>> Hi, I'm Peter Burris, Welcome to Wikibon's Action Item. (slow techno music) Once again Wikibon's research team is assembled, centered here in The Cube Studios in lovely Palo Alto, California, so I've got David Floyer and George Gilbert with me here in the studio, on the line we have Neil Raden and Jim Kobielus, thank you once again for joining us guys. This week we are going to talk about an issue that has been dominant consideration in the industry, but it's unclear exactly what direction it's going to take, and that is the role that open source is going to play in the next generation of solving problems with technology, or we could say the role that open source will play in future digital transformations. No one can argue whether or not open source has been hugely consequential, as I said it has been, it's been one of the major drivers of not only new approaches to creating value, but also new types of solutions that actually are leading to many of the most successful technology implementations that we've seen ever, that is unlikely to change, but the question is what formal open source take as we move into an era where there's new classes of individuals creating value, like data scientists, where those new problems that we're trying to solve, like problems that are mainly driven by the role that data as opposed to code plays, and that there are new classes of providers, namely service providers as opposed to product or software providers, these issues are going to come together, and have some pretty important changes on how open source behaves over the next few years, what types of challenges it's going to successfully take on, and ultimately how users are going to be able to get value out of it. So to start the conversation off George, let's start by making a quick observation, what has the history of open source been, take us through it kind of quickly. >> The definition has changed, in its first incarnation it was fixed UNIX fragmentation and the high price of UNIX system servers, meaning UNIX the proprietary UNIX's and the proprietary servers they were built, that actually rather quickly morphed into a second incarnation where it was let's take the Linux stack, Linux, Apache, MySQL, PHP, Python, and substitute that for the old incumbents, which was UNIX, BEA Web Logic, the J2E server and Oracle Database on an EMC storage device. So that was the collapse of the price of infrastructure, so really quickly then it morphed into something very, very different, which was we had the growth of the giant Internet scale vendors, and neither on pricing nor on capacity could traditional software serve their needs, so Google didn't quite do open source, but they published papers about what they did, those papers then were implemented. >> Like Map Produce. Yeah Map Produce, Big Table, Google File System, those became the basis of Hadoop which Yahoo open sourced. There is another incarnation going, that's probably getting near its end of life right now, which is sort of a hybrid, where you might take Kafka which is open source, and put sort of proprietary bits around it for management and things like that, same what Cloudera, this is called the open core model, it's not clear if you can build a big company around it, but the principle is, the principle for most of these is, the value of the software is declining, partly because it's open source, and partly because it's so easy to build new software systems now, and the hard part is helping the customer run the stuff, and that's where some of these vendors are capturing it. >> So let's David turn our attention to how that's going to turn into actual money. So in this first generation of open source, I think up until now, certainly Red Hat, Canonical have made money by packaging and putting forward distributions, that have made a lot of money, IBM has been one of the leaders in contributing open source, and then turning that into a services business, Cloudera, Horton Works, NapR, some of these other companies have not generated the same type of market presence that a Red Hat or Canonical have put forward, but that doesn't mean there aren't companies out there that have been very successful at appropriating significant returns out of open source software, mainly however they're doing it as George said, as a service, give us some examples. >> I think the key part of open source is providing a win-win environment, so that people are paid to do stuff, and what is happening now a lot is that people are putting stuff into open source in order that it becomes a standard, and also in order that it is maintained by the community as a whole. So those two functions, those two capabilities of being paid by a company often, by IBM or by whoever it is to do something on behalf of that company, so that it becomes a standard, so that it becomes accepted, that is a good business model, in the sense that it's win-win, the developer gets recognition, the person paying for it achieves their business objective of for example getting a standard recognized-- >> A volume. >> Volume, yes. >> So it's a way to get to volume for the technology that you want to build your business around. >> Yes, what I think is far more difficult in this area is application type software, so where open source has been successful, as George said is in the stacks themselves, the lower end of the stacks, there are a few, and they usually come from very very successful applications like Word, Microsoft Word, or things like that where they can be copied, and be put into open source, but even there they have around them software from a company, Red Hat or whoever it is, that will make it successful. >> Yes but open