>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.

>> from our studios in the heart of Silicon Valley, Palo Alto, California. It is a cute conversation. >> Levan, Welcome to this Cube conversation. I'm John for a host of the Cube here in our Cube Studios in Palo Alto, California. Harder Silicon Valley world startups are happening on the venture capitalists air. Here we have with us. O'Neill, Deli Wall, Who is the general partner Amplify Partners and Founder co found with Mike Dauber. You guys have a very successful firm. I've known you since the beginning. When you started this firm. You guys were very successful on your third fund. Congratulations. Thank you. Great to see you. Thanks for coming in. It's always fun >> to be back. Yours? First time we're doing it in person. >> Local as it posted out of the conference. Yeah. Got our studio here. We're kicking off two days a week. Soon to be five days or weeks. Folks watching studio will be open for a lot more. Start up coverage. So great to have you in. And congrats on 10 years for you guys. 10 years of the Cube. 10th year of'em world would do in a big special. So nice we're excited. Well, for another great 10 years have been a lot of fun. A lot of interesting things happen those 10 years and again, you've been on the track to foot during that time. Yeah, on, by the way, Congratulations, fastly when public, thank you very much. And you also investing early investor in Data Dog, which you probably can't comment on, but they look like they're gonna go public. It's a great business >> and it's moving the right direction. And I think they got a lot of happy users. So there's more good stuff in the future for them. >> So you guys came out early, Made big bets. They're paying off two of them. Certainly one. Did another one come around the bikemore. Take it. Give us update on Amplify Partners Current fund. Third Fund gives the numbers. How much? What do you guys investing in with some of the thesis? What's the vision? >> Yeah, the vision is really simple. So amplify has been around from the beginning to work with technical founders. And really, if you wanted to stop there, you could you know, we're the people that engineers, academics, practitioners, operators that they go to get their first capital when they are thinking about starting a company or have a niche that they just feel the need to scratch. We tend to be first call for those folks a lot of times before they even know that they're going to start something. And so we've been doing that. Investing at seeding Siri's A with those people in these really technical enterprise markets now for seven years. Third fund most recent funds a $200,000,000 fund and that has us doing everything from crazy pie in the sky. First check into, ah, somebody with wild vision to now bigger Siri's a lead Rounds, which we're doing a lot more of two. >> So on the business model, just to get a clear personal congratulations Really good venturing by the way. That's what venture capital should be First money in, You know, people not doing the big round. So that's a congratulated, successful thank you now that you have 200,000,000 Plus, are you file doing follow on rounds? Are you getting in on the pro rat eyes? Are you guys following on? Because he's Sonny's big head, sir. Pretty, pretty big. >> Yeah, we've been doing that from the beginning and I think we've always wanted to be people who will start early and go along. We've invested in every round that fastly did we invested in every round that data dog did. So yeah, we're long term supporters and we can go along with the company's. But our differentiation isn't showing up and being the guys who were gonna lead your Siri's g round at a $3,000,000,000 valuation, which might as well be your AIPO were really there to help people figure out how to recruit a Kick ass team and figure out how to find product market fit and get that engine working >> and also help be a friend of the on the same side of the tables and rather than being the potentially out of the side. So the question is, I know you guys do step away and don't go on board. Sometimes you do. Sometimes you don't. Was there a formula there? Do you go on the boards as further in the round? You happy the relief? It's a >> mix of, uh, you know, we talked about a couple of these cos fastly. I've been on the board since Day zero and data dog. I was never on the board. And you know what we do tend to be those pretty active. So people come work with us when they go. I've got this vision. I know where I want to go. But when I think about the hard things I've got to do over the 1st 2 to 3 years of a company's life, you know who I want by my side and not the person who wants to be my boss or tell me what to do or tell me why they need to own 1/3 of my company or control four seeds on my board. But who kind of what's it wants to sit shoulder to shoulder with me and probably has a long list of companies that look just like mine. Uh, that tell me that they're going to decent partner. >> We've had a lot of fun together. You and Mike the team and fly. Great party. Great networking. You gotta do that. >> Thank you. Great. Great party. Should hopefully my >> tombstone. Well, you gotta have the networking, and that's always good. Catalyst. That lubricant, if they say, is to get people going. But you guys were hanging out with us and the big data space that had Duke World. We saw Cloudera got to activist board members. That's not looking good there. It's unfortunate big friend of Amer Awadallah, but what ended up happening was cloud Right Cloud kind of changed the game a little bit, didn't change big data as an industry was seeing eye machine learning booming. So, you know, big data had duped change certainly cloud our speculation. But looking back over those 10 years, you saw the rise of the cloud really become Maur of a force than some people thought that most people thought Dev Ops really became the cultural shift. If I had to point to anything over the 10 years, it's Dev Ops, which is implies day to talk about your reaction to that because certainly independent on enabler, but also change the game a bit. >> It has its exploded. There's a couple things in there, so I think there's been a lot of innovation that's coming in the cloud platforms. There's a lot of innovation that cloud platforms have sucked up. We look at that. A lot of guys who back startups, one of things we always say is Hey, is this a primitive? Is this an infrastructure primitive? Because if it is, it's probably gonna be best delivered by a big platform unless you're able to deliver a very compelling and differentiated solution or service around it. And that's different. You know, it's it's different than having a solely a a p I accessible primitive that, you know you would swap out with the next thing if it was, you know, two cents cheaper or 2% faster. So when I think about what's been happening in the cloud, this kind of cloud to, oh, phenomena starts coming up, which is a lot of hell that excited very early on. It was about storage and compute and the real basic building blocks. But now you see people building really compelling experiences for developers, for database engineers for application developed owners all the way up and down this stack that yeah, there cloud companies, but they look a heck of a lot like more like solutions. And, you know, we've mentioned a couple companies in our portfolio that air going great. But there there's a ton of companies that we admire. You know, I look at what the folks that at Hashi Corp have done and what they continue to do. You know what a great business in in security and in giving people automation and configuration that that hasn't been there before. That's a phenomenal I >> mean, monitoring you mentioned is a monitoring to point out going on, he said. Pager duty Got a dining trace. These companies public this year, both public, and you got more coming around the corner, you got analytics is turning. That's calling it mean monitoring has been around for a long time. Observe ability. Now it's observe ability is the monitoring two point. Oh, and that's taking advantage of this Dev Ops Growth. Yeah, this is really the big deal. >> Yeah, well, it's if you're really getting into. And what a lot of this comes down to is velocity, right? A lot of people are trying to deliver software faster, deliver it more reliably, take away the bottlenecks that air between the vision that a product person has the fingers on the keyboard and the delightful experience that a user gets and that has a lot of gates. And I think one of the things that Dev Ops is really enabled is how do you shrink that time? And when you're trying to shrink that time and you're trying to say, Hey, if someone's can code it, we can push it well, that's a great way to do things except if you don't know what you've pushed and things were failing. So as velocity increases, the need to have an understanding of what's going on is going right alongside of it. >> So I want to get your thoughts on enterprise scale because cloud 2.0, it really is about enterprise. You guys have invested in pure cloud native startups. You've invested a networking invested in open sores. You guys house will have, ah, struggle. You are. But I have a strong view on Dev, Ops and Cloud to point out. But the enterprise is now experiencing that, and you guys also done a lot of enterprise deals. What's the intersection of the enterprise as it comes in with cloud two point? Oh, you're seeing Intelligent Edge being discussed Hybrid multi cloud, these air kind of the structural big kind of battle grounds with the changes. How do you guys look at that? How do you invest in that? How do you look for startups in that area? >> Yeah, well, I think we invest in it by starting from the perspective of the customer. What's the problem? And the problem is, a lot of times people know their security. There's compliance. And a lot of cases. There's a legacy infrastructure, right? But the it's not a green field environment is nowhere more applicable than in the enterprise. And so when you think about customers that are gonna need to accommodate the investments the last five and 10 years as well as this beautiful new vision of what the future is, you know you're talking basically talking about every enterprise CEOs problems. So we think a lot about companies that can solve those riel clear enterprise pain points security. One of them, um, we've had a bunch of successful cloud security companies that have been acquired already. We've got great stuff in compliance and data management and awesome company like Integris. That's up in Seattle and in really making sure that projects and software works well with legacy and more traditional enterprise environments, companies like replicated down in L. A. Um, you know, those folks have really figured out what it means to deliver modern on premise software and modern on premise really is, you know, in your V p c in your own environment in your own cloud. But that's on Prem Now that is what on Prem really looks like no one's rack and stack and servers in the closet. It's cloud operations. But if you're going to do that and you're gonna integrate all those legacy investments you've made in an audit, Maxis control et cetera, and you wanna put that together with modern cloud applications, your sass vendors, et cetera. You know you can't really do that in the native cloud unless you can really make it work for the enterprise. >> What is some of the market basket sectors that you see? Where the market second half of our market sectors that have a market basket of companies forming around it? You mentioned drivability. Obviously, that's one we're seeing. Clear map of a landscape developed there. Yeah, okay. Is there other areas just seeing a landscape around this cloud to point out that that are either knew or reconfigurations of other markets? Machine learning What's what. The buckets? What the market's out there that people are clustering around with some of the big >> high level. Well, I think one of things you're gonna see talking about new markets and people people. There's a bunch of It'll tell you what's already happening in history today. But if you want to talk about what's coming, that isn't really on people's radar screen, I think there's a lot that's happening in machine learning and data science infrastructure. And if you're a cloud vendor in the public cloud today, you are really ramping up quickly to understand what the suite of offerings are that you're gonna offer to both ML developers as well as traditional, you know, non machine learning natives to help them incorporate. You know what is really a powerful set of tools into their applications, and that could be model optimization. It could be, um, helping manage cost and scalability. It could be working on explain ability. It could be working