this post you argue that big sweeping changes like breaking apart companies to moderate monopoly power in the tech industry have been ineffective compared to market forces, but you're not saying government shouldn't be involved rather you're suggesting that more targeted measures combined with market forces are the right answer. Can you maybe explain a little bit more the premise behind your research and some of your conclusions? >> Sure, and first let's go back to that title, when I said, theory aside, that is referring to a huge debate that's going on in global antitrust circles these days about whether antitrust should follow the traditional path of being invoked when there's real harm, demonstrable harm to consumers or a new theory that says that any sort of vast monopoly power inevitably will be bad for competition and consumers at some point, so your best to intervene now to avoid harms later. And that school, which was a very minor part of the antitrust world for many, many years is now quite ascendant and the debate goes on doesn't matter which side of that you're on the questions sort of there well, all right, well, if you're going to do something to take on big tech and clearly many politicians, regulators are sort of issuing to do something, what would you actually do? And what are the odds that that'll do more good than harm? And that was really the origins of the piece and trying to take a historical view of that. >> Yeah, I learned a new word, thank you. Neo-brandzian had to look it up, but basically you're saying that traditionally it was proving consumer harm versus being proactive about the possibility or likelihood of consumer harm. >> Correct, and that's a really big shift that a lot of traditional antitrust people strongly object to, but is now sort of the trendy and more send and view. >> Got it, okay, let's look a little deeper into the history of tech monopolies and government action and see what we can learn from that. We put together this slide that we can reference. It shows the three historical targets in the tech business and now the new ones. In 1969, the DOJ went after IBM, Big Blue and it's 13 years later, dropped its suit. And then in 1984 the government broke Ma Bell apart and in the late 1990s, went after Microsoft, I think it was 1998 in the Wintel monopoly. And recently in an interview with tech journalist, Kara Swisher, the FTC chair Lena Khan claimed that the government played a major role in moderating the power of tech giants historically. And I think she even specifically referenced Microsoft or maybe Kara did and basically said the industry and consumers from the dominance of companies like Microsoft. So Dave, let's briefly talk about and Kara by the way, didn't really challenge that, she kind of let it slide. But let's talk about each of these and test this concept a bit. Were the government actions in these instances necessary? What were the outcomes and the consequences? Maybe you could start with IBM and AT&T. >> Yeah, it's a big topic and there's a lot there and a lot of history, but I might just sort of introduce by saying for whatever reasons antitrust has been part of the entire information technology industry history from mainframe to the current period and that slide sort of gives you that. And the reasons for that are I think once that we sort of know the economies of scale, network effects, lock in safe choices, lot of things that explain it, but the good bit about that is we actually have so much history of this and we can at least see what's happened in the past and when you look at IBM and AT&T they both were massive antitrust cases. The one against IBM was dropped and it was dropped in as you say, in 1980. Well, what was going on in at that time, IBM was sort of considered invincible and unbeatable, but it was 1981 that the personal computer came around and within just a couple of years the world could see that the computing paradigm had change from main frames and minis to PCs lines client server and what have you. So IBM in just a couple of years went from being unbeatable, you can't compete with them, we have to break up with them to being incredibly vulnerable and in trouble and never fully recovered and is sort of a shell of what it once was. And so the market took care of that and no action was really necessary just by everybody thinking there was. The case of AT&T, they did act and they broke up the company and I would say, first question is, was that necessary? Well, lots of countries didn't do that and the reality is 1980 breaking it up into long distance and regional may have made some sense, but by the 1990 it was pretty clear that the telecom world was going to change dramatically from long distance and fixed wires services to internet services, data services, wireless services and all of these things that we're going to restructure the industry anyways. But AT& T one to me is very interesting because of the unintended consequences. And I would say that the main unintended consequence of that was America's competitiveness in telecommunications took a huge hit. And today, to this day telecommunications is dominated by European, Chinese and other firms. And the big American sort of players of the time AT&T which Western Electric became Lucent, Lucent is now owned by Nokia and is really out of it completely and most notably and compellingly Bell Labs, the Bell Labs once the world's most prominent research institution now also a shell of itself and as it was part of Lucent is also now owned by the Finnish company Nokia. So that restructuring greatly damaged America's core strength in telecommunications hardware and research and one can argue we've never recovered right through this 5IG today. So it's a very good example of the market taking care of, the big problem, but meddling leading to some unintended consequences that have hurt the American competitiveness and as we'll talk about, probably later, you can see some of that going on again today and in the past with Microsoft and Intel. >> Right, yeah, Bell Labs was an American gem, kind of like Xerox PARC and basically gone now. You mentioned Intel and Microsoft, Microsoft and Intel. As many people know, some young people don't, IBM unwillingly handed its monopoly to Intel and Microsoft by outsourcing the micro processor and operating system, respectively. Those two companies ended up with IBM ironically, agreeing to take OS2 which was its proprietary operating system and giving Intel, Microsoft Windows not realizing that its ability to dominate a new disruptive market like PCs and operating systems had been vaporized to your earlier point by the new Wintel ecosystem. Now Dave, the government wanted to break Microsoft apart and split its OS business from its application software, in the case of Intel, Intel only had one business. You pointed out microprocessors so it couldn't bust it up, but take us through the history here and the consequences of each. >> Well, the Microsoft one is sort of a classic because the antitrust case which was raging in the sort of mid nineties and 1998 when it finally ended, those were the very, once again, everybody said, Bill Gates was unstoppable, no one could compete with Microsoft they'd buy them, destroy them, predatory pricing, whatever they were accusing of the attacks on Netscape all these sort of things. But those the very years where it was becoming clear first that Microsoft basically missed the early big years of the internet and then again, later missed all the early years of the mobile phone business going back to BlackBerrys and pilots and all those sorts of things. So here we are the government making the case that this company is unstoppable and you can't compete with them the very moment they're entirely on the defensive. And therefore wasn't surprising that that suit eventually was dropped with some minor concessions about Microsoft making it a little bit easier for third parties to work with them and treating people a little bit more, even handling perfectly good things that they did. But again, the more market took care of the problem far more than the antitrust activities did. The Intel one is also interesting cause it's sort of like the AT& T one. On the one hand antitrust actions made Intel much more likely and in fact, required to work with AMD enough to keep that company in business and having AMD lowered prices for consumers certainly probably sped up innovation in the personal computer business and appeared to have a lot of benefits for those early years. But when you look at it from a longer point of view and particularly when look at it again from a global point of view you see that, wow, they not so clear because that