office wasn't that successful, get to the kind of, today we have Amazon, we have some of the hyper scalars that are using that open core model and putting forward some pretty powerful services, is that the new Red Hat, is that the new Canonical? >> The person who's made most money is clearly Amazon, they took open source code and made it robust, and made it in volume, those are the two key things you to have for success, it's got to be robust, it's got to be in volume, and it's very difficult for the open source community to achieve that on its own, it needs the support of a large company to do that, and it needs the value that that large company is going to get from it, for them to put those resources in. So that has been a very successful model a lot of people decry it because they're not giving back, and there's an argument-- >> They being Amazon, have not given back quite as much. >> Yes they have relatively very few commiters. I think that's more of a problem in the T&Cs of the open source contract, so those should probably be changed, to put more onus on people to give back into the pool. >> So let me stop you, so we have identified one thing that is likely going to have to be evolved as we move forward, to prevent problems, some of the terms and conditions, we try to ensure that there is that quid pro quo, that that win-win exists. So Jim Kobielus, let me ask you a question, open source has been, as David mentioned, open source has been more successful where there is a clear model, a clear target of what the community is trying to build, it hasn't been quite successful, where it is in fact is expected that the open source community is going to start with some of the original designs, so for example, there's an enormous plethora of big data tools, and yet people are starting to ask why is big data more successful, and partly it's because putting these tools together is so difficult. So are we going to see the type of artifacts and assets and technologies associated with machine learning, AI, deep learning et cetera, easily lend themselves to an open source treatment, what do you think? >> I think were going to see open source very much take off in the niches of the deep learning and machine learning AI space, where the target capabilities we've built are fairly well understood by our broad community. Machine learning clearly, we have a fair number of frameworks that are already well established, with respect to the core capabilities that need to be performed from modeling and training, and deployment of statistical models into applications. That's where we see a fair amount of takeoff for Tensor Flow, which Google built in an open source, because the core of deep learning in terms of the algorithm, in terms of the kinds of functions you perform to be able to take data and do feature engineering and algorithm selection are fairly well understood, so those are the kinds of very discreet capabilities for which open source code is becoming standard, but there's many different alternative frameworks for doing that, Tensor Flow being one of them, that are jostling for presence in the market. The term is commoditized, more of those core capabilities are being commoditized by the fact that there well understood and agreed to by a broad community. So those are the discrete areas we're seeing the open source alternatives become predominant, but when you take a Tensor Flow and combine it with a Spark, and with a Hadoop and a Kafka and broader collections of capabilities that are needed for robust infrastructure, those are disparate communities that each have their own participants committed and so forth, nobody owns that overall step, there's no equivalent of a lamp stack were all things to do with deep learning machine learning AI on an open source basis come to the fore. If some group of companies is going to own that broadening stack, that would indicate some degree of maturation for this overall ecosystem, that's not happening yet, we don't see that happening right now. >> So Jim, I want to, my bias, I hate the term commoditization, but I Want to unify what you said with something that David said, essentially what we're talking about is the agreement in a collaborative open way around the conventions of how we perform work that compute model which then turns into products and technologies that can in fact be distributed and regarded as a standard, and regarded as a commodity around which trading can take place. But what about the data side of things George, we have got, Jim's articulated I think a pretty good case, that we're going to start seeing some tools in the marketplace, it's going to be interesting to see whether that is just further layering on top of all this craziness that is happening in the big data world, and just adding to it in the ML world, but how does the data fit into this, are we going to see something that looks like open source data in the marketplace? >> Yes, yes, and a modified yes. Let me take those in two pieces. Just to be slightly technical, hopefully not being too pedantic, software used to mean algorithms and data structures, so in other words the recipe for what to do, and the buckets for where to put the data, that has changed in the data in terms of machine learning, analytic world where the algorithms and data are so tied together, the instances of the data, not the buckets, that the data changed the algorithms, the algorithms change the data, the significance of that is, when we build applications now, it's never done, and so you go, the construct we've been focusing on is the digital twin, more broadly defined than a smart device, but when you go from one vendor and you sort