 Okay, welcome back everyone. We are here live in Las Vegas for IBM, Pulse IBM's premier cloud show. This is Silicon Angles, The Cube, our flagship program. We go out to the events, extract a signal from the noise. It's exclusive IBM coverage. I'm John Furrier, the founder of Silicon Angles. I'm Joe Michael, Dave Vellante, co-founder of wikibond.org. And our next guest is Steve Mills, senior vice president, group executive, IBM software and systems, welcome to The Cube. Thank you. Well, IBM's had a lot of transitions over the years, but this one is pretty big. The cloud is a big growth driver. Numbers are being forecasted, technology, code is being written on the keynote, opening day, big developer focus. Same game to IBM, but just different players and elements to it. What's your perspective on this transition? And you've been part of many historically at IBM. So what's your take on the current cloud transition right now? Well, each one of these inflection points or changes has both similarities to things that have happened in the past, but also differences. What's fascinating about the information technology industry is that in some respects you get to redo things that you had done before, but the technology has advanced to allow you now to do things in ways that you could never have done previously. So you're working on similar kinds of business problems and challenges, but the technology now has made it possible for you to do it more economically, do it faster, more effective, and you kind of get a chance to sort of do it again right. The third time is a charm type of thing. And certainly around these cloud-based implementations, we're working with customers all over the world to help them, in a sense, rethink their business processes, make them more fluid, more open, more dynamic, and the cloud plays a part in that. And you've got the computing industry. IBM's been a big part of it from the beginning and doing business, solving customer problems. You've got computing, you've got software at the heart of it, right? Software-defined, everything. You've got software, the middle layer being discussed here. What gets you excited about the changes in the computing landscape and the software landscape as it kind of blends together in this hybrid and software-defined, software-driven, data-driven economy? Well, you know, I get very excited about the fact that we can do things at a scale today that we could never have imagined a decade ago. This ability to take on some really big, challenging problems, to analyze things in ways that we simply couldn't afford to do before. It's more data. Obviously, you're throwing a little bit of mobile and social, and you get all these little twists and turns and it becomes a multi-dimensional challenge. And I think that's one of the things that we're seeing with the current set of shifts is that more so, perhaps than ever before, we're seeing the combinatorial effects of many different aspects of technology coming together and therefore impacting the way in which you have to look at problems, think about solving them, the technologies that are available to you to attack the problems, different ways to experience the technology. Obviously, mobile devices have had a profound effect upon what's happening and the way in which applications are being built going forward. So in truth, it's never been a more exciting time. It's also never been more challenging for our clients in terms of trying to figure out, well, given all these changes and all the possibilities, what do I do first? Show me a pathway, a roadmap to taking advantage of all these changes. We saw yesterday a demo of an application being written on stage in just a matter of minutes. You're obviously excited about that. Absolutely, especially since it all worked on stage. Yes, you never know, it was a live demo. What do you think that means for the development community in terms of the number of developers that are going to be attracted to the developing code and how organizations are going to respond to that? Well, I think that it's going to get harder and harder to differentiate who is a developer of technology or at least code. I mean, hardware will always have its unique attributes. But when it comes to software, people will both develop and consume. We're watching this today. We're watching people, various walks of life that are producing downloadable apps, the explosion on the Apple App Store type of thing, Android apps. It's opening up the aperture for lots of creativity. And oftentimes, those that perhaps don't have traditional computer science training but have an understanding of a business problem, the tools are there, scripting, simple interfaces. And suddenly, you're doing things creatively and building useful software that, frankly, you could not have done years ago if you hadn't gone to school and picked up the entire litany of skills associated with information technology. So I wonder if we could maybe go memory lane here. So you've been around and seen a number of transitions as we talked about. I kind of look at you as the Bill Belichick of the IT industry. And you've earned it. The big tuna. Yeah, the big tuna, right. So you were an architect, and you always say, well, I had a lot of help, but the, if not the, an architect of IBM's transition into software. And obviously, things like web sphere, you're pulling learnings from that. How do you compare sort of the changes that we're going through now? What learnings can you pull through when you look at things like Blue Mix and this whole opportunity around, around pass? And what gives you confidence that you can take this forward? Well, if we look at the little demo that was done on stage yesterday, you know, this is all very much around construction more so than programming, right? It's, it's changing the paradigm, you know, away from something that is purely of a technical nature. And allowing composition, you know. Allowing creativity and expression to