very presence of AMD meant that there's a lot more pressure on Intel in terms of its pricing, its profitability, its flexibility and its volumes. All the things that have made it harder for them to A, compete with chips made in Taiwan, let alone build them in the United States and therefore that long term effect of essentially requiring Intel to allow AMD to exist has undermined Intel's position globally and arguably has undermined America's position in the long run. And certainly Intel today is far more vulnerable to an ARM and Invidia to other specialized chips to China, to Taiwan all of these things are going on out there, they're less capable of resisting that than they would've been otherwise. So, you thought we had some real benefits with AMD and lower prices for consumers, but the long term unintended consequences are arguably pretty bad. >> Yeah, that's why we recently wrote in Intel two "Strategic To Fail", we'll see, Okay. now we come to 2022 and there are five companies with anti-trust targets on their backs. Although Microsoft seems to be the least susceptible to US government ironically intervention at this this point, but maybe not and we show "The Cincos Comas Club" in a homage to Russ Hanneman of the show "Silicon Valley" Apple, Microsoft, Google, and Amazon all with trillion dollar plus valuations. But meta briefly crossed that threshold like Mr. Hanneman lost a comma and is now well under that market cap probably around five or 600 million, sorry, billion. But under serious fire nonetheless Dave, people often don't realize the immense monopoly power that IBM had which relatively speaking when measured its percent of industry revenue or profit dwarf that of any company in tech ever, but the industry is much smaller then, no internet, no cloud. Does it call for a different approach this time around? How should we think about these five companies their market power, the implications of government action and maybe what you suggested more narrow action versus broad sweeping changes. >> Yeah, and there's a lot there. I mean, if you go back to the old days IBM had what, 70% of the computer business globally and AT&T had 90% or so of the American telecom market. So market shares that today's players can only dream of. Intel and Microsoft had 90% of the personal computer market. And then you look at today the big five and as wealthy and as incredibly successful as they've been, you sort of have almost the argument that's wrong on the face of it. How can five companies all of which compete with each other to at least some degree, how can they all be monopolies? And the reality is they're not monopolies, they're all oligopolies that are very powerful firms, but none of them have an outright monopoly on anything. There are competitors in all the spaces that they're in and increasing and probably increasingly so. And so, yeah, I think people conflate the extraordinary success of the companies with this belief that therefore they are monopolist and I think they're far less so than those in the past. >> Great, all right, I want to do a quick drill down to cloud computing, it's a key component of digital business infrastructure in his book, "Seeing Digital", Dave Moschella coined a term the matrix or the key which is really referred to the key technology platforms on which people are going to build digital businesses. Dave, we joke you should have called it the metaverse you were way ahead of your time. But I want to look at this ETR chart, we show spending momentum or net score on the vertical access market share or pervasiveness in the dataset on the horizontal axis. We show this view a lot, we put a dotted line at the 40% mark which indicates highly elevated spending. And you can sort of see Microsoft in the upper right, it's so far up to the right it's hidden behind the January 22 and AWS is right there. Those two dominate the cloud far ahead of the pack including Google Cloud. Microsoft and to a lesser extent AWS they dominate in a lot of other businesses, productivity, collaboration, database, security, video conferencing. MarTech with LinkedIn PC software et cetera, et cetera, Googles or alphabets of business of course is ads and we don't have similar spending data on Apple and Facebook, but we know these companies dominate their respective business. But just to give you a sense of the magnitude of these companies, here's some financial data that's worth looking at briefly. The table ranks companies by market cap in trillions that's the second column and everyone in the club, but meta and each has revenue well over a hundred billion dollars, Amazon approaching half a trillion dollars in revenue. The operating income and cash positions are just mind boggling and the cash equivalents are comparable or well above the revenues of highly successful tech companies like Cisco, Dell, HPE, Oracle, and Salesforce. They're extremely profitable from an operating income standpoint with the clear exception of Amazon and we'll come back to that in a moment and we show the revenue multiples in the last column, Apple, Microsoft, and Google, just insane. Dave, there are other equally important metrics, CapX is one which kind of sets the stage for future scale and there are other measures. >> Yeah, including our research and development where those companies are spending hundreds of billions of dollars over the years. And I think it's easy to look at those numbers and just say, this doesn't seem right, how can any companies have so much and spend so much? But if you think of what they're actually doing, those companies are building out the digital infrastructure of essentially the entire world. And I remember once meeting some folks at Google, and they said, beyond AI, beyond Search, beyond Android, beyond all the specific things we do, the biggest thing we're actually doing is building a physical infrastructure that can deliver search results on any topic in microseconds and the physical capacity they built costs those sorts of money. And when people start saying, well, we should have lots and lots of smaller companies well, that sounds good, yeah, it's all right, but where are those companies going to get the money to build out what needs to be built out? And every country in the world is trying to build out its digital infrastructure and some are going to do it much better than others. >> I want to just come back to that chart on Amazon for a bit, notice their comparatively tiny operating profit as a percentage of revenue, Amazon is like Bezos giant lifestyle business, it's really never been that profitable like most retail. However, there's one other financial data point around Amazon's business that we want to share and this chart here shows Amazon's operating profit in the blue bars and AWS's in the orange. And the gray line is the percentage of Amazon's overall operating profit that comes from AWS. That's the right most access, so last quarter we were well over a hundred percent underscoring the power of AWS and the horrendous margins in retail. But AWS is essentially funding Amazon's entrance into new markets, whether it's grocery or movies, Bezos moves into space. Dave, a while back you collaborated with us and we asked our audience, what could disrupt Amazon? And we came up with your detailed help, a number of scenarios as shown here. And we asked the audience to rate the likelihood of each scenario in terms of its likelihood of disrupting Amazon with a 10 being highly likely on average the score was six with complacency, arrogance, blindness, you know, self-inflicted wounds really taking the top spot with 6.5. So Dave is breaking up Amazon the right formula in your view, why or why not? >> Yeah, there's a couple of things there. The first is sort of the irony that when people in the sort of regulatory world talk about the power of Amazon, they almost always talk about their power in consumer markets, whether it's books or retail or impact on malls or main street shops or whatever and as you say that they make very little money doing that. The interest people almost never look at the big cloud battle between Amazon, Microsoft and lesser extent Google, Alibaba others, even though that's where they're by far highest market share and pricing power and all those things are. So the regulatory focus is sort of weird, but you know, the consumer stuff obviously gets more appeal to the general public. But that survey you referred to me was interesting because one of the challenges I sort of sent myself I was like okay, well, if I'm going to say that IBM case, AT&T case, Microsoft's case in all those situations the market was the one that actually minimized the power of those firms and therefore the