of partially build it, it's an evergreen thing, it's never done, then you go to the next vendor, but you need to be able to backport some core of that to the original vendor, so for all intents and purposes that's open source, but it boils down to actually the original Berkeley license for open source, not the Apache one everyone is using now. And remind me of the other question? >> The other issue is are we going to see datasets become open source like we see code bases and code fragments and algorithms becoming open source? >> Yes this is also, just the way Amazon made infrastructure commoditized and rentable, there are going to be many datasets were they used to be proprietary, like a Google web crawl, and Google knowledge graph of disambiguation people, places and things, some of these things are either becoming open source, or openly accessible by API, so when you put those resources together you're seeing a massive deflation, or a massive shrinkage in the capital intensity of building these sorts of apps. >> So Neil, if we take a look at where we are this far, we can see that there is, even though we're moving to a services oriented model, Amazon for example is a company that is able to generate commercial rents out of open source software, Jim has made a pretty compelling case that open source software can be, or will emerge out of the tooling world for some of these new applications, there are going to be some examples of datasets, or at least APIs to datasets that will look more open source like, so it's not inconceivable that we'll see some actual open source data, I think GDPR, and some other regulations, we're still early in the process of figuring out how we're going to turn data into commodity, using Jim's words. But what about the personnel, what about the people? There were reasons why developers moved to open source, some of the soft reasons that motivated them to do things, who they work with, getting the recognition, working on relevant projects, working with relevant technologies, are we going to see a similar set of soft motivators, diffuse into the data scientist world, so that these individuals, the real ones who are creating the real value, are going to have some degree of motivation to participate with each other collaborate with each other in an open source way, what do you think? >> Good question, I think the answer is absolutely true, but it's not unique to data scientists, academics, scientists in molecular biology, civil engineers, they all wannabe recognized by their peers, on some level beyond just their, just what they're doing in their organization, but there is another segment of data scientists that are just guys working for a paycheck, and generating predictive analysis and helping the company along and so forth, and that's what they're going to do. The whole open source thing, you remember object programming, you remember JavaBeans, you remember Web Services, we tried to turn developers into librarians, and when they wanted to develop something, you go to Github, I go to Github right now and I say I'm looking for a utility that can figure out why my face is so pink on this camera, I get 1000 listings of programs, and have no idea which ones work and which ones don't, so I think the whole open source thing is about to explode, it already has, in terms of piece parts. But I think managing in an organization is different, and when I say an organization, there's the Googles and the Amazons and so forth of the world, and then there's everybody else. >> Alright so we've identified an area where we can see some consequence of change where we can anticipate some change will be required to modernize the open source model, the licensing model, we see another one where the open source communities going to have to understand how to move from a product and code to a data and service orientation, can we think of any others? >> There is one other that I'd like to add to that, and that is compliance. You addressed it to some extent, but compliance brings some real-world requirements onto code and data, and you were saying earlier on that one of the options is bringing code and data so that they intermingle and change each other, I wonder whether that when you look at it from a compliance point of view will actually pass muster, because you need from a compliance point of view to prove, for example, in the health service, that it works, and it works the same way every time, and if you've got a set of code and data that doesn't work the same every time, you probably are going to get pushed back from the people who regularly health that this is not, you can't do it that way, you'll have to find another way to do it. But that again is, is at the same each time, so the point I'm making-- >> This is a bigger issue than just open source, this is an issue where the idea if continuous refinement of the code, and the data-- >> Automatic refinement. >> Automatic refinement, could in fact, we're going to have to change some compliance laws, is open source, is it possible the open source community might actually help us understand that problem? >> Absolutely, yes. >> I think that's a good point, I think that's a really interesting point, because you're right George, the idea of a continuous development, is not something that for example Serr Banes actually says I get this, Serr Banes actually says "Oh yeah, I get this." Serr Banes actually is like, yes the data, I acknowledge that this date is right, and I acknowledge the process by which it was created was read, now this is another subject, let's bring this up later, but I think it's relevant here, because in many respects it's a difference between an