on, um, optimizing performance with the introduction of different acceleration techniques. All of that stack is really knew. You know, people gobbled up tensorflow from Google, and that was a great example of what you could do if you turned on ml specific. You know, tooling for for developers. But I think there's a lot more coming there, and we're just starting to see the beginning. >> It's interesting you bring this up because I've been thinking about this and I really haven't been talking about a publicly other than the cloud to point. It was kind of a generic area, but you're kind of pointing out the benefits of what cloud does. I mean, the idea of not having to provision something or invest a lot of cash to just get something up and running fast with this machine learning tooling that's the big problem was stacking everything up and getting it all built >> right goes back. The velocity were talking about earlier, right? >> So velocity is the key to success. Could be any category to be video. It could be, um, you know, some anything. So we're >> also seeing another. The other side of it is, is another form of velocity is we're going to Seymour that's happening and things that look like low code or no code, so lowering the barriers for someone doesn't have to be a true native or an expert in domain, but can get all the benefits of working with, Let's say, ml tooling, right? How do you make this stuff more accessible? So you don't need a phD from Berkeley or Stanford to go figure it out right? That's a huge market. That's just stop happening. We've got a ah phenomenal come way company in New York called Runway ML that has huge adoption. Their platform and their magic is Hey, here's how we're gonna bring ML to the creative class. If you're creative and you want to take advantage of ML techniques and the videos you're working on, the content that you're creating, maybe there's something you can do here at the Cube. You know, these guys were figure out how to do that and saying, Look, we know you're not a machine learning native. Here's some simple, primitive >> Well, this screen, you know, doesn't talk about video, but serious. We have a video cloud of people have seen it out there, demo ing, seeing highlights going around. But you bring up a good point. If we want to incorporate State machine learning into that, I can just connect to a service. I mean slack, I think, is the poster child for how they grew a service that's very traditional a message board put a great you around it. But the A P I integrations were critical for that. They've created a great way to do that. So this is the whole service is game. Yeah, this is the velocity and adding functionality through service is >> Yeah, And this is this this idea that, um the workflow is what matters. I think it has not traditionally been a thing that we talked a lot about an enterprise infrastructure. It was. Here's your tool. It's better than the previous two or three years ago. Throat the new ones by this one. And now people are saying, Well, I don't want to be wed to the tool. What I really want to understand is a process in a workflow. How should I do this? Right? And if I If I do that right, then you're not gonna be opinionated as to whether I'm using Jiro for you know, you're for managing issues or something else or if it's this monitoring the other. >> So I got to get the VC perspective on this because what you just said, she pointed out, is what we've been talking about as the new I p. The workflow is the I P. That translates to an application which then could be codified and scaled up with infrastructure, cloud and other things that becomes the I P. How do you guys identify that? Is that do you first? Do you agree with that? And then, too, how do you invest into that? Because it's not your traditional few of things. If that's the case, do you agree with it? And if you do, how do you invest in? >> I've modified slightly. It's the marriage of understanding that work flow with the ability to actually innovate and do something different. That's the magic. And so I'll give you a popular problem that we see amongst a lot of start ups that come see us. Uh, I am the best, and I'll pick on machine learning for a second. I've you know, I've got the best natural language processing team in this market. We're going to go out and solve the medical coding and transcription and building problem. Hey, sounds awesome. You got some great tech. What do you know about medical transcription and building? Uh, we gotta go hire that person. Do you know how doctors work? Do you know how insurance companies work. That's kind of Byzantine. How? You know, payers and providers, we're gonna work together. We'll get back to you that companies not gonna be that successful in the marriage of that work. >> Full knowledge. Good idea. Yeah, expertise in the work edge of the workflow. >> Well, traditionally, you get excited about the expertise in attack and what you realize in a lot of these areas. If you care about work full, you care about solutions. It's about the marriage of the two. So when you look across our portfolio in applied A I and machine learning, we've actually got shockingly nine companies now that are at the intersection of, um, machine intelligence and health care, both pre clinical and clinical. And people are like, Wow, that's really surprising for, ah, for an infrastructure firm or an enterprise focus firm, like amplifying we're going. No, you know, there's there's groundbreaking ML technology, but we're also finding that people know there's really high value verticals and you put domain experts in there who really understand the solutions, give them powerful tools, and we're seeing customers just adopted >> and that, unlike the whole full stack kind of integration if you're gonna have domain experts in the edge of that work flow, you have the data gathered. It's a data machine learning. I can see the connection. They're very smart, very clever. So I want to get your thoughts on two areas around this cloud to point. I think that come up a lot. Certainly machine learning. You mentioned one of them, but these other ones come up all the time as 2.0, Problems and opportunities. Cloud one. Dato storage, Computing storage. No problem. Easy coat away. Cloud two point. Oh, Networking Insecurity. Yeah, So as the cloud as everyone went to the cloud and cloud one dato there now the clouds coming out of the cloud on premise. So you got edge of the network. So intelligent edge security if you're gonna have low code and no could have better be secure on the cover. So this has become too important. Points your reaction to networking and security as an investor in this cloud. 2.0, vision. >> Yeah, there's different pieces of it. So networking The closer you go to the edge, you say the word ej and edges, you know, a good bit of it is networking, and it's also executing with limited resource is because we could debate what the edge means for probably three hours. >> Writing is very go there, but what it certainly means is you >> don't have a big data center. That's Amazon scale to run your stuff. So you've got to be more efficient and optimized in some dimension. So people that are really at the intersection of figuring out how to move things around efficiently, deliver with speed and reduce late and see giving platforms to developers at the edge, which, you know if you've one of the big reasons for faster going public was to bring their edge. Developments story out to the larger market. Um, absolutely agree with that as it as it relates to broader security. We're seeing security started, stop being a cyclical trend and started becoming a secular one pretty much at the moment the cloud exploded and those things are not, You know, it's not just a coincidence, as people got Maur comfortable with giving up control of the stuff that that had their arms around for years, a perimeter right at the same time that they say we're going through everything online and connect everything up and get over developers whatever they want and bringing all our partners to our. The amount of access to systems grew dramatically right. At the same time, people handed over a lot of these traditional work flows and processes and pieces of infrastructure. So, yeah, I think a lot of people right now are really re platforming to understand what it means to be to build securely, to deploy securely, to run securely. And that's not always a firewall rack and stack boxes and scan packets type of a game. >> Yeah, I'm serious, certainly embedded. And everything's not just part of the applications everywhere. That's native. Yeah, final question for you. What do you guys investing in now? What's the hot areas you mention? Machine learning? Give a quick plug for your key investments. What's the pitch? The entrepreneur? >> Yeah, so again are pitched. The entrepreneur really hasn't changed from Day zero, and I don't see it changing anytime in the future, which is if you're a world beating technologists, you know you want someone who understand what it's like to work with other world beating technologists and take him from start upto I po And that's the thing that we know how to do both in previous career is as well as in the history of Amplified. That's the pitch. The things that we're really excited right now is, um, what does it look like when the best academic experts in the world who understand new areas of machine learning, who are really able to push the forefront of what we're seeing in reinforcement, learning and machine vision and natural language processing are able to think beyond the narrow confines of what the tech can do and really partner of the domain experts? So there is a lot of domain specific applied A i N M l that we're really excited about thes days. We talked about health care, but that is just the tip of the iceberg we're excited about. Financialservices were excited about traditional enterprise work flows. I'd say that that's one big bucket. Um, we're is excited about the developer as we've ever been. >> You know, you and I were talking before he came on camera for the cube conversation. Around our early days in the industry, we were riffing on the O S. I, you know, open systems interconnect, stack if you look at what that did, Certainly it didn't always get standardize. That kind of dinner is up with T C p I p layer, but still, it changed. That changed the game in the computing industry. Now, more than ever, this trend that we're on the next 10 years is really gonna be about stacks involving and just complete horizontal scalability. Elastic resource is new ways to develop Apple case. I mean a completely different ball game. Next 10 years, your your view of the next 10 years as this 1000 flowers start to bloom with stacks changing in new application methods. How do you see it? Yeah, well, >> what Os? I was a great example of this trend that we go through every few months. So many years. You, you, somebody create something new. It's genius. It's maybe a little bit harder than it needs to be in. At some point, you wanted to go mass market and you introduce an abstraction. And the abstractions continue to work as ways to bring more people in and allow them not to be tough to bottom experts. We've done it in the technology industry since the sixties, you know, thank you. Thank you. Semiconductor world All the way on up. But now I think the new abstractions actually look a heck of a lot like the cloud platforms. Right? They're abstractions. People don't. People want toe. Say things like, I am going to deploy using kubernetes. I want a container package. My application. Now let me think from that level. Don't have don't have me think about particular machines don't have to think about a particular servers. That's one great example developments. The same thing. You know, when you talk about low code and no Koda's ideas, it's just getting people away from the complexity of getting down in the weeds. So if you said, What's the next 10 years look like? I think it's going to be this continual pull of making things easier and more accessible for business users abstracting, abstracting, abstracting and then right up into the point where the abstractions get too generalized and then innovation will come in behind it. >> As I always say in the venture business, cool and relevant works and making things simple, easy use and reducing the steps it takes to do something. It's always a winning formula. >> That's pretty good. Don't >> start to fund a consistent Sydney Ellen. Of course not. The cube funds coming in the next 10 years celebrating 10 years. Great to see you. And it's been great to have you on this journey with you guys and amplify. Congratulations. Congrats on all your success is always a pleasure. Appreciate it. Take care. Okay. I'm here with steel. Dolly. Well, inside the key studios. I'm John for your Thanks for watching.