antitrust stuff wasn't really necessary. Well, how true is that going to be again, just cause it's been true in the past doesn't mean it's true now. So what are the possible scenarios over the 2020s that might make it all happen again? And so each of those were sort of questions that we put out to others, but the ones that to me by far are the most likely I mean, they have the traditional one of company cultures sort of getting fat and happy and all, that's always the case, but the more specific ones, first of all by far I think is China. You know, Amazon retail is a low margin business. It would be vulnerable if it didn't have the cloud profits behind it, but imagine a year from now two years from now trade tensions with China get worse and Christmas comes along and China just says, well, you know, American consumers if you want that new exercise bike or that new shoes or clothing, well, anything that we make well, actually that's not available on Amazon right now, but you can get that from Alibaba. And maybe in America that's a little more farfetched, but in many countries all over the world it's not farfetched at all. And so the retail divisions vulnerability to China just seems pretty obvious. Another possible disruption, Amazon has spent billions and billions with their warehouses and their robots and their automated inventory systems and all the efficiencies that they've done there, but you could argue that maybe someday that's not really necessary that you have Search which finds where a good is made and a logistical system that picks that up and delivers it to customers and why do you need all those warehouses anyways? So those are probably the two top one, but there are others. I mean, a lot of retailers as they get stronger online, maybe they start pulling back some of the premium products from Amazon and Amazon takes their cut of whatever 30% or so people might want to keep more of that in house. You see some of that going on today. So the idea that the Amazon is in vulnerable disruption is probably is wrong and as part of the work that I'm doing, as part of stuff that I do with Dave and SiliconANGLE is how's that true for the others too? What are the scenarios for Google or Apple or Microsoft and the scenarios are all there. And so, will these companies be disrupted as they have in the past? Well, you can't say for sure, but the scenarios are certainly plausible and I certainly wouldn't bet against it and that's what history tells us. And it could easily happen once again and therefore, the antitrust should at least be cautionary and humble and realize that maybe they don't need to act as much as they think. >> Yeah, now, one of the things that you mentioned in your piece was felt like narrow remedies, were more logical. So you're not arguing for totally Les Affaire you're pushing for remedies that are more targeted in scope. And while the EU just yesterday announced new rules to limit the power of tech companies and we showed the article, some comments here the regulators they took the social media to announce a victory and they had a press conference. I know you watched that it was sort of a back slapping fest. The comments however, that we've sort of listed here are mixed, some people applauded, but we saw many comments that were, hey, this is a horrible idea, this was rushed together. And these are going to result as you say in unintended consequences, but this is serious stuff they're talking about applying would appear to be to your point or your prescription more narrowly defined restrictions although a lot of them to any company with a market cap of more than 75 billion Euro or turnover of more than 77.5 billion Euro which is a lot of companies and imposing huge penalties for violations up to 20% of annual revenue for repeat offenders, wow. So again, you've taken a brief look at these developments, you watched the press conference, what do you make of this? This is an application of more narrow restrictions, but in your quick assessment did they get it right? >> Yeah, let's break that down a little bit, start a little bit of history again and then get to Europe because although big sweeping breakups of the type that were proposed for IBM, Microsoft and all weren't necessary that doesn't mean that the government didn't do some useful things because they did. In the case of IBM government forces in Europe and America basically required IBM to make it easier for companies to make peripherals type drives, disc drives, printers that worked with IBM mainframes. They made them un-bundle their software pricing that made it easier for database companies and others to sell their of products. With AT&T it was the government that required AT&T to actually allow other phones to connect to the network, something they argued at the time would destroy security or whatever that it was the government that required them to allow MCI the long distance carrier to connect to the AT network for local deliveries. And with that Microsoft and Intel the government required them to at least treat their suppliers more even handly in terms of pricing and policies and support and such things. So the lessons out there is the big stuff wasn't really necessary, but the little stuff actually helped a lot and I think you can see the scenarios and argue in the piece that there's little stuff that can be done today in all the cases for the big five, there are things that you might want to consider the companies aren't saints they take advantage of their power, they use it in ways that sometimes can be reigned in and make for better off overall. And so that's how it brings us to the European piece of it. And to me, the European piece is much more the bad scenario of doing too much than the wiser course of trying to be narrow and specific. What they've basically done is they have a whole long list of narrow things that they're all trying to do at once. So they want Amazon not to be able to share data about its selling partners and they want Apple to open up their app store and they don't want people Google to be able to share data across its different services, Android, Search, Mail or whatever. And they don't want Facebook to be able to, they want to force Facebook to open up to other messaging services. And they want to do all these things for all the big companies all of which are American, and they want to do all that starting next year. And to me that looks like a scenario of a lot of difficult problems done quickly all of which might have some value if done really, really well, but all of which have all kinds of risks for the unintended consequence we've talked before and therefore they seem to me being too much too soon and the sort of problems we've seen in the past and frankly to really say that, I mean, the Europeans would never have done this to the companies if they're European firms, they're doing this because they're all American firms and the sort of frustration of Americans dominance of the European tech industry has always been there going back to IBM, Microsoft, Intel, and all of them. But it's particularly strong now because the tech business is so big. And so I think the politics of this at a time where we're supposedly all this great unity of America and NATO and Europe in regards to Ukraine, having the Europeans essentially go after the most important American industry brings in the geopolitics in I think an unavoidable way. And I would think the story is going to get pretty tense over the next year or so and as you say, the Europeans think that they're taking massive actions, they think they're doing the right thing. They think this is the natural follow on to the GDPR stuff and even a bigger version of that and they think they have more to come and they see themselves as the people taming big tech not just within Europe, but for the world and absent any other rules that they may pull that off. I mean, GDPR has indeed spread despite all of its flaws. So the European thing which it doesn't necessarily get huge attention here in America is certainly getting attention around the world and I would think it would get more, even more going forward. >> And the caution there is US public policy makers, maybe they can provide, they will provide a tailwind maybe it's a blind spot for them and it could be a template like you say, just like GDPR. Okay, Dave, we got to leave it there. Thanks for coming on the program today, always appreciate your insight and your views, thank you. >> Hey, thanks a lot, Dave. >> All right, don't forget these episodes are all available as podcast, wherever you listen. All you got to do is search, "Breaking Analysis Podcast". Check out ETR website, etr.ai. We publish every week