income statement and balance sheet right? Saying it's good now, is kind of like the income statement, but let's come back to this, because I think it's a bigger issue. You're asserting the open source community in fact may help solve this problem by coming up with new ways of conceiving say versioning of things, and stamping things and what is a distribution, what isn't a distribution, with some of these more tightly bound sets of-- >> What we find normally is that-- >> Jim: I think that we are going to-- >> Peter: Go on Jim. >> Just to elaborate on what Peter was talking about, that whole theme, I think what we're going to see is more open source governance of models and data, within distributed development environments, using technologies like block chain as a core enabler for these workflows, for these as it were general distributed hyper ledgers indicate the latest and greatest version of a given dataset, or a given model being developed somewhere around some common solution domain, I think those kinds of environments for governance will become critically important, as this pipeline for development and training and deployment of these assets, gets ever more distributed and virtual. >> By the way Jim I actually had a conversation with a very large open source distribution company a few months ago about this very point, and I agree, I think blockchain in fact could become a mechanism by which we track intellectual property, track intellectual contributions, find ways to then monetize those contributions, going back to what you were saying David, and perhaps that becomes something that looks like the basis of a new business model, for how we think about how open source goes after these looser, goosier problems. >> But also to guarantee integrity without going through necessarily a central-- >> Very important, very important because at the end of the day George-- >> It's always hard to find somebody to maintain. >> Right, big companies, one of the big challenges that companies today are having is that they do open source is that they want to be able to keep track of their intellectual property, both from a contribution standpoint, but also inside their own business, because they're very, very concerned that the stuff that they're creating that's proprietary to their business in a digital sense, might leave the building, and that's not something a lot of banks for example want to see happen. >> I want to stick one step into this logic process that it think we haven't yet discussed, which is, we're talking about now how end customers will consume this, but there still a disconnect in terms of how the open source software vendor's or even hybrid ones can get to market with this stuff, because between open source pricing models and pricing levels, we've seen a slow motion price collapse, and the problem is that, the new go to market motion is actually made up of many motions, which is discover, learn, try, buy, recommend, and within each of those, the motion was different, and you hear it's almost like a reflex, like when your doctor hit you on the knee and your leg kind of bounced, everybody says yeah we do land and expand, and land was to discover, learn, try augmented with inside sales, the recommend and standardizes still traditional enterprise software where someone's got to talk to IT and procurement about fitting into the broader architecture, and infrastructure of the firm, and to do that you still need what has always been called the most expensive migratory workforce in the world, which is an enterprise sales force. >> But I would suggest there's a big move towards standardization of stacks, true private cloud is about having a stack which is well established, and the relationship between all the different piece parts, and the stack itself is the person who is responsible for putting that stack and maintaining that stack. >> So for a moment pretend that you are a CIO, are you going to buy OpenStack or are you going to buy the Vmware stack? >> I'm going to buy Vmware stack. >> Because that's about open source? >> No, the point I'm saying is that those open source communities or pieces, would then be absorbed into the stack as an OEM supplier as opposed to a direct supplier and I think that's true for all of these stacks, if you look at the stack for example and you have code from Netapp or whatever it is that's in that code and they're contributing It You need an OEM agreement with that provider, and it doesn't necessarily have to be open source. >> Bottom line is this stuff is still really, really complicated. >> But this model of being an OEM provider is very different from growing an enterprise sales force, you're selling something that goes into the cost of goods sold of your customer, and that the cost of goods sold better be less than 15 percent, and preferably less than five percent. >> Your point is if you can't afford a sales force, an OEM agreement is a much better way of doing it. >> You have to get somebody else's sales force to do it for you. So look I'm going to do the Action Item on this, I think that this has been a great conversation again, David, George, Neil, Jim, thanks a lot. So here's the Action Item, nobody argues that open source hasn't been important, and nobody suggests that open source is not going to remain important, what we think based on our conversation today is that open source is going to go through some changes, and those changes will occur as a consequence of new folks that are going to be important to this like data scientists, to some of the new streams of value in the industry, may not have the same motivations that the old developer world had, new types of problems