live from San Francisco it's the cube covering micron insight 2018 brought to you by micron welcome back to the San Francisco Bay everybody we saw the Sun rise in the bay this morning of an hour so we're gonna see the Sun set this gorgeous setting here at Pier 27 Nob Hills up there the Golden Gate Bridge over there and of course we have this gorgeous view of the bay you're watching the cube the leader in live tech coverage we're covering micron insight 2018 ai accelerating intelligence a lot of talk on on on memory and storage but a lot more talk around the future of AI so we got a great discussion here on the auto business and how AI is powering that business Jeff Bader is here is the corporate vice president and general manager of the embedded business unit at micron good to see you again Jeff thanks for coming on and Simon and rigor is the vice president BMW and he's also joined by Vijay Nadkarni who was the global head of AI and augmented reality at Visteon which is a supplier to Automobile Manufacturers gentlemen welcome to the cube thanks so much for coming on thank you so you guys had a panel earlier today which was pretty extensive and just a lot of talk about AI how AI will be a platform for interacting with the vehicle the consumer the driver interacting with the vehicle also talked a lot about autonomous vehicles but Simon watch you kick it off your role at BMW let's let's just start there it will do the same for Vijay and then get into it research portion that we do globally in which is represented here in North America and so obviously we're working on autonomous vehicles as well as integrating assistance into the car and basically what we're trying to do is to get use AI as much as possible in all of the behavioral parts of the vehicle that uses have an expectations towards being more personalized and having a personalized experience whereas we have a solid portion of the vehicle is going to be as a deterministic anesthetic as we have it before like all of the safety aspects for example and that is what we're working on here right now Vijay Visteon is a supplier to BMW and other auto manufacturers yes we are a tier 1 supplier so we basically don't make cars but we supply auto manufacturers of which BMW is one and my role is essentially AI technology adversity on and also augmented reality so in AI there are basically two segments that we cater to and one of them is that almost driving which is fully our biggest segment and the second one is infotainment and in that the whole idea is to give the driver a better experience in the car by way of recommendations or productivity improvements and such so that is so my team basically develops the technology and then we centrally integrate that into our products so so not necessarily self-driving it's really more about the experience inside the vehicle that is the and then on the autonomous driving side we of course very much are involved with the autonomous driving technology which is tested with detecting objects are also making the proper maneuvers for the Waker and we're definitely going to talk about that now Jeff you sell to the embedded industry of fooding automobile manufacturers we hear that cars have I forget the number of microprocessors but there's also a lot of memory and storage associate yeah I mean if you follow the chain you have our simon representing the OEMs Vijay represented the Tier one suppliers were supplier to those Tier one suppliers in essence right so so we're providing memory and storage that then goes in to the car in as you said across all of the different sort of control and engine drone and computing units within the car in particular into that infotainment application and increasingly into the a TAS or advanced driver assistance systems that are leading toward autonomous driving so there's a lot of AI or some AI anyway in vehicles today right presumably yeah affected David who did a wonderful job on the panel he was outstanding but he kind of got caught up in having multiple systems like a like an apple carplay your own system I actually have a bit about kind of a BMW have a mini because I'm afraid it's gonna be self-driving cars and I just want to drive a drive on car for this take it away from me though but but you push a button if you want to talk to a Syrian yeah push another button if you want to talk to the mini I mean it's it's gonna use it for different use cases right exactly may I is also about adaption and is also about integrating so AI is is is coming with you with the devices that you have with you anyway right so your might be an Alexa user rather than a Google assistant user and you would have that expectation to be able to ask to chat with your Alexa in your car as well that's why we have them in the vehicle also we have an own voice assistant that we recently launched in Paris Motorshow which augments the experience that you have with your own assistants because it factors in all of the things you can do with the car so you can say there is a solid portion of AI already in the vehicle it's mainly visible in the infotainment section right and of course I remember the first time I'm sure you guys experienced to that the the car braked on my behalf and then kind of freaked me out but then I kind of liked it too and that's another form of machine intelligence well that out well that counts for you that had not that has not necessarily been done by AI because in in in let's say self-driving there is a portion of pretty deterministic rule based behavior and exactly that one like hitting an object at parking you don't need AI to determine to hit the right there is no portion or of AI necessary in order to improve that behavior whereas predicting the best driving strategy for your 20-mile ride on the highway this is where AI is really beneficial in fact I was at a conference last week in Orlando it's the Splunk show and it was a speaker from BMW talking about what you're doing in that regard yeah it's all about the data right learning about it and and in turning data into insights into better behavior yes into better expected behavior from whatever the customer wants so Vijay you were saying before that you actually provide technology for autonomous vehicles all right I got a question for you could it autonomous - could today's state of autonomous vehicles pass a driver's test no no would you let it take one no it depends I mean there are certain companies like way mo for example that do a lot but I still don't think way mo can take a proper driver's test as of today but it is of course trying to get there but what we are essentially doing is taking baby steps first and I think you may be aware of the SAE levels so level 1 level 2 level 3 level 4 SF and a 5 so we and most of the companies in the industry right now are really focusing more on the level 2 through level 4 and a few companies like Google or WAV or other and uber and such are focusing on the level 5 we actually believe that the level 2 through 4 is the market would be ready for that essentially in the shorter term whereas the level 5 will take a little while to get that so everybody Christmas and everyone we're gonna have autonomous because I'm not gonna ask you that question because there's such a spectrum of self-driving but I want to ask you the question differently and I ask each of you when do you think that driving your own car will become the exception rather than than the rule well I'd rather prefer actually to rephrase the question maybe to where not when because we're on a highway setting this question can be answered precisely in roughly two to three years the the functionality will kick in and then it's going to be the renewal of the vehicles so if you answer if you if you ask where then there is an answer within the next five years definitely if we talk about an urban downtown scenario the question when is hard to answer yeah well so my question is more of a social question it is a technology question because I'm not giving up my stick shift high example getting my 17 year old to get his permit was like kicking a bird out of the nest I did drive his permanent driver on staff basically with me right so why but I mean when I was a kid that was freedom 16 years old you racing out and there is a large generational group growing up right now that doesn't necessarily see it as a necessity right so not driving your own car I think car share services right share who bore the so and so forth are absolutely going to solve a large portion of the technology of the transportation challenge for a large portion of the population I think but I agree with the the earlier answers of it's gonna be where you're not driving as opposed to necessarily win and I think we heard today of course the you know talking about I think the number is 40,000 fatalities on the roadways in the u.s. in the u.s. yeah everybody talks about how autonomous vehicles are going to help attack that problem um but it strikes me talk about autonomous cars it why don't we have autonomous carts like in a hospital or even autonomous robots that aren't relying on lines or stripes or beacons you one would think that that would come before in our autonomous vehicle am I missing something are there are there there there systems out there that that I just haven't seen well I don't know if you've ever seen videos of Amazon distribution centers yeah but they're there they're going to school on lines and beacons and they are they're not really autonomous yeah that's fair that's fair yeah so will we see autonomous carts before we see autonomous cars I think it's a question what problem that solves necessarily yeah it's just as easy for them to know where something is yeah you think about microns fabs every one of our fabs is is completely automated as a material handling system that runs up and down around the ceilings handling all the wafers and all the cartridges the wafers moving it from one tool to the next tool to the next tool there's not people anymore carrying that around or even robots on the floor right but it's a guided track system that only can go to certain you know certain places well the last speaker today ii was talking about it I remember when robots couldn't climb stairs and now they can do backflips and you know you think about the list of things that humans can do that computers can't do it let's get smaller and smaller every year so it's kind of scary to think about one hand is that does the does the concept of Byzantine fault-tolerance you guys familiar with that does that does that come into play here you guys know what that's about I don't know what it is exactly so that's a problem and I first read about it with it's the Byzantine general problem if you have nine generals for one Oh attack for one retreat and the ninth sends a message to half to retreat or not and then you don't have the full force of the attack so the concept is if you're in a self-driving boat within the vehicle and within the ecosystem around the city then you're collectively solving the problem so there these are challenging math that need to be worked out and and I'm not saying I'm a skeptic but I just wanted more I read about it the more hurdles we have there's some isolated examples of where AI I think fits really well and is gonna solve problems