on wikibon.com and siliconangle.com. And you can email me david.vellante@siliconangle.com or DM me @davevellante. Comment on my LinkedIn post. This is Dave Vellante for Dave Michelle for "theCUBE Insights" powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (slow tempo 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 in Boston, bringing you data-driven insights from theCUBE and ETR, this is Braking Analysis with Dave Vellante. >> Intel's big announcement this week underscores the threat that the United States faces from China. The US needs to lead in semiconductor design and manufacturing. And that lead is slipping because Intel has been fumbling the ball over the past several years, a mere two months into the job, new CEO Pat Gelsinger wasted no time in setting a new course for perhaps, the most strategically important American technology company. We believe that Gelsinger has only shown us part of his plan. This is the beginning of a long and highly complex journey. Despite Gelsinger's clear vision, his deep understanding of technology and execution ethos, in order to regain its number one position, Intel we believe we'll need to have help from partners, competitors and very importantly, the US government. Hello everyone and welcome to this week's Wikibon CUBE insights powered by ETR. In this breaking analysis we'll peel the onion Intel's announcement of this week and explain why we're perhaps not as sanguine as was Wall Street on Intel's prospects. And we'll lay out what we think needs to take place for Intel to once again, become top gun and for us to gain more confidence. By the way this is the first time we're broadcasting live with Braking Analysis. We're broadcasting on the CUBE handles on Twitch, Periscope and YouTube and going forward we'll do this regularly as a live program and we'll bring in the community perspective into the conversation through chat. Now you may recall that in January, we kind of dismissed analysis that said Intel didn't have to make any major strategic changes to its business when they brought on Pat Gelsinger. Rather we said the exact opposite. Our view at time was that the root of Intel's problems could be traced to the fact that it wasn't no longer the volume leader. Because mobile volumes dwarf those of x86. As such we said that Intel couldn't go up the learning curve for next gen technologies as fast as its competitors and it needed to shed its dogma of being highly vertically integrated. We said Intel needed to more heavily leverage outsourced foundries. But more specifically, we suggested that in order for Intel to regain its volume lead, it needed to, we said at the time, spin out its manufacturing, create a joint venture sure with a volume leader, leveraging Intel's US manufacturing presence. This, we still believe with some slight refreshes to our thinking based on what Gelsinger has announced. And we'll talk about that today. Now specifically there were three main pieces and a lot of details to Intel's announcement. Gelsinger made it clear that Intel is not giving up its IDM or integrated device manufacturing ethos. He called this IDM 2.0, which comprises Intel's internal manufacturing, leveraging external Foundries and creating a new business unit called Intel Foundry Services. It's okay. Gelsinger said, "We are not giving up on integrated manufacturing." However, we think this is somewhat nuanced. Clearly Intel can't, won't and shouldn't give up on IDM. However, we believe Intel is entering a new era where it's giving designers more choice. This was not explicitly stated. However we feel like Intel's internal manufacturing arm will have increased pressure to serve its designers in a more competitive manner. We've already seen this with Intel finally embracing EUV or extreme ultraviolet lithography. Gelsinger basically said that Intel didn't lean into EUV early on and that it created more complexity in its 10 nanometer process, which dominoed into seven nanometer and as you know the rest of the story and Intel's delays. But since mid last year, it's embraced the technology. Now as a point of reference, Samsung started applying EUV for its seven nanometer technology in 2018. And it began shipping in early 2020. So as you can see, it takes years to get this technology into volume production. The point is that Intel realizes it needs to be more competitive. And we suspect, it will give more freedom to designers to leverage outsource manufacturing. But Gelsinger clearly signaled that IDM is not going away. But the really big news is that Intel is setting up a new division with a separate PNL that's going to report directly to Pat. Essentially it's hanging out a shingle and saying, we're open for business to make your chips. Intel is building two new Fabs in Arizona and investing $20 billion as part of this initiative. Now well Intel has tried this before earlier last decade. Gelsinger says that this time we're serious and we're going to do it right. We'll come back to that. This organizational move while not a spin out or a joint venture, it's part of the recipe that we saw as necessary for Intel to be more competitive. Let's talk about why Intel is doing this. Look at lots has changed in the world of semiconductors. When you think about it back when Pat was at Intel in the '90s, Intel was the volume leader. It crushed the competition with x86. And the competition at the time was coming from risk chips. And when Apple changed the game with iPod and iPhone and iPad, the volume equation flipped to mobile. And that led to big changes in the industry. Specifically, the world started to separate design from manufacturing. We now see firms going from design to tape out in 12 months versus taking three years. A good example is Tesla and his deal with ARM and Samsung. And what's happened is Intel has gone from number one in Foundry in terms of clock speed, wafer density, volume, lowest cost, highest margin to falling behind. TSMC, Samsung and alternative processor competitors like NVIDIA. Volume is still the maker of kings in this business. That hasn't changed and it confers advantage in terms of cost, speed and efficiency. But ARM wafer volumes, we estimate are 10x those of x86. That's a big change since Pat left Intel more than a decade ago. There's also a major chip shortage today. But you know this time, it feels a little different than the typical semiconductor boom and bust cycles. Semiconductor consumption is entering a new era and new use cases emerging from automobiles to factories, to every imaginable device piece of equipment, infrastructure, silicon is everywhere. But the biggest threat of all is China. China wants to be self-sufficient in semiconductors by 2025. It's putting approximately $60 billion into new chip Fabs, and there's more to come. China wants to be the new economic leader of the world and semiconductors are critical to that goal. Now there are those poopoo the China threat. This recent article from Scott Foster lays out some really good information. But the one thing that caught our attention is a statement that China's semiconductor industry is nowhere near being a major competitor in the global market. Let alone an existential threat to the international order and the American way of life. I think Scotty is stuck in the engine room and can't see the forest of the trees, wake up. Sure. You can say China is way behind. Let's take an example. NAND. Today China is at about 64 3D layers whereas Micron they're at 172. By 2022 China's going to be at 128. Micron, it's going to be well over 200. So what's the big deal? We say talk to us in 2025 because we think China will be at parody. That's just one example. Now the type of thinking that says don't worry about China and semi's reminds me of the epic lecture series that Clay Christiansen gave as a visiting professor at Oxford University on the history of, and the economics of the steel industry. Now if you haven't watched this series, you should. Basically Christiansen took the audience through the dynamics of steel production. And he asked the question, "Who told the steel manufacturers that gross margin was the number one measure of profitability? Was it God?" he joked. His point was, when new entrance came into the market in the '70s, they were bottom feeders going after the low margin, low quality, easiest to make rebar sector. And the incumbents nearly pulled back and their mix shifted to higher margin products and their gross margins went up and life was good. Until they lost the next layer. And then the next, and then the next, until it was game over. Now, one of the things that got lost in Pat's big announcement on the 23rd of March was that Intel guided the street below