that are inherently more data oriented as opposed process-oriented, and it's not as clear that the whole concept of data as an artifact, data as a convention, data as standards and commodities, are going to be as easy to define as it was in the cold world. As well as ultimately IT organizations increasingly moving towards an approach that focused more on the consumption of services, as opposed to the consumption of product, so for these and many other reasons, our expectation is that the open source community is going to go through its own transformation as it tries to support future digital transformations, current and future digital transformations. Now some of the areas that we think are going to be transformed, is we expect that there's going to be some pressure on licensing, we think there's going to be some pressure in how compliance is handled, and we think the open source community may in fact be able to help in that regard, and we think very importantly that there will be some pressure on the open source community trying to rationalize how it conceives of the new compute models, the new design models, because where open source always has been very successful is when we have a target we can collaborate to replicate and replace that target or provide a substitute. I think we can all agree that in 10 years we will be talking about how open source took some time to in fact put forward that TPC stack, as opposed to define the true private cloud stack. So our expectation is that open source is going to remain relevant, we think it's going to go through some consequential changes, and we look forward to working with our clients to help them navigate what some of those changes are, both as commiters, and also as consumers. Once again guys, thank you very much for this week's Action Item, this is Peter Barris, and until next week thank you very much for participating on Wikibon's Action Item. (slow techno music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering Interconnect 2017. Brought to you by IBM. >> Hey, welcome back everyone. We're live here in Las Vegas at the Mandalay Bay for IBM InterConnect 2017 exclusive Cube coverage. I'm John Furrier, my co-host Dave Vellante, our next guest Dr. Angel Diaz who is the vice president of developer technology. Also you know him from the open source world. Great to see you again. >> Nice to see you. Thanks for spending time with us. >> Thank you. >> Boy, Blockchain, open source, booming, cloud-native, booming, hybrid cloud, brute force but rolling strong. Enterprise strong, if you will, as your CEO Ginni Rometty started talking about yesterday. Give us the update on what's going on with the technology and developers because this is something that you guys, you personally, have been spending a lot of time with. Developer traction, what's the update? >> Well you know if you look at history there's been this democratization of technology. Right, everything from object oriented programming to the internet where we realize if we created open communities you can build more skill, more value, create more innovation. And each one of these layers you create abstractions. You reduce the concept count of what developers need to know to get work done and it's all about getting work done faster. So, you know, we've been systematically around cloud, data, and AI, working really hard to make sure that you have open source communities to support those. Whether it's in things like compute, storage, and network, platform as a service like say Cloud Foundry, what we're doing around the open container initiatives and the Cloud Native Computing Foundation to all the things you see in the data space and everywhere else. So it's real exciting and it's real important for developers. >> So two hot trends that we're tracking obviously, one's pretty obvious. That's machine learning in cloud. Really hand and glove together. You see machine learning really powering the AI, hitting IOT all the way up to apps and wearables and what not, autonomous vehicles. Goes on and on. The other one is Kubernetes, and Kubernetes, the rise of Kubernetes has really brought the containers to a whole nother level around multi-cloud. People might not know it, but you are involved in the CNCF formation, which is Kubernetes movement, which was KubeCon, then it became part of the Linux Foundation. So, IBM has had their hand in these two trends pretty heavily. >> Angel: Oh yeah, absolutely. >> Give the perspective, because the Kubernetes one, in particular, we'll come back to the machine learning, but Kubernetes is powering a whole nother abstraction layer around helping containers go to the next level with microservices, where the develop equation has changed. It's not just the person writing code anymore, a person writing code throws off an application that has it's own life in relationship to other services in the community, which also has analytics tied to it. So, you're seeing a changing dynamic on this potential with Kubernetes. How important is Kubernetes, and what is the real impact? >> No, it is important. And what there actually is, there's a couple of, I think, application or architecture trends that are fundamentally changing how we build applications. So one of them I'll call, let's call it Code First. This is where you don't even think about the Kubernetes layer. All you do is you want to write your code and you want to deploy your code, and you want it to run. That's kind of the platform. Something like Cloud Foundry addresses the Code First approach. Then there's the whole event-drive architecture world. Serverless, right? Where it has a particular use case, event-driven, standing, stuff up and