today but this singularity of vehicle seems to be we have a highly regulated environment obviously public transportation or public roads right are a highly regulated environment so it's like it's different than curating playlists or whatever right this is not so much regulated traffic and legislation isn't there yet so especially and it's it's designed for humans right traffic cars roads are designed for human to use them and so the adoption to they the design of any legislation any public infrastructure would be completely different if we didn't drive as humans but we have it we have machines drive them so why are robots and carts not coming because the infrastructure really is designed for humans and so I think that's what's going to be the ultimate slow down is how fast we as a society that comes up with legislation with acceptance of behavioral aspects that are driven by AI on how fast we adopt it technically I think it can happen faster than yeah yeah it's not a technology problem as much as it is the public policy insurance companies think about one of the eventually you can think of from from let's say even level four capable car on a highway is platooning yeah right instead of having X number of car lengths to the turn fryer you just stack them up and they're all going on in a row that sounds great until Joe Blow with their 20 year old Honda you know starts to pull into that Lane right so you either say this Lane is not allowed for that or you create special infrastructure essentially that isn't designed for humans there is more designed specifically for the for the machine driven car right how big is this market it's it feels like it's enormous I don't know how do you look at the tan we can talk to the memory I can talk the memory storage part of it right but today memory and storage all of memory storage for automotive is about a two and a half billion dollar market that is gonna triple in the next three years and probably beyond that my visibility is not so good maybe yours is better for sure but it then really driven by adoption rate and how fast that starts to penetrate through the car of OAM lines and across the different car in vijay your firm is when were you formed how long you've been around or vistas be around basically since around 2001 okay we were part of relatively old spun out whiskey on that at work right okay so so alright so that's been around forever yeah for this Greenfield for you for your your group right where's the aw this is transitional right so is it is it is it you try not to get disrupted or you trying to be the disrupter or is it just all sort of incremental as a 101 year old company obviously people think about you as being ripe for disruption and I think we do quite well in terms of renewing ourselves coming from aeroplane business to a motorcycle business to garbage and so I think the answer is are we fast enough I'll be fast enough in adoption and on the other hand it's fair to say that BMW with all of its brands is part of a premium thing and so it's not into the mass transportation so everything that's going to be eaten up by something like multi occupancy vehicle mass transportation in a smaller effort right this is probably not going to hurt the premium brand so much as a typical econo type of boxy car exciting time so thanks so much for coming on the cube you got a run appreciate thank you so much okay thanks for watching everybody we are out from San Francisco you've watched the cube micron inside 2018 check out Silicon angle comm for all the published research the cube dotnet as well you'll find these videos will keep on calm for all the research thanks for watching everybody we'll see you next time you

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.

>> [Narrator] From San Francisco, it's theCUBE, covering Radio 2018. Brought to you by VMware. (upbeat techno music) >> Welcome back everyone. We're here with theCUBE in San Francisco for exclusive coverage for VMware's Radio 2018. I'm John Furrier, your host. This is the event where everyone comes together in the R&D and the organic engineering organization of VMware to flex their technical muscles, stretch their minds, compete for the papers, and also get to know each other. And the key person behind this is the chief research officer David Tennenhouse. Thanks for joining us today. >> Thank you John. Really glad to be here. >> So you're the chief research officer. You got to look at the company-wide agenda. But this event is more of a special event, organically. Talk about for the folks out there watching what's different about this event that goes outside the scope of kind of the top-down research. >> Yeah this is really, you know, for the developers by the developers. So when you said I'm in charge, I'm definitely not in charge. And you know, we have a program committee. There's a programming committee chair. It's much like the way an academic conference might be organized, where you know, there's kind of a group of academics that sort of watch over the content. In this case, we have many hundreds of folks that submit proposals into radio. They can't all get selected. It's very competitive because in addition, if you get accepted, you get a ticket to radio. You get to attend. So everybody really wants to do that. >> Talk about the organic nature. 'Cause this is one of the things that I've seen that's been part of a world-class organization. Like Amazon has their own process for it called the big idea. They have certain working documents that process to foster any idea across the organization. How important is that as part of Radio? I mean literally it's anyone right? >> Well it's not just Radio. It's important to the whole company. So I think of this as when you're working on innovation, you're gonna have sort of a breadth component. You want everybody doing a little. And some of that's gonna be incremental. One thing I learned in a prior role at a different company is you know if you add up a lot of two percenters, that's how you can double things and keep on Moore's law every year. So you're gonna get some of that. And you're gonna get some really disruptive ideas. So you know, from a top-down point of view, we try to drive some disruptions. Some disruptions show up organically from the troops. And a ton of that breadth stuff shows up. >> I'm honored to be here. It's the 14th year, and some T-shirts commemorating the key milestones from way back in the day. This is the first year press was allowed in. I noticed a handful of folks came in to kind of document this. A lot of the brightest minds in VMware here. Again, great to have us. We're super excited. But share with us. Like, what's happened over the years. Give some examples of where people were coming together, where there's a collision of ideas, and just that combustion that happens. Can you share some stories around key notable, or potentially as Raghu pointed out, there's been some misses too. (laughing) >> Yeah, you're gonna get some of that. I mean you've gotta take risks. Not everything's gonna work. You know and just to speak to misses. What I've learned in the innovation and research space is as much as anything, it's about timing. It's pretty rare that you completely technically miss. Usually engineers have an idea. They'll figure out a way to make it happen. Then the question is, is it the right time? Are the customers ready? Is the market ready to go in that direction? So, that's just to talk to that. >> Timing's everything. >> Timing is a big deal. >> Well there's never a miss too in R&D because if you, like Pat Yelson said, understand when it's gotta be re-casted. Know if it works or not. >> Yeah just understanding. So those are the ones actually you know I feel, what I really hate is if for some reason we have to end a project and we haven't actually gotten to the bottom of it. And so you don't know yes or no. And sometimes that can be the kind of time's run out, right. You've decided well, even if it works, it's too late. But you know, getting back to some of the examples, I'll focus on some more recent ones. We had some really interesting work come together on containers. And there were some folks that, and this is going back like four years ago. Containers aren't a new story, and certainly not for VMware. But around four years ago, there was a proposal at Radio that had to do with hey let's make containers a first class citizen on VMware's platform. Okay, so top level that makes great sense. Let's go do it. Containers are great for developers. The IT folks still want the isolation they get form VMs. Let's put these together really effectively. So that was top level. There was a next level, the idea that said gee at Radio, a couple years before, there'd been this idea of being able to do something called VM fork, or being able to clone a VM. And saying you know... And this came out of our end user computing group, the VDI folks. And if you think about it, if you've got a virtualized PC, you want to be able to clone that so you can start these up really fast. And the container folks said hey, we've got the same problem. Could we actually try to make use of that technology and use that as part of our bigger container push? So you know, those are examples of things that came together at Radio. And there are also examples of things where the market timing may not have quite been there. So we went out with the container work. That was actually post-Radio. It was funded. We incubated it. You've got vSphere Integrated Containers hit the market exactly the right time. >> Timing right there. >> Right, timing right there. But what we learned as we actually started doing trials with customers was that they didn't actually need the instant clone on the containers. What they needed is throughput. They wanted to know that they could do large numbers per second as opposed to you'll get that container really quickly. So as the team went along, they actually shifted away from that fork idea. We'll probably come back to it when the time's right for it. >> Well you have a nice little positioning there. I like the timing. 