occur. And granted, the gentleman up demonstrating certainly had technical skills. So he wasn't a neophyte by any means. But what he did wasn't all that profound. When you looked at it on the glass, you'd say, well, gee, I could have done that. You know, do that, that looks pretty simple. You know, that's the kind of thing I would. He memorized it type as fast as he could. That's the kind of thing I want to work with. That's something I could take advantage of. I don't need to, you know, to have a graduate degree in computer science to understand how I would manipulate, you know, that tool set, those components, link these things together. It's all very visual. You know, it's logical construction wiring together. You know, and frankly, this is the kind of thing that dramatically expands the adoption of information technology. How do you move it away from the high priests and priestesses, you know, and make it more, more accessible to all kinds of developers, many of whom, again, are not going to have the kind of formalized training. And so I think that's a, these are powerful models. And we've been working on these models for a very long time. The idea of scripting things together is not a new idea, you know, that some of the computer science concepts are out of the question. I mean, basically what you're saying is a new user of computer science is the user itself. And there could be data analysts using data science stuff. Increasingly self-directed. You know, I want to use the technology the way I want to use it. How can I lay it out? How can I assemble it? How can I get mass customization? Can different users have their own particular personalized experience? And yet, fundamentally, you're still working off the same component parts. As if we're all given the same box of crayons. You know, we all get the full Crayola set. But what we choose to draw with it is of our own making, you know, and our own creativity. What's your strategy to win the developer ecosystem? And obviously that's going to be the tell-tale sign and an open framework. You get Cloud Foundry, you get OpenStack, got a lot of other open initiatives. And that's, Open's not new to IBM at all. They guys have been involved in Open Source for many, many decades. But now you have this DevOps culture. Dave and I say, you know, they're like, they eat glass and spin nails. These guys, they don't want to deal with configurations of hardware. They just want to push code and versioning control. All that stuff automated completely for them. That's the DevOps way. That is like these young guys. I think you've just defined part of it. They're looking for the environment. The environment needs to be welcoming. There needs to be the ability to play, to experiment, to sandbox. I want to have a lot of working material, a lot of components. It really is about creativity. Given my craft and my skills, if you give me good tools, more working material, I can create more things. And so the environment has to be adaptive. And many of the things that we'll provide are not IBM unique. They come out of the open systems world, open tooling, open components. We're looking for this Bluemix environment to be contributory. We want to actually encourage not just IBMers, but our customers, our partners, to push things into Bluemix. Use it as an environment that becomes the kickstart mechanism, if you will, for building next generation applications. You run quite a portfolio. And of course, I think of you, many observers starting as a software guy, even though you predated the whole software strategy. But then when you inherited the hardware business, you are an executive that spends a lot of time in all your businesses. I know that just by observing you. But you made a decision, you and your team, to exit the X86 server business. I wonder if you could talk about that. Why that decision? Was it purely a tactical financial decision? And what does this mean for the organization, your organization going forward? Is it you shifting resources towards software? Are you sharpening the focus on hardware? I wonder if you could discuss that a bit. So what we sold our selling to Lenovo is our basic X86 business. There are some elements of that business that we're not selling. Things that we sell as software. Pure app, pure data, net teaser, cast iron, the data power, ISS, appliances. We have a number of different things which we really sell as software, but they come with hardware. And they're Intel-based, and we're gonna continue to make those things and sell them. So we're not exiting all aspects of the Intel space. But frankly, it is very much an economic decision. It's looking at where the margins are, where the margins are going. The margins are unbelievably stressed in the X86 desktop business. We sold that in 2005. They're unbelievably stressed in the server business. Clearly, Intel gets the inventor's profit from X86. There's a need for distribution. The distributors are the vads and bars as it were. They'll continue to deliver X86-based systems. But frankly, the in-between profit margins have been literally squeezed out. And we reached that point where we concluded there just wasn't the ability to make the kind of money we wanted to make in delivering hardware as hardware. At least around X86. Now, we look at power and our mainframe. We are the inventor. We get the inventor's profit. Inventor's profit, packages profit, distribution profit. Those profit pools are accessible to us in the X86-based world. The access to the profit pools just weren't there. Except for those things that are, in effect, software that wraps around the hardware and then is sold as software. And that teases a wonderful example. It's very much a part of our software business. It carries software-type margins. It's highly differentiated, but the software is what differentiates the technology. So does that free up resource for you to double down on the inventor's side, profit side of the hardware business? Or do you