>> Announcer: From Austin, Texas, it's theCUBE covering DockerCon 2017. Brought to you by Docker and support from its ecosystem partners. (techno music) >> Welcome back. Hi, I'm Stu Miniman, joined with Jim Kobielus. You're watching theCUBE's SiliconANGLE Media's production of DockerCon 2017. We're the worldwide leader in live enterprise tech coverage. And we can't finish any DockerCon without having Jerry Chen on. So, Jerry, partner with Greylock, always a pleasure to interview you. We've had you on the Amazon shows a lot, Docker, other ecosystem shows, so, great to see ya. >> Stu, Jim. Hey, thanks for having me, as always. It's great to be here. >> Alright, so first of all, I mean, you invested back in the dotCloud days. Could you imagine, when you were meeting with Solomon and those guys and everything that we'd be here with 5,500 people as to where they'd go? What's your take on the growth? >> Every year just blows my mind, both in open-source community developers, ecosystem partners, and more recently, past year and a half, the enterprise customers that take Docker seriously, or replatformed applications on Docker, amazes me. I think I did an investment in 2013, and there were a few hundred thousand downloads of Docker, now there's billions and billions of containers being pulled. When I talk to CIOs that I deal with frequently, they're like, "Docker containers, what is this thing, pants?" And then, (laughter) three and a half, four years later, I can't have a conversation without a Fortune 500 CIO without talking about their Docker container strategy. >> By the way, I hear if you do send back a belt or something that's broken to the Docker people, they'll fix it for you, and maybe send some whale stickers. >> It's like the old school Nordstroms where they take any return. They're this urban store, with the four tires return to Nordstrom, return some pants, you'll be fine. >> You know, we work on container strategy, but we're also your repair shop for you know, men's apparel. So, it's always interesting to look at-- >> Jim: Integration fabric. >> Brilliant. You know, the maturation of technology, of ecosystem, of monetization. I feel like you talked about the growth of the containers. We've seen the ecosystem. It's gone through some fits and spurts and changes over the last couple of years. I think we're really well-received this week. And then there's the money maturation and how they mature that. What do you see? How does open-source fit into your investment strategy, and any commentary on Docker and beyond? >> I was thinking about this on the flight over here today. Open source today is very different than open source five years ago, 10 years ago, as 15. So what what Red Hat did 20 years ago, is very different than what Xen tried to do 10 years ago. When I was at VMware, very different from what Docker is doing today. And it's different in a couple ways. I think the way you monetize is different. Because you have cloud, and cloud changes things. The ecosystem's very different, because all of a sudden the developers, contributors, are not just kind of your misfits and rebels working on the weekends. They are Fortune 100, Fortune 500 companies. Their jobs are now dedicated to this. And then the business models of the developers' ecosystem, how you work with them is very different. So before, you had maybe one or two models to make money in open source. Or one or two ways to develop a community. We did that at Red Hat, which Greylock was lucky enough to be investors in years ago. I was at VMware around Cloud Foundry, we built that. We had a model mine, we had a spring source as well, and what you've seen Docker in the past three or four years, is they're really pioneering a way to bring open source and community ecosystem into the next 10-20 years. So I think it's one to watch. I think Solomon's probably as good as anybody understanding what developers need. >> So a little broader, what's your thoughts on developers today? You actually made the comment coming over, there's two big developer shows this week. You've got F8 and you've got DockerCon, two very different communities. >> Right, it's kind of funny. There's always this sense of, do you consider yourself a developer? So if I write a line of JavaScript, am I a developer? My two cents is yes. If I'm a developer, from JavaScript to Swift to Docker to cURL hacking, it's all great. But if you look at those two conferences, you have F8 going on right now, and the announcements there around augmented reality and messaging, and it's trying to be a platform, but they're doing many of the same things. You have a distribution platform be it Messenger or Facebook, and they're open sourcing technologies around the camera, the lens, the filters, to have developers a) go through the channel, b) add apps or widgets. It's really beyond my ability to comprehend these filters, but Docker today announced a couple great projects: Moby and Linux Kit, much the same way as trying to give tools to the ecosystem developers to build what they want. I think what you've learned is, if you give developers the building blocks, the "Legos" as they call it today, they're going to build some awesome structures. >> Jim was, we talked about coming in here as the role of how data science fits into the developers, and developer is such a broad term, as to what we have here. >> One of the core themes I have is that the data scientist is the nucleus of next generation developer because much of the IP that's being built in the applications now, is statistical models and machine learning and so forth, driving recommendation, but much of that development is being containerized using new tool kits and so forth. But it needs to be more containerized so you can deploy statistical predictive models, machine learning, deep porting to routing the string ecosystem into a hybrid cloud to perform various functions. >> Right now there's, in most companies, there's a data engineer, there's a data scientist, and the two typically work hand in hand. >> Jim: One manages Hadoop, the other one does the modeling. >> Does the modeling, so one speaks in R and Python and works in Jupyter Notebook, the other person runs on Hadoop or database or Redis. The two need to work together and so what you're seeing now and obviously we're investors of Cloudera, that's another great open source company, what you see now is either a) a set