'Cause by the way, entrepreneurial timing is the same way. You go outside... >> I was a VC. (laughing) >> Okay, so you know okay. Timing's everything. How many times you seen that entrepreneur wicked early on it going... And they keep scratching that itch and finally they get it. The art of the timing. But also the art of knowing when to, what to keep in inventory. Pat mentioned vCloud Air as an interesting example. Recognizing abandonment there. Okay hey, let's just stop, take pause. Let's use what we have. >> Do something else. >> Do something else. >> Gotta do something else. And by the way along the way, in parallel with vCloud Air, we had built up these vCloud partners. And that's phenomenal right. So we have you know, people think in terms of a couple of very large public clouds. But we've got literally thousands of people running public clouds in either specialized markets, or particular countries, that are running on our platform. And you know that whole vCloud Air effort helped push that forward. >> So where were you a VC? Just curious. >> I was actually in a company that fits with sort of my role in research and innovation. I was in a specialized firm, boutique firm, new venture partners, that specialized in spin outs from large companies. This goes to the timing, right. I'd previously been at another large company. You know, and whenever you have a research portfolio, you're gonna have some projects that you started. They were technically successful. That's your first notch. Then you go look and say hey, can I find a business model for it. Some of these are both technically successful. You find a business model, but you had anticipated that the company strategically was gonna zig. The company zagged. Now this is a great opportunity that doesn't quite fit. So you know, we did those as spin outs. >> Well I love the perspective too of you said earlier, David, around not getting to the bottom of it. And that's the most frustrating part. Because you just gotta get some closure you know. Like okay, this thing, we took it to the end, completion, this is not gonna... Good try guys. >> And we know why. >> And you know why. Now let's take it to the next level. Now the market we're living in now I heard with Ray O'Farrell, I was talking with earlier. We talked about the confluence of these big markets coming together. Infrastructure market, which is kinda declining on paper. But cloud is filling the void. Big data's becoming AI, and blockchain over the top. These are four major markets. And at the center of them, intersecting all these nuances, security, data, IoT. >> Governance. >> Governance. So there's some sticky areas that are evolving based upon these moving markets. Opportunity recognition's another one. So this is what you're kind of doing now with the research. Talk about opportunity recognition. >> We definitely do that. And I do want to say on the infrastructure side, you know something to recall is that as people, you know they've got their private clouds. Those are individually getting actually bigger as they consolidate. But now with IoT, you're seeing edge computing pop up. Right, so the private infrastructure doesn't go away, it moves around. It's like a liquid. And you pour it from place to place in some sense. >> Moving computer around. Sound like what Ray O'Farrell was talking about in his keynote, early days of VMware. Again, Compute's the center of this. >> Right, Compute, but you know I'm a networking guy so you know, we've grown that. And I think that in fact, you know more and more as we make progress with software defined network, and network virtualization. And if you think about that, so you know let's look at that. So Compute's definitely at the center of what happens in the data center, in the cloud, right. You're gonna want to be able to string those piece together. So today we've got AirWatch. I think that's strategically really key. Because it gives us a little bit of presence on the edge devices that touch people. That's one of the ways information gets from the physical world to the virtual world is through people. >> It's an edge device. People are things too. >> IoT, right. So we're you know, working hard. And that's one of the projects that we incubated, and researched, and is now become a business at Vmware. It's to get that presence right at the edge of the gateways that bridge between the things that are connected to the physical world, and bringing it into the virtual world. Now if we can put our software defined network between all that, so you got it between the public cloud, the private cloud, the mobile devices in people's hands. >> And on premise, data center. >> Exactly, all of 'em. >> All right, so here's a question for you. This is one of those trick questions. Is the cell phone an edge device or an IoT device? >> Well I think it's in many ways both. And what I think of it is is more of a gateway. If you think about the IoT world, you have the things. >> IoT is a strict definition though in your mind, right. People refer to IoT as more of a sensor thing to a physical device. >> I tend to think of it as it's got some connection to some physical device. It's able to bring information in from the physical world. Okay, so now you look at your cell phone. It can bring information. It's got that microphone. It's got that camera, right. It can bring information in. >> [John] Connect it to a physical person. >> It can put information back out. Yeah, through a physical person. I've been in the space for a long time. Going back to my time at DARPA, we set out to create the IoT world. This wasn't an accident, right. We looked at this and said, okay the main way information gets between these two worlds today is through human beings. The way I used to explain this to the generals is you know, we can't keep putting human beings in the direct line of fire of information technology. So we've gotta get these devices, gotta get all these sensors. It's taken a long time. This is you know again, timing. But if you look at the research world. >> By the way, incredible work you've done by the way from there to here, it's been amazing. >> You know pull this along. But you know so when you look at that cell phone, it's got some of those sensors. It's got actually a whole pile of sensors in the phones today. It's got actuation, the ability to put the information back out. It's also a gateway. Because typically you know, particularly through its Bluetooth functionality, and as we get Bluetooth low power now. So it's also acting as a gateway to connect up other devices around your body, network etc. >> Personal networking, whatever comes on your physical presence. >> So you know, turn that around and it says in the IoT world, we've gotta manage gateways. We've gotta make sure gateways stay secure. Because they're really gonna be the sort of main perimeter, the line of defense. If you think about all these things that are gonna be out there, as an industry, we're gonna collectively try very hard to secure all those things. But let's be realistic. They're gonna be supplied from a wide variety of companies, and they're gonna last longer than people might think. >> How much of those devices are operationally, operation technology is non IP, versus not IP. Internet Protocol. >> Non Internet Protocol. Yeah, yeah. >> Internet Protocol now. >> [David] Non Internet, you had it right. >> Got the VC in the brain there. The VC, IP, I'm like get that IP right. So internet protocol devices, which has some challenges but that's getting fixed, versus OT just sensors proprietary. >> Yeah well either proprietary or let's say, you know it may be an industry standard, but an industrial standard. So today, a very large fraction, particularly you asked about how we focused at Vmware. Well one of our foci is we're about what are our enterprise customers gonna need. So when we think IoT, we're not really thinking that much about the consumer devices. We're thinking about those enterprise devices. So a lot of those will use... >> That's where AirWatch might come in. So employees still have phones though. >> Employees still have phones. So that's why I said, so there's the human interface. We want to be there. And there's the other enterprise interfaces to all these sensors. That could be in a factory. It could be in a smart city, any number of places. So as we pull information in from those, we're gonna find that they come from a lot of different suppliers and they're gonna last a long time. You know, even if you buy a device that's got a three to four year lifetime, probably 10 to 20% of those still gonna be around 10 years later, right. You're smiling because you know that in your home you have some wifi connected devices that are a little older than they probably should be. >> And they have full processing capability threaded processes on it, which could be running malware as we speak. >> So as I said, as an industry, we'll try to secure those really edge things. But the reality is we're gonna have to draw the line at the gateway. >> It's a lot more security work. I totally hear you. I mean the light bulb could have a full thread on there. The surface area is so huge now. >> And there have been attacks on light bulbs. >> Yeah I know. So I gotta ask you a question. 'Cause you bring up this networking edge, which by the way I love anything that's network. 'Cause I think this is the future of work. How is the future of work impacting some of the R&D you're doing. Because you talked about AirWatch them having more mobility. The human impact, society, whether it's mission driven and or just human collaboration going digital. You're gonna need to have policies. You need to have a networked society. This is super relevant. But it brings back that future work. >> It does. And so couple different aspects. You know, one you know, which just relates to a point you raised is if you look at something like our Workspace ONE product, if you've had a chance to do that. It's kind of a win win, because you get one portal. So you know, an employee for an enterprise, they've got one portal. They get access, it doesn't matter whether they're getting to a web app, they're getting to a you know, a DVI supported application. They're getting to something that's on a server, something on a SAS player, right. They get through that portal. So for them it's convenient. I mean for me as a manager, I love this, right. Because whether I'm on my cell phone, I'm on a laptop, doesn't matter, I can get to the same expense app. I can approve things. >> You don't need to carry two phones. My work phone and my... >> And I can do all these approvals really easily, right. So I also don't worry. I don't see the difference between which device I'm on. At the same time that you're delivering that convenience to the user, you're delivering governance because the IT team can be deciding how that portal's populated, how things are connected, right, and how the wiring works. All the authorization, you've got a common identification system and all of that. So that's kind of very specific to you know, let's say near term changing the user interface. In terms of the broader future of work, clearly machine learning is the big story here, right. And I think that what we're gonna see is, particularly again in enterprise, more and more need for data analysts to be able to look at the big data. We're gonna see sort of more and more use of machine-learning technologies. It's gonna you know basically creep in everywhere. And we're getting this at just the right time. So if you want to think about future work in the big national and international scale, what you really sort of stop to look at is say, gee, okay, these machines are gonna do all this work. What about the people? And you know a lot of people therefore get concerned. Gee, the computers are gonna take away all the jobs. Right, you get these sound bytes. >> I think right now we're worried about fake news and real content. (laughing) >> Well let's come back to that one later. But there is a sense of gee, you know, the computers will take on all the jobs. And you know what I think people are not doing carefully is looking at the demographics. Because if you look at basically all the developed economies for practical purposes, we actually have a demographic problem. Our problem is actually not a surplus of workers. It's gonna be a shortage of workers. In fact, actually in the US right now, you're starting to feel this. Now that's at the