consensus on revenue and earnings. But the stock went up the next day. Now when asked about gross margin in the Q&A segment of the announcement, yes, gross margin is a if not the key metric in semi's in terms of measuring profitability. When asked Intel CFO George Davis explained that with the uptick in PCs last year there was a product shift to the lower margin PC sector and that put pressure on gross margins. It was a product mix thing. And revenue because PC chips are less expensive than server chips was affected as were margins. Now we shared this chart in our last Intel update showing, spending momentum over time for Dell's laptop business from ETR. And you can see in the inset, the unit growth and the market data from IDC, yes, Dell's laptop business is growing, everybody's laptop business is growing. Thank you COVID. But you see the numbers from IDC, Gartner, et cetera. Now, as we pointed out last time, PC volumes had peaked in 2011 and that's when the long arm of rights law began to eat into Intel's dominance. Today ARM wafer production as we said is far greater than Intel's and well, you know the story. Here's the irony, the very bucket that conferred volume adventures to Intel PCs, yes, it had a slight uptick last year, which was great news for Dell. But according to Intel it pulled down its margins. The point is Intel is loving the high end of the market because it's higher margin and more profitable. I wonder what Clay Christensen would say to that. Now there's more to this story. Intel's CFO blame the supply constraints on Intel's revenue and profit pressures yet AMD's revenue and profits are booming. So RTSMCs. Only Intel can't seem to thrive when there's this massive chip shortage. Now let's get back to Pat's announcement. Intel is for sure, going forward investing $20 billion in two new US-based fabrication facilities. This chart shows Intel's investments in US R&D, US CapEx and the job growth that's created as a result, as well as R&D and CapEx investments in Ireland and Israel. Now we added the bar on the right hand side from a Wall Street journal article that compares TSMC CapEx in the dark green to that of Intel and the light green. You can see TSMC surpass the CapEx investment of Intel in 2015, and then Intel took the lead back again. And in 2017 was, hey it on in 2018. But last year TSMC took the lead, again. And appears to be widening that lead quite substantially. Leading us to our conclusion that this will not be enough. These moves by Intel will not be enough. They need to do more. And a big part of this announcement was partnerships and packaging. Okay. So here's where it gets interesting. Intel, as you may know was late to the party with SOC system on a chip. And it's going to use its packaging prowess to try and leap frog the competition. SOC bundles things like GPU, NPU, DSUs, accelerators caches on a single chip. So better use the real estate if you will. Now Intel wants to build system on package which will dis-aggregate memory from compute. Now remember today, memory is very poorly utilized. What Intel is going to do is to create a package with literally thousands of nodes comprising small processors, big processors, alternative processors, ARM processors, custom Silicon all sharing a pool of memory. This is a huge innovation and we'll come back to this in a moment. Now as part of the announcement, Intel trotted out some big name customers, prospects and even competitors that it wants to turn into prospects and customers. Amazon, Google, Satya Nadella gave a quick talk from Microsoft to Cisco. All those guys are designing their own chips as does Ericsson and look even Qualcomm is on the list, a competitor. Intel wants to earn the right to make chips for these firms. Now many on the list like Microsoft and Google they'd be happy to do so because they want more competition. And Qualcomm, well look if Intel can do a good job and be a strong second sourced, why not? Well, one reason is they compete aggressively with Intel but we don't like Intel so much but it's very possible. But the two most important partners on this slide are one IBM and two, the US government. Now many people were going to gloss over IBM in this announcement, but we think it's one of the most important pieces of the puzzle. Yes. IBM and semiconductors. IBM actually has some of the best semiconductor technology in the world. It's got great architecture and is two to three years ahead of Intel with POWER10. Yes, POWER. IBM is the world's leader in terms of dis-aggregating compute from memory with the ability to scale to thousands of nodes, sound familiar? IBM leads in power density, efficiency and it can put more stuff closer together. And it's looking now at a 20x increase in AI inference performance. We think Pat has been thinking about this for a while and he said, how can I leave leap frog system on chip. And we think he thought and said, I'll use our outstanding process manufacturing and I'll tap IBM as a partner for R&D and architectural chips to build the next generation of systems that are more flexible and performant than anything that's out there. Now look, this is super high end stuff. And guess who needs really high end massive supercomputing capabilities? Well, the US military. Pat said straight up, "We've talked to the government and we're honored to be competing for the government/military chips boundary." I mean, look Intel in my view was going to have to fall down into face not win this business. And by making the commitment to Foundry Services we think they will get a huge contract from the government, as large, perhaps as $10 billion or more to build a secure government Foundry and serve the military for decades to come. Now Pat was specifically asked in the Q&A section is this Foundry strategy that you're embarking on viable without the help of the US government? Kind of implying that it was a handout or a bailout. And Pat of course said all the right things. He said, "This is the right thing for Intel. Independent of the government, we haven't received any commitment or subsidies or anything like that from the US government." Okay, cool. But they have had conversations and I have no doubt, and Pat confirmed this, that those conversations were very, very positive that Intel should head in this direction. Well, we know what's happening here. The US government wants Intel to win. It needs Intel to win and its participation greatly increases the probability of success. But unfortunately, we still don't think it's enough for Intel to regain its number one position. Let's look at that in a little bit more detail. The headwinds for Intel are many. Look it can't just flick a switch and catch up on manufacturing leadership. It's going to take four years. And lots can change in that time. It tells market momentum as well as we pointed out earlier is headed in the wrong direction from a financial perspective. Moreover, where is the volume going to come from? It's going to take years for Intel to catch up for ARMS if it never can. And it's going to have to fight to win that business from its current competitors. Now I have no doubt. It will fight hard under Pat's excellent leadership. But the Foundry business is different. Consider this, Intel's annual CapEx expenditures, if you divide that by their yearly revenue it comes out to about 20% of revenue. TSMC spends 50% of its revenue each year on CapEx. This is a different animal, very service oriented. So look, we're not pounding the table saying Intel's worst days are over. We don't think they are. Now, there are some positives, I'm showing those in the right-hand side. Pat Gelsinger was born for this job. He proved that the other day, even though we already knew it. I have never seen him more excited and more clearheaded. And we agreed that the chip demand dynamic is going to have legs in this decade and beyond with Digital, Edge, AI and new use cases that are going to power that demand. And Intel is too strategic to fail. And the US government has huge incentives to make sure that it succeeds. But it's still not enough in our opinion because like the steel manufacturers Intel's real advantage today is increasingly in the high end high margin business. And without volume, China is going to win this battle. So we continue to believe that a new joint venture is going to emerge. Here's our prediction. We see a triumvirate emerging in a new joint venture that is led by Intel. It brings x86, that volume associated with that. It brings cash, manufacturing prowess, R&D. It brings global resources, so much more than we show in this chart. IBM as we laid out brings architecture, it's R&D, it's longstanding relationships. It's deal flow, it can