of tools and technologies to either blend the two together in some cases, either enable engineers to be more data scientists, or enable data scientists to be more engineers, but also see a bunch of technology tools that say, no, two different roles, I'm going to create tools purpose-built for the data scientists, create tools purpose-built for the power of a data engineer. And I think there's space for both to the extent that you have applications running from news feed or ads to predicting how my self-driving car should make a left turn, you're going to need tools that are used by both types of populations. >> I think Cloudera now has a collaboration environment in the data science department. IBM has something very similar with what they're doing, so it's a team that has specialties such as coders, such as data modelers and data engineers. Point well taken. Cloudera's made a major entrance into that space of collaborative development, of these rich stacks of IP, essentially, that include deterministic program code, but also probabilistic models in a deepening stack. >> I think you've seen Cloudera definitely follow that path from Hadoop and low-level file system HDFS, to these high-level tools for data scientists that's becoming a platform for machine learning for these next generation applications. I think you see Docker in the infrastructure analogy doing low-level tools like Project Moby and Linux Kit, to high-level services around Docker Datacenter. So you can either have the basic tools for your low-level developer, or for the system admin or administrator who wants to operate or run the cloud, you have tools for him or her, too. >> It's interesting, you look at some of these projects and some of the maturation and pivots you see. We talked about dotCloud went over to Docker. You see a bunch of open stock companies that are now Kubernetes companies. I see companies that were big data, they're now, "Oh, I'm an AI or ML company." It's always like, it's usually not the tool, it's the wave. What is the driver? Is data the driver of our next wave there? Is it the application? Is it some combination of the two? Those are the two that I usually look at. Follow the data, follow the application. >> I would say it's data driving. It's really data application, it's data, and the applications make use of the data. Algorithms, I think, is a component. They're important, but they're a component. So what you see now is, to be on the right side of history, data is outstripping compute and storage, so the amount of videos and center data that we're generating from our phones, our cars, our homes, that is outstripping most of the other charts in compute, networking, whatever. That's definitely kind of a rising tide or a wave, as Stu was saying. Now how do we extract data, or value from this data? And historically, because you didn't have infrastructure, that cloud, or compute capacity to make use of this data, it was kind of stranded, so what you've seen in generation technologies like Hadoop or big data or cloud technologies like Docker did, is distribute your applications across a cloud. That's actually enabling you to now build applications to get value out of this data. And that value can be something like forecasting your sales this quarter. It can be about figuring which shade of brown belt you should wear with your pants, going back to our clothing analogy. Or it could be like, let me build a model around how this car or this drone should drive or fly itself. So you combine the vast amount of data, nearly infinite resource of compute, with these machine-learning or AI techniques. Machine learning is one AI technique, but all these other techniques, you can build another generation application, this new intelligent application to power everything from your home, your car, your watch, or your enterprise app, as wonderful as that is. >> Much of the sea change is less and less coding or programming is actually being done or needs to be done because more of the application logic is being distilled directly from the data in the form of machine learning. There's automated machine learning tools that are coming. Google has been a major investor as is Facebook in automated machine learning. >> I would say application logic from the inside, right. So in my mind, application logic, an application is reflecting business process. Hire to fire, order to cash. You still need a program that does logic. Data in itself, or AI in itself without that context, without that business process, is meaningless, right. Just having a model around Jim or Stu, it doesn't matter unless you're trying to buy something. Google pioneered machine learning in a workflow perfectly. You're searching for something, they knew who you were based upon history, you're searching the right ad and say, "Oh, you really want to buy a car, you want to buy a house." So in the workflow, or in the application logic of a search, they used ML to serve you timely information. Now if you're an enterprise, you're looking at help desk tickets, be it ITSM like ServiceNow, or support tickets like Zendesk supporting B to C support tickets. That's a workflow, there's application logic. They take information on a user or a grumpy customer, and they do things like automatically respond to a help ticket, reset your password, provision a server. So I think when you have AI or have applications using this data in the context of a business process, that's magic. And I think we're seeing some core technologies like TensorFlow out there that are super compelling. But we're seeing a generation of developers and founders take that technology, apply it to a problem, it could be HR or CRM, ITSM, or true vertical. Construction, finance, health care. >> Jim: Streaming media analytics is a core area where that's coming in. >> Media analytics because there's a ton of data. Understand what you watch and what you want to see, and so you apply things to a vertical, like health care, or apply the technology to a problem space like media analytics, and you have a wonderful application and hopefully a great company. >> Jerry, we've talked a lot at the cloud shows about how do the startups maintain relevant and get involved when there's all of these platforms. We talked about what Google does, Amazon of course is eating the entire world in everything. Microsoft is making lot of moves here. How do companies, what do you look for? Has your investment strategy changed at all in the last couple of years? >> It is daunting. I think about this a lot in terms of business models and defensibility, and the question goes, what are the sustainable moats you can build around your business as a startup anymore? 