peak of the economic cycle. So of course you feel it, you know, a bit. >> They need trained workers too. Also people who qualify. >> Right. So I think the thing we really need to look at is how do we do a much better job at matching, you know, sort of workers, both folks coming into the workplace, people with existing skills, to available opportunities. Because actually we're gonna have a shortage of workers. And it's not just sort of the US and Europe. I mean China, Japan. Well Japan for a long time. China, headed to a shortage of workers. I was out in Singapore not too long ago and was surprised to find out not just that they're concerned. But they went and looked at the Southeast Asian countries around them that are their markets. They're looking at a shortage of workers. So you know, if we didn't have something like machine-learning and AI coming along, we'd be sitting there saying, how are we gonna keep our economies growing? >> We need augmentation for sure. >> We need this augmentation. And it's coming at just, you know, you talked about timing. You know, it's coming at just the right time. Now, there definitely are gonna be some tough transitions along the way, right. So we definitely, you know, for example, as autonomous vehicles come along, we've gotta figure out, okay, all those people that are driving vehicles, what are they gonna do going forward? But let's not kid ourselves too, you know. If you've got trucks moving around with high-value cargoes, you're not gonna leave those unattended, right. We're gonna have to figure all this out. So there's gonna be a lot of interesting opportunities. >> What's your take on blockchain? Well first of all, GDPR, real quick. Train wreck, useful? >> I think it's you know, if you backed up and asked me four or five years ago, I'd have said train wreck. And largely because we still don't have the sort of kind of international consensus on what the rules should be. >> But you mentioned governance earlier. That certainly needs to be at the center of the action. >> Right, but you know, if we take a look now, it seems like it's showing up at just the right time, right. You know, in that sense. I think part of what's happened is over the intervening years, a lot of countries outside of Europe, because they realize these regulations would apply to them, they've worked with European regulators to help the regulators understand the technology, you know, help the companies understand. >> That's a good politically correct answer. I'll just say I think it's a shit-show personally. But you know. I mean it's gonna force people... It's like Y2K in money making, but Y2 never happened. It's forcing people to really, I think the value of GDPR is the big companies are gonna get hit hard on some suits. Just the trolling thing bothers me. Just the trolls that come out of the woodwork. But I think the positive that puts the center of the value proposition, making data, not a one off, like backup and recovery. It has to be core to technical operations. >> And making privacy something that's really in that first class category. You know, as I said. >> Great first step, but... There's a big but. >> There is more to be done. >> Hopefully they don't go after us little guys. All right, final question, blockchain. We are super excited about blockchain. You have teams working on this. >> [David] I am super excited about blockchain. >> Talk about your view on blockchain. Why are you excited about it? Obviously we feel it's very efficient, makes inefficiencies efficient across all industries. Your thoughts. >> Okay so again, we look at things through this prism. What are enterprise customers gonna be looking at? What do they want? And you know, so we're not you know... I think you're in the same place. We're not looking at the crypto currencies, right. That's not the thing. And in fact, we're not even looking at cohabiting on the Bitcoin blockchain. Because do you really want to run your business in the same place that a whole bunch of other people are running illegal businesses and the whole thing. >> And by the way, there's some technical issues. (laughing) >> We'll get to that. We're gonna get there. But just even as a starting point. So we pretty quickly looking even you know, three, four years ago said, okay enterprise is not gonna want to go that way. But this idea of a federated ledger, right. So if you can make federated ledgers and we can have reusable technology, that means now, if I want to federate with other companies or other organizations, or you know, or you need companies federating with governments, or governments federating with each other. Anywhere you want to pull together essentially a club for the exchange of data, with a persistent record of what happened, you've now got a common way of doing it, right. Or we can drive towards that. You know there'll be a standardization process to get there. But so it's not to me, federated ledgers means lowering the barrier to federation. And I think that's pretty exciting. Whole bunch of places. You know, supply chain, clearly one. Financial technology, but... >> David, we gotta spend some time, have you come in the studio. I'd love to explore some of these great topics with you. But I gotta ask you one final question. You know, with your history going back to ARPA, D-ARPA days, and looking at really the beginning of the information super highway, IP, connecting some universities together, to today, the waves that have gone through. We've talked about standards. The OSI stack, you had all these grandiose standard plans. Not all of them have happened exactly as planned. But defacto standards play a really important role. It galvanizes community, gives people guiding principles, a north star, whatever metaphor you want to use. The key is the enabling disrupting technologies, a defacto standard. What's happening now in your mind that you see out there that's starting to emerge as defacto? 'Cause certainly there's a lot of standard things going, open sources for tier one citizen growing, rapidly, which is greatness. Cloud is booming, unlimited resources, Compute, fingertip compute... All this is good. >> Yeah. >> All these new standards, I got Kubernetes, I got this going on, what's emerging? >> Well again, they're defacto, right. Kubernetes is an interesting example of basically open source meets defacto. And that's pretty exciting right. I mean, we're excited about it. I think people are often surprised we're a fan of open source. And I guess really, I just like to sort of back up a notch. Because you know what you touched on is defacto standards, whether it's open source or not, have suddenly become a lot easier. When I say suddenly, over like a 10 year period. And I think what's going on there is this is part of the change to software. So you know, if you're talking about hardware, and you got screws, you know, and you got threads, these physical things have to match, and they have to match exactly, right. Say when you travel overseas, you need to carry converters, physical converters to convert from one thing to another. So if you want to interoperate, if you and I want to have stuff that interoperates, we needed to build like either, do the same thing, or have a physical adapter. There was a cost to not having a standard. If you think about in the software world, we can build software converters, right. So if I've got you know, say we've got two, or three, or four, or even 50 defacto standards in the software world. You know, blockchain. So there's 50 new things. Everybody launches their own. Pretty quickly, the market will drive that down to a small number. And then you can put software converters in place. So we no longer actually have to get to one. >> [John] That's the software economic model. >> It's a big change. >> And that is huge. So by the way, we had Dirk Hohndel on at CubeCon. Love his open source mission, just a shout out to you guys, doing a great job. You guys at VMware certainly that we know, love you over on the East Coast. Final prediction. Final question. Give us a prediction. >> Give you a prediction. >> 2018, second half of the year, what's gonna happen? What's gonna be a notable thing that you see out on the horizon that might happen in the marketplace that might be notable for people to stand up and pay attention to? >> I think we're gonna see some significant developments in the blockchain space. And it's gonna be in the category of people starting to announce real deployments. And you know, if you're sort of looking at that time frame, you know you've had a lot of different enterprises try things. We've had people kind of dabble at things. I think you're gonna start seeing some people really move significantly in that space. >> And do you think like, just to follow up on that, do you think like in the database world now, where by the way, it's okay to have a zillion databases now. 'Cause you talk about databases. >> But it consolidated down to a few players. >> You get some extraction layers. It's okay to have a few variety of blockchains. I mean, there's no one blockchain. >> Correct, so that's where I think as I said, you're gonna see actually a bunch of these deployments. They'll be using different technologies. And then the fun really starts right. As people consolidate, especially with open source, they swap ideas. We boil it down to what's the best of the best. We've got you know, stuff we're doing certainly to knock the throughput down, sorry throughput up, latency down. (John laughing) And you know, we think we've got a very scalable approach. And most important, you know something that's really... I don't know if you talked to people about our sustainability. You know, it's a key value for VMware. >> [John] Yeah, lot of great standards there, yeah. >> So you can imagine we looked at blockchain. We looked at proof of work. And we said that's proof of energy wasted. We're not going there. >> Gotta make it more efficient. >> I think you're gonna see more and more folks focusing on things like Byzantine fault tolerant. Ours is scalable. You know SBFT. >> Yeah performance is key. And the energy's a huge problem. >> But performance and at acceptable energy. You can't you know, just waste. It's immoral to just waste energy. And it really goes against what a lot of the whole IT industry's built up. You know, I think we've, over the decades, we've done a lot of things for the good of society. And we gotta stay the mission. >> I think as the more, I won't say mature, but big world-class organizations join in, I think that'll straighten itself out. And certainly, as any evolution would see, the web. I remember dial-up and AOL. It can't go as fast as this minicomputer. Well you don't get it, it's the web okay. David, thanks so much for coming on, appreciate it. Great conversation here at Radio 2018. I'm John Furrier, Cube coverage of VMware's annual 14th year conference, at Radio 2018. Thanks for watching. (upbeat techno music)