down, dealing with many types of inputs, running rules. Then you have, let's say the more transactional type applications. Microservices, right? These three thing, when combined allows you to kind of break the shackles of the monolith of old application architectures, and build things the way that best suit your application model, and then come together in much more coherent way. Specifically in Kubernetes, and that whole container stuff. You think think about it, initially, when, containers have been around a long time, as we all know, and Docker did a great job in making container accessible and easy, right? And we worked really closely with them to create some multisource activities around the base container definitions, the open container initiative in the Linux Foundation. But of course, that wasn't enough. We need to then start to build the management and the orchestration around that. So we teamed up with others and started to kind of build this Kubernetes-based community. You know, Docker just recently brought ContainerD into the CNCF, as well, as another layer. They are within the equation. But by building this, it's almost just Russian doll of capability, right, you know, you're able to go from one paradigm, whether it's a serverless paradigm running containers, or having your microservices become use in serverless or having Code First kick off something, you can have these things work well together. And I think that's the most exciting part of what we're doing at Kubernetes, what we're doing in serverless, and what we're doing, say, in this Code First world. >> So, development's always been kind of an art form. How is that art form evolving and changing as these trends that you're describing-- >> Oh, that's a great, I love that. 'Cause I always think of ourselves as computer science artists. You and I haven't spoken about that. That's awesome. Yeah, because, you know, it is an art form, right? Your screen is your canvas, right, and colors are the services that you can bring in to build, and the API calls, right? And what's great is that your canvas never ends, because you have, say, a cloud infrastructure, which is infinitely scalable or something, right? So, yeah. But the definition of the developer is changing because we're kind of in this next phase of lowering concept count. Remember I told you this lowering of concept count. You know, I love those O'Reilly books. The little cute animals. You know, as a developer today, you don't have to buy as many of those books, because a lot of it is done in the API calls that you've used. You don't write sorting algorithms anymore. Guess what, you don't need to do speech to text algorithms. You don't need to do some analysis algorithms. So the developer is becoming a cognitive developer and a data science developer, in addition to a application developer. And that is the future. And it's really important that folks skill up. Because the demand has increased dramatically in those areas, and the need has increased as well. So it's very exciting. >> So the thing about that, that point about cognitive developer, is that in the API calls, and the reason why we don't buy all those books is, the codes out there are already in open source and machine learning is a great example, if you look at what machine learning is doing. 'Cause now you have machine learning. It used to be an art and a science. You had to be a great computer scientist and understand algorithms, and almost have that artistic view. But now, as more and more machine learning comes out, you can still write custom machine learning, but still build on libraries that are already out there. >> Exactly. So what does that do? That reduces the time it takes to get something done. And it increases the quality of what you're building, right? Because, you know, this subroutine or this library has been used by thousands and thousands of other people, it's probably going to work pretty well for your use case, right? But I can stress the importance of this moment you brought up. The cognitive data application developer coming together. You know, when the Web happened, the development market blew up in orders of magnitude. Because everybody's is sort of learning HTML, CSS, Javascript, you know, J2E, whatever. All the things they needed to build, you know, Web Uize and transactional applications. Two phase commit apps in the back, right? Here we are again, and it's starting to explode with the microservices, et cetera, all the stuff you mentioned, but when you add cognitive and data to the equation, it's just going to be a bigger explosion than the Web days. >> So we were talking with some of the guys from IBM's GBS, the Global Business Services, and the GTS, Global Technology Services, and interesting things coming out. So if you take what you're saying forward, and you open innovation model, you got business model stacks and technology stacks. So process, stacks, you know, business process, and then technology, and they now have to go hand-in-hand. So if you take what you're saying about, you know, open source, open all of this innovation, and add say, Blockchain to it, you now have another developer type. So the cognitive piece is also contributing to what looks like to be a home run with Blockchain going open source, with the ledger. So now you have the process and the stacks coming together. So now, it's almost the Holy Grail. It used to be this, "Hey, those business processor guys, they did stuff, and then the guys coded it out, built stacks. Now they're interdependent a bit. >> Yeah. Well I mean, what's interesting to me about Blockchain, I always think of, at this point about business processes, you