'Cause you feel like economies of scale and ecosystems, network effects, those were historically big defensive moats for a Windows operating system. Now those apply to Facebook's platform, Apple's platform, or AWS. They have scale and they have network effects for the ecosystem, so now your startup is saying, okay, how can I either a) overcome those moats, or b) how can I develop my own IP or my own moats around myself that I can actually sustain and thrive in this generation. I think you got to play a different game. As a startup, you're not going to try to out-scale Google or Microsoft; leave that to Amazon and those three or four players. But you can get scale in a domain, so either a problem space like autonomous vehicles, security is a great one, or vertical construction or health care. You redefine the market that you can dominate, can you build your own moat around that IP. >> It's interesting. did you hear Adrian Cockcroft who went from Battery Ventures over to AWS. He's like, "Well, rather than go startup that business, "come build that next thing at Amazon "and we'll do it there." Is that a viable way for people with the entrepreneurial spirit to go be part of that two-pizza team doing something cool inside a large platform? >> I think Adrian probably has motivation and more developers on Amazon now, but I would say most of our companies, not all, but a lot of them started at Amazon. Some start in ads, some start in Google, some start with their own data centers. I think what they believe is they'll get started in one of these clouds but I don't believe, so we talked about this first, it's not a one-cloud-rules-all world. I think there'll be three or four, if not more, clouds in every different geography from Europe to Asia to Russia to the US, will have different clouds, different players. So I think it's fine to get started in Amazon and be a two-pizza team with the other two-pizza team, but over time I see these applications being cross-cloud, and that's where something like Docker comes into play. Docker wants to be cross-cloud, better than any other technology out there. >> On some level, actually, the moat could be, or increasingly is, the training data that drives the refinement of your AI, like Tesla is a perfect example. The self-driving capabilities that they built into the vehicle, they have now a few years' worth of rich test data, training data I should say, that is a core moat in terms of continuing refinement of those algorithms. So that gives you sort of an example of some startup might come along with some very specialized application that takes the consumer world by storm and then they build up some deep well of training data in some very specialized area that becomes their core asset that their next competitor down the pipe doesn't have. >> It has to be a set of data that's unique or proprietary. You're not going to basically out-train your model on cat photos from Google, right? So it has to be a combination of either proprietary data or a combination of data sources that you can stick together. So it's not just one data source, I believe you have to combine multiple data sources together. >> So Jerry, sitting over Jim's shoulder is VMware's booth. I haven't talked about VMware at all this week. You worked at VMware, I've worked with VMware since pretty early days. What advice would you give VMware in the containerized cloud future? How should they be doing more to be part of more conversations? >> I think it's amazing that they have a presence here in the size and scale. The past couple years they're really done a lot to embrace containers and Docker, so I think that's first and foremost. They've done a couple great moves lately. Embracing Amazon last year, with VMware on Amazon, was a big move. Embracing containers with some of their cloud and data technologies I think was an aggressive move too. So I think they're moving in the right direction. I think what they need to understand is, are they going to revolutionize themselves and push these new technologies aggressively, or are they going to keep hanging onto some of their old businesses? For any company of their size and scale, they have multiple motivations, but I think they're making the right steps. So five years ago, or four years ago, I don't think they would have taken this DockerCon seriously. I don't think they were exhibitors at the first DockerCon. But in the past 24 months they've done some amazing moves, so I would say it makes me smile to see them take these great steps forward. >> Jerry, I want to give you the last word. Any cool companies we should be looking at, or things that are exciting to you without giving away trade secrets? >> I can't broadcast the companies I want because everyone else is going to chase those investments. I don't know, I think I'm going to enjoy spending time, actually less with the companies here but a lot with the developers and customers, because I think by the time they have a booth here, everybody knows the company's investment is probably too far along maybe for me to invest, maybe not. But talking to developers to hear what are their friction points? I think when you hear enough friction either in this ecosystem or another ecosystem or at AWS or VWware, then there's something there, you just got to scratch. >> I was talking to some of the people working the booths and they just said the quality of the attendees here, you learn something with every single person you talk to, and there's only a few shows that say that. Amazon reinvented one, the quality of the attendees always real good, this one and a few others. >> I think people who come here by definition are learners, both the companies and the individuals, and you want to surround yourself with learners, people who are open and honest and always learning. >> Jerry, I think that's a perfect note to end it on. We are always learners here and helping to help our audience in trying to understand these technologies, so Jerry Chen, always a pleasure. And we'll be back with the wrap-up here of day one DockerCon 2017. 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