>> Announcer: Live from Nassau in the Bahamas, it's theCUBE, covering Polygon 18, brought to you by Polyman. >> Welcome back everyone, we're here live with theCUBE's exclusive coverage of Polycon '18. We're in the Bahamas, I'm John Furrier with Dave Vellante, co-founders and co-hosts of theCUBE. We're here with special guest Halsey Minor, entrepreneur, serious serial entrepreneur here on theCUBE. Halsey, great to have you. You're the founder and CEO of VideoCoin, a successful ICO. You had an event last night, kind of an investor thank you event out in the Bahamas Country Club, there, you're here. Man, you're a pro, you're back in the game with this crypto. This is the wave, I mean, I want to get your perspective 'cause you see waves. You've seen CNET, you started that from scratch before online news was anything, you were the pioneer in that. First investor, first operator in salesforce.com, a variety of other successful entrepreneurial adventures. You've got a nose for the waves. So just put it in perspective, what is this wave? >> Yeah, so I actually have an interesting story because I've actually started around 2012, and I launched my first business in 2013. So, the first problem that I saw was, how do you get your money from your bank account and buy Bitcoin? Still a problem, hasn't been fixed, right? So I tried to fix that. Oh well, I did to a certain extent, I did fix the problem. So what I did was created effectively a coin-based converter, and I started out and was going to make it very easy for you to take your bank account, connect it up, seemed logical, and then buy, you know, the currency. The company was called Bit Reserve at the time. So, no bank would touch anybody named Bit in their name. And it was even worse than that, all of us who put our company name into our bank account, we had our bank accounts basically shut down, right? So, I started getting an idea how difficult this was going to be, you know, Coinbase getting a Silicon Valley bank account early on to become a conduit, was very fortuitous. It ultimately took two and a half years and buying a big chunk of New Jersey Bank before we were able to allow you to connect your US bank and your European bank into Uphold to buy currency. So it's really Uphold, Coinbase, maybe like Gitbit, very, very few who've been able to crack that problem. We literally had to buy part of a bank to do it. So that's where I started. So I really looked at it very much as money, as a new monetary system. And I still see unlimited opportunities in that area. It wasn't until really a couple years later that I saw the block chain as the new architecture for the computer, and what I mean by that, is what Bitcoin proved was that if you gave people software and they ran it on their computer and they got paid in some funny kind of digital money, they would convert that money back into fee hock, you know, dollars, and they go buy more computers. And nobody asks anybody to be a Bitcoin miner, they just come and showed up the more, the bigger it got, the bigger the opportunity. And what's most interesting is when you make money or lose money, depends on your cost of power. So for most of these Bitcoin miners, they're near hydroelectric dams. So what I realized, and VideoCoin is in the area of video. It's a direct competitor with Amazon web services, everything they do in video. So there's, it's called encoding which is compress it, there's storage and there's streaming, three basic pieces. So what I realized was, two things: first of all, 20% of servers and data centers are not used at all. They're called zombies, right? So all of these people, the Airbnb, Uber model, they can all of a sudden start earning on assets that are doing nothing. But even if you look out into the future, if video mining, which is what we call it, ends up being like bitcoin mining, then what happens is that the whole thing works on the cost of power. It's not good for Amazon, if they have to be competitive solely based on the cost of power. >> Dave, so he's got an ICO going on, we looked Filecoin, right? So Filecoin was storage and that's infrastructure. You go to VideoCoin, we're streaming right now, we've got video. This is kind of like an interesting digital media infrastructure ... >> Well ... >> What's your take compared to Filecoin? >> What's interesting to me is that I'd love to get Halsey's input on, because you've got the full spectrum here. You started in publishing and now-- >> With five TV shows. >> Dave: Okay. >> Yeah, CNET had five TV shows. >> So right, and so very digital from the beginning and relatively ripe for disruption and then now into banking, which really hasn't been disrupted, but we all think it's coming. So that's an interesting spectrum. It's not Negroponte, I don't think, bits versus atoms, because you've seen, you know tax season get disrupted. That's atoms. So what are the factors that make an industry ripe for disruption? >> Well, I mean the obvious thing is really disruptive technologies, right? And so for the Internet, for me, it was, I started the company in '93 to be on commercial online services like AOL and I saw, I guess, the first browser in '93 and, actually at Sun, and it made me believe the Internet was going to be this incredible thing. And it was really seeing information coming in, and, you know, the Internet wasn't that big back then but I watched a gif of a storm, you know, from one of the weather centers, and so I realized that this information thing was incredibly interesting. And so what all of us did, the way I thought about it and seen it, is we're cracking open databases and we're just letting people have the information. And it was silly things like the ability for me to live in San Francisco but know what the weather was in New York and pack appropriately. This was the magic, I mean, we take all of this for granted. This was magic, right, at the time. You had to go out and buy a USA Today-- >> Check the stock price. >> Yeah, exactly. >> Call your friends in New York. >> Yeah, that was magic. So at a very high level, it was just access to information. At a very high level, what this is is combining information and money into a packet. Right? So now what we can do is, I can gather information from servers about what they're really doing and I can also be paying them at the same time. So you know, it would have actually solved a lot of problems around the Internet, because on the Internet getting paid was hard. And there were so many times we'd go into a meeting and we'd agree on the partnership but we didn't know who was paying who. You know? (laughing) Am I paying you for traffic or are you paying me for content or you know, how is that going? So this kind of comes with a built-in payment system, which I think is what makes it so incredible as a system. >> So we're-- >> And more stable, I am inferring, long-term anyway. Because that whole system that you just described on the Internet all blew up when the funding dried up. >> It blew up and I think, you know, I think there are certainly a lot of risks. The number one thing I would tell everybody in this area is, you know, be very cautious about what in you invest in. There were a lot of companies that, uh-- so my whole description was sort of the Internet bubble was that people say that, well, you know, nine trillion dollars was lost in investing. >> With everything that happened though. >> And when I-- >> The plus.com happened, everything happened. >> And what I said to the people is that it would be great if people had just invested in the survivors, but who knew what they were? The only reason the United States emerged, with, you know, with Salesforce and Ebay and Amazon, etc., the only reason that we emerged dominating the world was 'cause we invested in them all. Right? And so-- >> Even all those things that were called silly ideas actually happened. >> And they ended up happening. It was all a matter of timing, yeah. So you know, what's happening now is very much the same thing. You know, a lot of people are going to invest in a lot of bad ideas, right? But this is all necessary for the good ideas to get funding and for something big to come out of this. >> So I want to get your take on with the VideoCoin and in comparison, you mentioned Amazon, right? So our observation, obviously we're recording all these shows, Amazon web service, among others, the big guys are sucking all the oxygen out of the room. Look at the big whales, Google, Facebook, Amazon, I mean, we can't even run any ads on our site. We actually prefer to just push the content all over the world because it's hard to build a destination site. I mean, people going out of business in the media business. Video, your choices are Ustream now owned by IBM, Twitch TV became Amazon which was Ustream before that. Build your own custom player, set up a CDN, which is actually hard and expensive. Okay, so do I do Facebook live, again controlled by Facebook? So there's an opportunity that you're pursuing. Did you have that in mind? I mean, we see it every day and we know this, but luckily we have a good deal with Ustream, but the point is that is going to be up too. What's the alternative producers, content producers who have streaming, whether it's a pro set like this or someone who's going to have unlimited access to video streaming? >> So the real issues are cost and innovation, okay? And so Hanno Basse, who's the CTO of 20th Century Fox and one of our advisors, right? And all these media companies have the same problem. Nobody is watching broadcast anymore that'll cost them nothing and everybody's now streaming in, which is one-to-one and has a cost associated with it. So that's why, and even worse, videos going to 4k, 8k, VR, data that's going up like this-- >> Data isn't growing as fast either. >> So all these companies are confronted with all these costs and they can't monetize them. Google can monetize it, Amazon can monetize it. >> Tel cos ... >> Netflix, yeah. >> Ouch. >> But they can't monetize it, so it's all cost effectively and no revenue. So the one thing that we offered to VideoCoin by using all this research is we cut the cost 60 to 80%, so that's huge. The other thing is, in the early days, everybody bought Salesforce because it was cheaper. It was 1/10th of the cost. And I used to say to people, in the long run, it's going to be way more innovation, right? Because they're constantly, every quarter, rolling out a new version, right? And they're going to have the ability to connect, an API effectively, and the ability to connect, and the whole ecosystem can arise around that. And that's why their conference has 140,000 people, Dreamforce, because there's a whole ecosystem. >> It's sticky as hell too. >> That's right. >> Hard to get out. >> That's right. So while we are 60 to 80% lower cost, we're also effectively open source at the same time. So the ability to have a community arise and develop software. And so right now, you've seen this huge consolidation because it's actually kind of hard to build new kinds of apps on top of Amazon web services, right? But if you have this open system, and you have all these people are contributing code to it, all of a sudden, there are apps, video apps, that they'll be literally a whole new-- >> So you're going to have an open source contribution piece to your ... ? >> Yeah, I mean basically, everything we build is open source, right, so you know, all the way through to the network. So it creates a palate for people to start innovating in video. Because really what's happening is a lot of innovation is getting hurt by the fact these big guys totally dominate