know, business processes have always been hard to change, right? You know, once you have partners in your ecosystem, it's hard to change. Things like APIs and all the technology allows it to be much quicker now. But with Blockchain, you don't need a human involved in the decision of who's in your partner network as long as they're trusted, right? I remember when Jerry Cuomo and Chris Ferris, in my team, he's the chairman of the Blockchain, of the hyperledger group, we're talking initially when we kind of brought it to the Linux Foundation. We were talking a lot about transactions, because you know, that was one of the initial use cases. But we always kind of new that there's a lot of other use cases for this, right, in addition to that. I mean, you know, the government of China is using Blockchain to deal with carbon emissions. And they have, essentially, an economy where folks can trade, essentially, carbon units to make sure that as an industry segment, they don't go over, as an example. So you can have people coming in and out of your business process in a much more fluid way. What fascinates me about Blockchain, and it's a great point, is it takes the whole ecosystem to another level because now that they've made Blockchain successful, ecosystem component's huge. That's a community model, that's just like open source. So now you've got the confluence of open source software, now with people in writing just software, and now microservices that interact with other microservices. Not agile within a company, agile within other developers. >> Angel: Right. >> So you have a data piece that ties that together, but you also have the process and potential business model disruption, a Blockchain. So those two things are interesting to me. But it's a community role. In your expert opinion on the community piece, how do you think the community will evolve to this new dynamic? Do you think it's going to take the same straight line growth of open source, do you think there's going to be a different twist to it? You mentioned this new persona is already developing with cognitive. How do you see that happening? >> Yes, I do. There's two, let's say three points. The first on circling the community, what we've been trying to do, architecturally, is build an open innovation platform. So all these elements that make up cloud, data, AI, are open so that people can innovate, skills can grow, anything, grow faster. So the communities are actually working together. So you see lots of intralocks and subcommittees and subgroups within teams, right? Just say this kind of nesting of technology. So I think that's one megatrend that will continue-- >> Integrated communities, basically. >> Integrated communities. They do their own thing. >> Yeah. >> But to your point earlier, they don't reinvent the wheel. If I'm in Cloud Foundry and I need a container model, why am I going to create my own? I'll just use the open compute initiative container model, you know what I'm saying? >> Dave: And the integration point is that collaboration-- >> Is that collaboration, right. And so we've started to see this a lot, and I think that's the next megatrend. The second is, we just look at developers. In all this conversation, we've been talking about the what? All the technology. But the most important thing, even more so than all of this stuff, is the how. How do I actually use the technology? What is the development methodology of how I add scale, build these applications? People call that DevOp, you know, that whole area. We at IBM announced about a year and a half ago, at Gene Kim's summit, he does DevOps, the garage method, and we open sourced it, which is a methodology of how you apply Agile and all the stuff we've learned in open source, to actually using this technology in a productive way at scale. Often times people talk about working in theses little squads and so forth, but once you hire, say you've got 10 people in San Francisco, and you're going to hire one in San Ramon, that person might as well be on Mars. Because if you're not on the team there, you're not in the decision process. Well, that's not reality. Open source is not that way, the world doesn't behave that way. So this is the methodology that we talked about. The how is really important. And then the third thing, is, if you can help developers, interlock communities, teach them about the how to do this effectively, then they want samples to fork and go. Technology journeys, physical code. So what you're start to see a lot of us in open source, and even IBM, is provide starters that show people how to use the technology, add the methodology, and then help them on their journey to get value. >> So at the base level, there's a whole new set of skills that are emerging. You mentioned the O'Reilly books before, it was sort of a sequential learning process, and it seems very nonlinear now, so what do you recommend for people, how do they go about capturing knowledge, where do they start? >> I think there's probably two or three places. The first one is directly in the open source communities. You go to any open source community and there's a plethora of information, but more so, if you hang out in the right places, you know, IRC channels or wherever, people are more than willing to help you. So you can get education for free if you participate and contribute and become a good member of a community. And, in fact, from a career perspective today, that's what developers want. They want that feeling of being part of something. They want the merit badge that you get for being a core committer, the pride that comes with that. And frankly, the marketability of yourself as a developer, so that's