divert those resources to software? Or is it not that, like in one? It's not either or. It's actually both. And certainly, we're going to continue to be a systems company. We're in the storage business. And over time, much of our storage investment actually has been software, not hardware, because we're not producing magnetic rotating disks. We're one of the biggest providers of flash, solid state disk technology in the industry. We obviously acquire that from third parties. We package it with a control unit, a lot of software. And so there are a lot of these software-enabled offerings in the marketplace that are changing the nature of the hardware business. Talk about the big data opportunity. I respect that. Obviously, you guys are all in on big data. We're hearing a lot of Watson conversations in the front end. Is Watson going to answer all the business questions rather than Jeopardy questions? Is it going to answer all the data center questions around sensors and Internet of Things? And how does that fit into the kind of, you see in the API economy, stripe across multiple units with an IBM software-wise? Is it the same for big data in terms of strategy-wise? Well, let's separate a little bit of Watson from the big data. So Watson clearly is a big data system. It is a system. And if you're patient and you keep feeding it information, it becomes all-knowing. And so it's not a matter of what Watson can do, but rather where is Watson a practical solution to a particular problem? And where does that problem get satisfied using other technology that don't necessarily need Watson? So you think about the problem sets. There are things in the world that have very deterministic characteristics. I'm collecting data. That data is understood. It's a format that might have to filter it and organize it in structure in a way that I can then mine the data, but I don't necessarily need the kind of sophisticated inferencing that a Watson system provides. We have a huge portfolio. We've invested more than 20 billion acquiring technologies around analytics and the information management. It's a big investment area for IBM. It's one of the fast-growing parts of our business. And we are clearly a market leader in this whole area related to big data and analytics. Watson really represents a set of capabilities that are brought together in a system that's designed to deal with complex problems where the amount of information is huge and the nature and the structure of the information makes it very challenging to know what the right answer is. It does a good job of humanizing too the whole big data kind of results. We've made it appear that way. It's still a machine. It's like I tell my kids, you eat good, you'll be healthy. With Watson, you feed the right data, it does a good job. Well, you have to feed it. No junk data. You have to feed it a lot of data, but you also have to teach it the truth. It's like a child. He doesn't know what's right and wrong. You teach the truth to the system and as those truths increase, what happens is the system does statistics. I mean, it's a big math engine and it calculates based upon new information that you present to it, whether or not the new information appears to be similar to correlated to related to what it already knows and it gives you back a probability, hopefully a high probability, that what you've now presented to the system relates to these things and therefore I can constantly ingest more facts, more information related to what I know. It's sort of the way your brain works, sort of. Dave always wants to know, is there a single version of the truth? I think we have the right answer. There's a statistical version of the truth and Watson has the statistical version of the truth. I know you're tight on time, but real quick, acquisitions, M&A, organic, you guys have done a great job of balancing maybe a word on your strategy there and what makes you so good at acquisitions? Well, our strategy has been consistent. That's one of the things that has made it a good approach for us in the marketplace. We have a big portfolio, but as we engage with our customers, we certainly discover things we don't have and the decisions there or do you make it? Do you partner with somebody to get that technology that helps fill out your solution or do you buy? And buying certainly has been a good strategy for us. We buy things that fit with what we have. We believe in adjacency. We want things to be synergistic with what we have. If it's the right company, fits well with the things we have, we get tremendous growth. You look at, going back a year or so, the acquisition of work light, great acquisition, a little tiny company, really had at their size, going to have a hard time scaling that business. In a very short period of time, we've moved that product out to well over 400 customers around the world with a company that was really just getting started, great technology, but just getting started. You put it in the IBM company environment with all of our sellers at all our market reach and you get this incredible lift. You're watching this happen with Fiberlink, for example. Certainly you'll watch it happen with Cloud. And again, the profile of these companies tends to be relatively small. We tend to not buy things that are big, but they fit well with what we have. And they are sort of the missing link. It's the puzzle piece that completes the picture. And everybody goes, ah-ha, I really need that. And boom, things take off. Steve Mills here inside the CUBE Senior Executive, legend with an IBM veteran, been through all the wars in IBM, all the successes, all the transitions, looking good with the Cloud software. At the center of its systems are back. Computer science from programmers to analysts to users is part of IBM's major efforts. Big data, Cloud, middleware, software. This is the CUBE, our exclusive coverage of IBM Pulse. I'm John Furrier with Dave Vellante. We'll be right back after this short break.