funnel its business to the joint venture as can of course, parts of Intel. We see IBM getting a nice licensed deal from Intel and or the JV. And it has to get paid for its contribution and we think it'll also get a sweet deal and the manufacturing fees from this Intel Foundry. But it's still not enough to beat China. Intel needs volume. And that's where Samsung comes in. It has the volume with ARM, has the experience and a complete offering across products. We also think that South Korea is a more geographically appealing spot in the globe than Taiwan with its proximity to China. Not to mention that TSMC, it doesn't need Intel. It's already number one. Intel can get a better deal from number two, Samsung. And together these three we think, in this unique structure could give it a chance to become number one by the end of the decade or early in the 2030s. We think what's happening is our take, is that Intel is going to fight hard to win that government business, put itself in a stronger negotiating position and then cut a deal with some supplier. We think Samsung makes more sense than anybody else. Now finally, we want to leave you with some comments and some thoughts from the community. First, I want to thank David Foyer. His decade plus of work and knowledge of this industry along with this collaboration made this work possible. His fingerprints are all over this research in case you didn't notice. And next I want to share comments from two of my colleagues. The first is Serbjeet Johal. He sent this to me last night. He said, "We are not in our grandfather's compute era anymore. Compute is getting spread into every aspect of our economy and lives. The use of processors is getting more and more specialized and will intensify with the rise in edge computing, AI inference and new workloads." Yes, I totally agree with Sarbjeet. And that's the dynamic which Pat is betting and betting big. But the bottom line is summed up by my friend and former IDC mentor, Dave Moschella. He says, "This is all about China. History suggests that there are very few second acts, you know other than Microsoft and Apple. History also will say that the antitrust pressures that enabled AMD to thrive are the ones, the very ones that starved Intel's cash. Microsoft made the shift it's PC software cash cows proved impervious to competition. The irony is the same government that attacked Intel's monopoly now wants to be Intel's protector because of China. Perhaps it's a cautionary tale to those who want to break up big tech." Wow. What more can I add to that? Okay. That's it for now. Remember I publish each week on wikibon.com and siliconangle.com. These episodes are all available as podcasts. All you got to do is search for Braking Analysis podcasts and you can always connect with me on Twitter @dvellante or email me at david.vellante, siliconangle.com As always I appreciate the comments on LinkedIn and in clubhouse please follow me so that you're notified when we start a room and start riffing on these topics. And don't forget to check out etr.plus for all the survey data. This is Dave Vellante for theCUBE insights powered by ETR, be well, and we'll see you next time. (upbeat 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 theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> Robotic process automation solutions remain one of the most attractive investments for IT buyers. This is despite our overall 2020 IT spending forecast, which remained depressed at minus four to minus 5% for the year. Relative to previous surveys, we do see some softness in traditional RPA strongholds such as large financial services and big insurance and giant public and privates. But RPA relative to other IT investments remains at the top as a sector with the highest spending momentum ahead of machine learning, ahead of AI, ahead of containers, and ahead of cloud computing. Hello, everyone, this is Dave Vellante, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we want to update you on the latest RPA trends and share fresh ETR data with our community. So let's get right into it with a quick summary. Now, as I said, despite our pretty tepid IT spending outlook for the entire year in 2020, demand for RPA software continues to grow at a 60 to 70% clip. Now remember, RPA mimics human computer interactions, it uses software scripts or robots that execute human tasks in a runtime assembly of discrete steps. The practice first became popular for back office functions mostly, is unattended bots. The pandemic appears to be accelerating front office adoption and this is creating a bit of a schism between front and back office. Digital transformation initiatives in many ways, they're going to create the connective tissue between front and back of the house. Now competitive dynamics are heating up. The two emergent leaders Automation Anywhere and UiPath are separating from the pack. Large incumbent software vendors like Microsoft, IBM and SAP are entering the market and positioning RPA as a feature. Meanwhile, legacy business process automation players continue to focus on taking their install bases on a broader automation journey. However, all three of these constituents are on a collision course in our view, where deeper automation objective is kind of the North Star. Now there are two material changes to our previous scenario. First, we've expanded our thinking on the RPA TAM, and we're extending this toward a broader automation agenda more consistent with buyer goals. In other words, the TAM is much larger than we initially thought, and we'll explain why. Second, we no longer see this as a winner take all or even winner take most market. In this segment, we'll look deeper into the leaders and share some new data. In particular, well, it appeared in our previous analysis that UiPath was running the table in the market, we see a much more textured, competitive dynamic setting up. And the data suggests that other players including Automation Anywhere, and even some of the larger incumbents will challenge UiPath for leadership in this space. Now, as with many developing software markets, the ultimate leader is not crystal clear at this point. Let's talk about the effects of the pandemic. A conventional wisdom really suggests and by the way, we would agree that the automation mandate has accelerated by several years due to Coronavirus. It's three points here. One is that yes, COVID has put digital transformation on the front burner of executives priority lists. Second is automation isn't trivial. So there's a real difference between wanting and achieving. And third, we believe there's another driver for the automation mandate, which will survive a vaccine or herd immunity, and that is the productivity gap. So this chart here underscores that point and was brought to our attention by a friend of ours, Dave Moschella. Specifically, we've seen a noticeable decline in productivity in the US and EU, since remember the personal productivity boom from the personal computer? The PC and the internet brought forth those trends in Moschella's premise and we agree is that in order to solve the grand challenges of the 2020s and beyond automation is going to be necessary. Think about climate change, global competitiveness, aging populations and infrastructure, massive deficits, mass immigration, sustainable food sources, healthcare. These are all going to require huge injections of automation into the system to solve problems associated with these areas. Human labor just isn't the answer. So this in part has influenced our expanded thinking on the total available market. The diagram we're showing here updates our expectations on the TAM for RPA. The first takeaway is that we're envisioning a market for business automation well beyond software bots, which are represented really in the first two layers, that back office and front office divide, if you will. And we see that coming together in the third layer, those two are really going to happen through digital transformation initiatives. But we also envision a massive market for automated decision making, and very deep business integration where systems are communicating to each other, system to system, machine to machine, and also making real time decisions on behalf of humans. Sometimes we call that systems of agency. Now, I won't go deep into this TAM, as it's a bit academic, but suffice it to say this is an enormous market comprising many layers of the tech stack and services stacks. And this represents a serious opportunities for multiple players, both vendors and buyers. Okay, let's get a little bit more tactical and look at the spending data, the latest spending data, from the ETR survey. The chart we're showing