probably the first place. The second is, look, at IBM, we spend a huge amount of time trying to help developers be productive, especially in open source, as we started this conversation. So we have a place, developer.ibm.com. You go there and you can get links to all the relevant open source communities in this open innovation platform that I've talked about. You can see the methodologies that I spoke about that is open. And then you could also get these starter code journeys that I spoke about, to help you get started. So that's one place-- >> That's coming out in April, right? >> That's right. >> The journeys. >> Yeah, but you can go now and start looking at that, at developer.ibm.com, and not all of it is IBM content. This is not IBM propaganda here, right? It is-- >> John: Real world examples. >> Real world examples, it's real open source communities that either we've helped, we've shepherded along. And it is a great place, at least, to get your head around the space and then you can subset it, right? >> Yeah. So tell us about, at the last couple of minutes we have, what IBM's doing right now from a technology, and for developers, what are you guys doing to help developers today, give the message from what IBM's doing. What are you guys doing? What's your North Star? What's the vision and some of the things you're doing in the marketplace people can get involved in? You mentioned the garage as one. I'm sure there's others. >> Yeah, I mean look, we are m6anically focused on helping developers get value, get stuff done. That's what they want to do, that's what our clients want to do, and that's what turns us on. You build your art, you talk, you're going back to art, you build your drawing, you want to look at it. You want it to be beautiful. You want others to admire it, right? So if we could help you do that, you'll be better for it, and we will be better for it. >> As long as they don't eat their ear, then they're going to be fine. >> It's subjective, but give value of what they do. So how do they give value? They give value by open technologies and how we've built, essentially, cloud, data, AI, right? So art, arts technology adds value. We get value out of the methodology. We help them do this, it's around DevOps, tooling around it, and then these starters, these on-ramps, right, to getting started. >> I got to ask you my final question, a more personal one, and Dave and I talk about this all the time off camera, being an older guy, computer science guy, you're seeing stuff now that was once a major barrier, whether it's getting access to massive compute, machine learning, libraries, the composability of the building blocks that are out there, to create art, if you will, it's phenomenal. To me, it's just like the most amazing time to be be a computer scientist, or in tech, in general, building stuff. So I'm going to ask you, what are you jazzed up about? Looking back, in today's world, the young guns that are coming onto the scene not knowing that we walked barefoot in the snow to school, back in the old days. This is like, it's a pretty awesome environment right now. Give us personal color on your take on that, the change and the opportunity. >> Yeah, so first of all, when you mentioned older guys, you were referring to yourselves, right? Because this is my first year at IBM. I just graduated, there's nothing old here, guys. >> John: You could still go to, come on (laughs). >> What does that mean? Look you know, there's two things I'm going to say. Two sides of the equation. First of all, the fundamentals of computer science never go away. I still teach, undergrad seminars and so forth, and you have to know the fundamentals of computer science. That does not go away because you can write bad code. No matter what you're doing or how many abstractions you have, there are fundamental principles you need to understand. And that guides you in building better art, okay? Now putting that aside, there is less that you need to know all the time, to get your job done. And what excites me the most, so back when we worked on the Web in the early 90s, and the markup languages, right, and I see some in the audience there, Arno, hey, Arno, who helped author some of the original Web standards with me, and he was with the W3C. The use cases for math, for the Web, was to disseminate physics, that's why Tim did it, right? The use case for XML. I was co-chair of the mathematical markup language. That was a use case for XML. We had no idea that we would be using these same protocols, to power all the apps on your phone. I could not imagine that, okay? If I would have, trust me, I would have done something. We didn't know. So what excites me the most is not being able to imagine what people will be able to create. Because we are so much more advanced than we were there, in terms of levels of abstraction. That's what's, that's the exciting part. >> All right. Dr. Angel Diaz, great to have you on theCUBE. Great inspiration. Great time to be a developer. Great time to be building stuff. IOT, we didn't even get to IOT, I mean, the prospects of what's happening in industrialization, I mean, just pretty amazing. Augmented intelligence, artificial intelligence, machine learning, really the perfect storm for innovation. Obviously, all in the open. >> Angel: Yes. Awesome stuff. Thanks for coming on the theCUBE. Really appreciate it. >> Thank you guys, appreciate it. >> IBM, making it happen with developers. Always have been. Big open source proponents. And now they got the tools, they got the garages for building. I'm John Furrier, stay with us, there's some great interviews. Be right back with more after this short break. (tech music)