it, right? They don't want to see any innovation outside of the funds they bring you, right? >> Right, so you've heard my rap on this. I'd love to get Halsey's thoughts. So the big guys, you're right, have won. It's like centralization and victory. People here are saying, "No, we want to take it back." The premise that I hear a lot is there's been no innovation in protocols in, you know ... Google built gmail on SMPT, HTTP, DNS, it's all government-funded or academia. >> Yeah. >> And it's just a lack of innovation. >> That's right. >> And now, this is why I counter Warren Buffet and Charlie Monger, is no, we're building out a new set of infrastructure. >> That's right. >> Okay, so where do you guys fit into that? What are your thoughts, first of all, on that premise? And where do you guys fit? >> Yeah, I mean, look, you've got these huge companies that are totally dominant and even though they are, in fact, you know, innovative Silicon Valley companies by label, okay, they have all the same issues-- like I say to people, nobody today believes that anybody can put Amazon web services at risk. If I went to somebody and said, "You know Amazon web services which are worth 3/4 "of the value of the company, or 5/6, "depending on who you talk to, "there's going to be something after that." It would literally be a new concept because everybody's convinced this is Amazon's-- >> John: The winner. >> Yeah, this is their big, this is the way they make all their money-- >> Alright it's over-- >> Right, and if you say to somebody there is going to be a next thing, they would look at you like, you know, like you're foolish. But the reality is when you start changing some basic, underlying infrastructure in the Internet and you start doing things, decentralization, this is the word we're going to be using, you know, we're going to see it in solar power. And solar power is, you know, on a cost to benefit like this so, you know, it isn't going to be long before we're going to have power in our house legitimately, not like, you know, some science-fiction thing, we'll be legitimately powering most of our needs with solar that we connect because the cost is coming down so much. So we're going to see all of this decentralization happening. And in the world of computing, decentralization means that this is going to be the most efficient that computing can ever be. Because just compare using the Uber and Airbnb model of saying anything that's excess, let's turn into value. And I've heard that for every Uber driver, 15 cars go away, right? So the decentralization is going to have a profound effect on the economy and it's going to have a profound effect on these big guys. >> Oh, even those guys are going to get disrupted. >> They're going to get disrupted. And they're 20 years old, it's time for them to get disrupted, I mean, you know ... >> E-commerce is a 20, 30-year-old stack, some say 20, 20-year-old stack on e-commerce, all these things are ready, even what we would consider modern, you know, the miracle of saying oh the weather in New York. I mean that magic is here now in a new way. So I got to ask you the question-- >> Taken for granted. >> I got to ask you a question because you brought up that point. In your history of your career as an entrepreneur because you're doing stuff that's always new and cool, and probably before anyone else sees it, can you talk about some of the ideas that you've seen, not necessarily your ideas, as well others, where the investor said, "That's the dumbest idea "I ever heard"? What billion dollar opportunities have you seen emerge that investors have said, "That's the dumbest idea "I've ever heard"? >> Well, actually, the one that is Salesforce. No VC would put money in. It was really kind of backed by Larry Ellison and me early on. And what's so-- >> John: Google was a dumb idea. We want portals, not search. >> Yeah, so the bet that nobody would take in 2000 was that companies would take their sales information and they would put it in the cloud. Nobody would believe that. Not anyone. And so I used to joke, I used to say the only way it's going to happen is if the sales guy's been waiting two years to get his sales management system in place actually runs over the head of security in the parking lot. That's what it's going to take because it's outsourcing and, you know, the security guys say, "Oh, no, no, no, "we're going to lose all of our data", right? It didn't matter that Salesforce had way more security guys, you know, than these guys had and better, you know, working internally. Nobody believed in it. Literally nobody believed in it. >> This is your point about the decentralization, no one's going to believe, "Wait a minute, "that could never happen." So, in a way, the investor thesis should be, "I want to invest in the dumbest ideas," because that might be the best idea. >> It is. I mean the big, obvious ones that attract billions and billions of dollars, I mean, how many of those end up actually not turning into anything? Right? A lot of them, right? So CDAT was profitable on nine million dollars. I believe that Yahoo was profitable on three million dollars. I think Google was somewhere around 12 to 15 million dollars, right? So there are a lot of these business-- Amazon's obviously the outlier. >> John: It's still not profitable. >> Yeah, it's the outlier. But you know, a lot of these businesses were started by people who used a relatively small amount of money and were very creative. You know, you're going to hear this over and over again. Microsoft never needed any money. They accepted five million dollars from-- >> John: (mumbles) >> Yeah, so this happens a lot. And in fact, I think it's very dangerous when in year five, you're losing three hundred million dollars, right? I mean, five hundred, or whatever it is. There are a lot of things that can go wrong. >> What's the role of community? Because we heard the guy from Locktower Capital say something I thought was really profound, "I don't need VC because, if you're a startup, "you don't have to waste your energy on board meetings "and other things, you can build your business "and use the community as your benchmark." So this plays to your whole picking up the slack kind of thing in efficiency. So entrepreneurs can be more efficient in these communities. This is where the cryptocurrency Blockchain is thriving. What's your thoughts to that and how do you see that community interaction progressing? >> In my career, there's been a sea change in sort of the culture of technology and really everything, right? You know, when I started out, everything was very hierarchical. You know, it's like how far up the chain you got that measured how successful you were. Now it's how big is your network, right? And you know, I was talking to somebody the other day who said VCs are going in and they're measuring these companies' success by how many Instagram and Twitter accounts they have and there's massive fraud going on because people are buying these accounts to pump up their numbers, right? So people are starting to value by the breadth of your network. >> John: Reputable network. >> Reputable, yeah. >> John: Not fake network. >> Yeah, but what I heard is there's actually a Twitter application which I haven't seen that'll go in and tell how many of 'em are real and how many of 'em are not now. So really the community becomes almost the measuring stick for your value. You know, before I'd seen it, I had users. Today, everybody has community members. And so, it becomes sort of, kind of like everything I guess. >> And our media model is all community-based which is, we just naturally go there because that's where the data is. >> That's right. >> That's where the feedback is. >> That's right. >> I mean, I can't get feedback from Facebook and Google, they own the data, right? There's no letters to the editor on Facebook. There's only hate comments. >> But you know before Microsoft and all these came, you know, IBM dominated the world. Nobody ever thought they would go away. AT&T dominated the world and nobody ever thought that they would go away, you know. >> Alright, personal question for you, I got to wrap because I know you got to go. Appreciate your time, by the way. Great story, we could go on for another hour. Personal note, what is the most compelling thing that's moved you, as an entrepreneur, in the crypto market? Like, something that, it could be an anecdote, it could be a situation. When you look at this opportunity, as the world's going to eventually be re-instrumented with data, with new open source and community, what's something that's surprised you or moves you as an entrepreneur saying, "This is freakin' awesome"? >> So this hasn't been done yet but it will be done. So this is what actually motivated me to start Uphold was the ability to turn your phone into your bank and to be able to exchange money and primarily really solving the ability for the poor to be able to move money around without having 10 to 20 to 30% of it taken away. Everybody's talked about this, remittance, and so far, nobody has actually solved that problem. That problem is going to get solved. I mean it's inevitable that the phone becomes the bank. There are so many regulations that are designed to stop that and it's extraordinary. Once you get into it and you see all the ways that have been set up-- >> Byzantine system. >> this problem should have been solved long ago, right? And every phone should be a bank. I mean, it can be connected to a bank, but every phone should have my money in it. I should be able to send it to you instantaneously. >> It shouldn't be like getting into Fort Knox. >> Yeah. I mean, computers, banks have computers, they could make this happen today. They just don't want to. So I think the most profound thing for me is the problem is still not solved, that the problem I set out to solve, which is really creating a more equitable financial system. And we live in a country where the banks make about 37 billion dollars a year in bounced check fees. Think about that. Thirty-seven billion dollars in bounced check fees. So if you just take that out, you just take out, 'cause it all affects people in the lower socioeconomic scale, you create a revolution. Just getting rid of the bank fees that you'll pay for bouncing checks. >> Well, I mean the narratives, like the narrative of taking down gatekeepers or central authorities, is the premise of this ecosystem and you could take that example and apply it to thousands of use cases. >> And banks are rapacious, flat out. American banks are the most rapacious 'cause no other country would allow 37 billion dollars to be taken away in bounced check fees. >> Halsey, congratulations on your success again and great to see you on theCUBE. You're now a Cube alumni, so ... >> Congratulations. >> We hope you'll come back again. >> Yeah, thank you guys. >> We're going to get you in our telegram group, now you'll be 42 members, we just turned on last night. (everyone laughs) We appreciate it and congratulations. >> Thank you very much. >> Thanks for your insight and experience and commentary. Halsey Minor, experienced entrepreneur, pro, here in the trenches, establishing a great new venture. We'll be back with more live coverage after this short break. (electronic music)