here is one of our favorites. And it compares leading RPA vendors on two dimensions. The y-axis is net score or spending momentum. It's a simple metric, that for this last survey asked buyers are you spending more or less in the second half of the year than you had originally planned. Net score is derived by subtracting the lesses from the mores, and is really shown in the upper right of this chart. You can see that in the green highlights. Note that the total N in the survey is around 1200. And you can see that the number of responses for each vendor is shown in the upper right in that gray area. We eliminated any RPA vendor that didn't get at least 25 mentions in responses in the survey. And you can see that Automation Anywhere and UiPath have essentially traded positions on the vertical axis. Indicating that Automation Anywhere customers expect greater spending momentum with the company than UiPath customers for the second half of this year, than they did in the first half. UiPath at 62% net score is still very, very high but this marks the first time since our reporting that AA, has taken the lead ahead of UiPath in net score. And the small arrow show the general direction of their respective momentum over the last couple of surveys, and I'll discuss this later on. Now on this chart, you can also see Blue Prism and Pegasystems and, while they're significantly below Automation Anywhere and UiPath, these are very respectable net scores for more mature players like these. But I don't really consider them RPA specialists, and especially Pega. I mean, they have an automation play well beyond RPA and have built really an awesome business and in many ways are benefiting from the hype being created by the newbies. I have to say I'm in awe of the business that Alan Trefler and his team have built. We're talking about a billion dollar company here. They've got a valuation, over 9 billion, the stock's near an all time high, and they never took a dime of outside capital prior to their IPO, which is just unreal. Oh, yeah, one more thing I want to call your attention to. There's Microsoft with power automate, and kind of crashing the party with a 1.0 product that is making some noise in the marketplace. Now on the y-axis, you can see UiPath has the market share lead, but I want to remind you what this is. Market shares I mentioned of pervasiveness in the data set in the survey and is, calculated by dividing the number of mentions for a vendor in a sector by the total mentions in the survey. So you can see that UiPath has the share of voice lead, but it's still under 10% of the total survey base. So lots of room for this market to grow. But I want to make an important note here because UiPath has historically been a collection of point products, whereas Automation Anywhere their go to market typically involves going to larger accounts and selling this sort of Mongo and digital transformation project to the line of business. As I said earlier, these two and other companies are on a collision course because that is the big prize. UiPath has restructured its product and pricing strategy, done some acquisitions to go after this. But it stands to reason that UiPath has a bigger presence in the ETR data set as measured by market share. So it makes sense that Automation Anywhere, their number one net score position, it makes it even more impressive. Now the other nuance is that ETR tends to be somewhat weighted to the IT side of the house. And although it most certainly picks up line of business spending, there's a bias in the data toward IT. So that means RPA is most likely even stronger in the context of spending initiatives, and it's already number one relative to other sectors. So that's pretty impressive. Now let's look at how net score has changed over time. This chart shows the change in net score or spending momentum for Automation Anywhere, UiPath, Blue Prism and Pegasystems over the last three survey periods. You see last October, this past April, the height of the lockdown in the US and the most recent July survey. And here you see that Automation Anywhere is accelerating and taking the lead over UiPath. And is the only one in the chart growing net score. Again, UiPath remains elevated despite the relative decline from previous surveys. The other two, I have to caution you again, the Pegasystems for example, and they're killing it in the market. The stock is up nearly 40% year to date, it's over 60% in the last 12 months. So because they're not so RPA only focused and they really are not an IT play per se, the survey data has to be digested in that context. But you do see them coming down from elevated levels last October. Now here's a time series view of that net score. This chart really what it does is it just extends the timeframe and shows more granularity of survey data back to January 2018. So we're talking about 11, quarterly survey data points and snapshots here. This really underscores the power of the ETR platform, because you can stretch the data over time. And you'll see Automation Anywhere overtakes UiPath for the first time since we started capturing the segment. UiPath along with the other shows a noticeable decline in net score in this survey, except for Microsoft, who's, you know, they're just showing up, as I said, they're elbowing their way into the marketplace. Now let's take that same sort of time series view but let's flip to market share. And this next chart shows that other favorite metric that we use all the time as market share or pervasiveness in the dataset, over a time series. Now remember, this is really mentions as a percent of the total. It's not an indication of spending amount, but it's a data point and we pay attention to this. And you can see how UiPath broke away from the pack. They did this back in October 2018, and that coincides with their big push on things like, events, and training, they really have done a good job of building a presence and awareness in the market. I've superimposed on the chart the upper left corner for context that shows net scores in the green and shared N in the gray. It's sorted off of that shared N. This refers to the number of mentions in the dataset for each vendor out of the 1192 total responses. So some of these have small Ns. So I'm not going to put too much emphasis on this except, that UiPath escalation is notable and hopefully I've explain that sufficiently. Okay, let's wrap. So we talked about the automation mandate, and the COVID wrecking ball effect. But it's more than that. The productivity pressures on the US and EU in particular make it exceedingly difficult to just throw labor at the world's grand problems. So this has opened up an enormous opportunity for technology companies and practitioners to drive automation. You know, we said this during the initial in the early days of the big data era. In fact, Peter Goldmacher, had this discussion with us on theCUBE really in the early part of last decade, that those companies that can implement automation at the time he was talking about big data are going to be the big big winners. So it's not just the tech players. Now of course, as we've seen, many of the big tech companies are benefiting enormously from the mega automation trend, but the broader set of industries has massive, massive upside. Now what this sets up is a multi-dimensional competitive environment. We have Automation Anywhere and UiPath battling it out to achieve escape velocity. Automation Anywhere just brought in Chris Riley to run go to market. So you know they're serious. He's a player who understands complex enterprise selling. And now you have UiPath, they're hiring engineers as fast as they can. And the other dimension is a classic battle of best of breed specialists like Automation Anywhere and UiPath, up against the bundlers, selling RPA as a feature of their services. Microsoft, IBM, SAP, etc, all see automation is a huge opportunity and everyone's going to hop on the bandwagon because this is worth hundreds of billions of dollars, at least. Okay. Thanks for watching this episode of theCUBE Insights powered by ETR. Remember all these episodes are available as podcasts wherever you listen. Check it out, we've also put up an archive of all the breaking analysis segments on wikibon.com. There's a link on the menu bar right at the top of the homepage that has all 46 episodes that we've done since inception. I write weekly on that wikibon.com platform and I also publish on siliconangle.com where you can find all the relevant news. And don't forget to check out etr.plus for all the survey data and analysis. Go there and sign up for a trial of the software. It's awesome. Okay, this is Dave Vellante, be well, and we'll see you next time. (bright 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 New York, it's theCUBE! Covering Machine Learning Everywhere: Build Your Ladder To AI. Brought to you by IBM. >> Well, good morning! Welcome here on theCUBE. Along with Dave Vellante, I'm John Walls. We're in Midtown New York for IBM's Machine Learning Everywhere: Build Your Ladder To AI. Great lineup of guests we have for you today, looking forward to bringing them to you, including world champion chess master Garry Kasparov a little bit later on. It's going to be fascinating. Dave, glad you're here. Dave, good to see you, sir. >> John, always a pleasure. >> How you been? >> Up from DC, you know, I was in your area last week doing some stuff with John Furrier, but I've been great. >> Stopped by the White House, drop in? >> You know, I didn't this time. No? >> No. >> Dave: My son, as you know, goes to school down there, so when I go by my hotel, I always walk by the White House, I wave. >> Just in case, right? >> No reciprocity. >> Same deal, we're in the same boat. Let's talk about what we have coming up here today. We're talking about this digital transformation that's going on within multiple industries. But you have an interesting take on it that it's a different wave, and it's a bigger wave, and it's an exciting wave right now, that digital is creating. >> Look at me, I've been around for a long time. I think we're entering a new era. You know, the great thing about theCUBE is you go to all these events, you hear the innovations, and we started theCUBE in 2010. The Big Data theme was just coming in, and it appeared, everybody was very excited. Still excited, obviously, about the data-driven concept. But we're now entering a new era. It's like every 10 years, the parlance in our industry changes. It was cloud, Big Data, SaaS, mobile, social. It just feels like, okay, we're here. We're doing that now. That's sort of a daily ritual. We used to talk about how it's early innings. It's not anymore. It's the late innings for those. I think the industry is changing. The describers of what we're entering are autonomous, pervasive, self-healing, intelligent. When you infuse artificial intelligence, I'm not crazy about that name, but when you infuse that throughout the landscape, things start to change. Data is at the center of it, but I think, John, we're going to see the parlance change. IBM, for example, uses cognitive. People use artificial intelligence. I like machine intelligence. We're trying to still figure out the names. To me, it's an indicator that things are changing. It's early innings now. What we're seeing is a whole new set of opportunities emerging, and if you think about it, it's based on this notion of digital services, where data is at the center. That's something that I want to poke at with the folks at IBM and our guests today. How are people going to build new companies? You're certainly seeing it with the likes of Uber, Airbnb, Waze. It's built on these existing cloud and security, off-the-shelf, if you will, horizontal technologies. How are new companies going to be built, what industries are going to be disruptive? Hint, every industry. But really, the key is, how will existing companies keep pace? That's what I really want to understand. >> You said, every industry's going to be disrupted, which is certainly, I think, an exciting prospect in some respects, but a little scary to some, too, right? Because they think, "No, we're fat and happy "and things are going well right now in our space, "and we know our space better than anybody." Some of those leaders might be thinking that. But as you point out, digital technology has transformed to the extent now that there's nobody safe, because you just slap this application in, you put this technology in, and I'm going to change your business overnight. >> That's right. Digital means data, data is at the center of this transformation. A colleague of mine, David Moschella, has come up with this concept of the matrix, and what the matrix is is a set of horizontal technology services. Think about cloud, or SaaS, or security, or mobile, social, all the way up the stack through data services. But when you look at the companies like Airbnb and Uber and, certainly, what Google is doing, and Facebook, and others, they're building services on top of this matrix. The matrix is comprised of vertical slices by industry and horizontal slices of technology. Disruptors are cobbling together through software and data new sets of services that are disrupting industries. The key to this, John, in my view, anyway, is that, historically, within healthcare or financial services, or insurance, or manufacturing, or education, those were very siloed. But digital and data allows companies and disruptors to traverse silos like never before. Think about it. Amazon buying Whole Foods. Apple getting into healthcare and financial services. You're seeing these big giants disrupt all of these different industries, and even smaller guys, there's certainly room for startups. But it's all around the data and the digital transformation. >> You spoke about traditional companies needing to convert, right? Needing to get caught up, perhaps, or to catch up with what's going on in that space. What do you do with your workforce in that case? You've got a bunch of great, hardworking people, embedded legacy. You feel good about where you are. And now you're coming to that workforce and saying, "Here's a new hat." >> I think that's a great question. I think the concern that one would have for traditional companies is, data is not foundational for most companies. It's not at their core. The vast majority of companies, the core are the people. You hear it all the time. "The people are our greatest asset." That, I hate to say it, but it's somewhat changing. If you look at the top five companies by market cap, their greatest asset is their data, and the people are surrounding that data. They're very, very important because they know how to leverage that data. But if you look at most traditional companies, people are at their core. Data is kind of, "Oh, we got this bolt-on," or it's in a bunch of different silos. The big question is, how do they close that gap? You're absolutely right. The key is skillsets, and the skills have to be, you know, we talk about five-tool baseball players. You're a baseball fan, as am I. Well, you need multi-tool players, those that understand not only the domain of whether it's marketing or sales or operational expertise or finance, but they also require digital expertise. They know, for example, if you're a marketing professional, they know how to do hypertargeting. They know how to leverage social. They know how to do SEO, all these digital skills, and they know how to get information that's relevant and messaging out into the marketplace and permeate that. And so, we're entering, again, this whole new world that's highly scalable, highly intelligent, pervasive, autonomous. We're going to talk about that today with a lot of their guests, with a lot of our guests, that really are kind of futurists and have thought through, I think, the changes that are coming. >> You can't have a DH anymore, right, that's what you're saying? You need a guy that can play the field. >> Not only play the field, not only a utility player, but somebody who's a utility player, but great. Best of breed at all these different skillsets. >> Machine learning, we haven't talked much about that, and another term, right, that certainly has different definitions, but certainly real specific applications to what's going on today. We'll talk a lot about ML today. Your thoughts about that, and how that squares into the artificial intelligence picture, and what we're doing with all those machines out there that are churning 24/7. >> Yeah, so, real quick, I know we're tight on time here. Artificial intelligence to me is the umbrella. Machine learning is the application of math and algorithms to solve a particular problem or answer a particular question. And then there's deep learning, which is highly focused neural networks that go deeper and deeper and deeper, and become auto-didactic, self-learning, in a manner. Those are just the very quick and rudimentary description. Machine learning to me is the starting point, and that's really where organizations really want to start to learn and begin to close the gap. >> A lot of ground to cover, and we're going to do that for you right here on theCUBE as we continue our coverage of Machine Learning Everywhere: Your Ladder To AI, coming up here, IBM hosting us in Midtown, New York. Back with more here on theCUBE in just a bit. (fast electronic music)