>> Live from Washington DC, it's theCUBE covering AWS Public Sector Summit 2018. Brought to you by Amazon Web Services and its ecosystem partners. >> Welcome back to our nation's capital. You're watching theCUBE, the worldwide leader in live tech coverage. I'm Stu Miniman, joined by my co-host Dave Vallante. Happy to welcome to the program Matt Olsen, who is the co-founder, president, and chief revenue officer of IronNet cybersecurity, thanks so much for joining us. >> Yeah, great to be here, Stu. Thanks. >> So, obviously, public sector, we've been talking a lot about the cyber, as it were. As a co-founder, always one of the first, give us the why of the company. Why was it founded and a little bit of background. >> Sure, you know, we were founded, I guess out of some frustration. A number of us, including our CEO, Keith Alexander, who was formerly the director of NSA, we came out of the government. And the frustration was, that what we saw happening to companies, big companies, small companies, and the government was getting hit with cyber attacks, you know, consistently and increasingly sophisticated and disruptive, even destructive cyber attacks. So we left the government around the same time, a number of us, and we decided, can we start a company to really take on these threats? What can we do to develop a technology based on the threat landscape that really takes cyber security to the next level? So our mission has always been to protect companies and governments from these types of attacks that are hitting us all the time. >> Yeah, so there's no shortage of security experts inside the government, especially NSA. Actually, I remember Dave and I had talked to, there was a little company called Squirrel that came out of the NSA a little bit later. Oh, what do you know? AWS acquired them last year. So bring us insight a little bit, you know, what's the offering that IronNet has? How do you differentiate yourself in the marketplace? >> Sure, and you're absolutely right. There is a lot of expertise in places like NSA, where I formally worked and a bunch of us formally worked. The offering basically is, network traffic analytics. So we look at the network traffic inside large companies and, right down to the PCAP, so we're looking at the actual network traffic and running analytics. And what that means is not signature-based, but behavioral analytics. Looking for those indicators of malicious activity that we then can alert the SOC operators in these companies that this is something they need to pay attention to right away. Of course the problem always with this area is false positives. You know, how do you make sure that the alerts you're giving to these operators really mean something? So we've done a lot of work to draw down those false positives so that we're giving them alerts that are actionable and meaningful in the context of, you know, a very difficult threat landscape. So that's the basic offering. >> So what's underneath the covers? I mean, what's the secret sauce? Are you using machine intelligence? Share with us. >> Yeah, sure, I think the secret sauce is really a combination of two things. It's analytics algorithms that our data scientists develop. We've got some world-class folks that came out of places like the Defense Research Agency and universities that develop the analytics, the algorithms, but we combine those, that math with real life operators, people who themselves were on the offense at one point, right? They were working to, you know, break into other networks. They were the hackers who understand how the adversary operates like nobody else does. Combining the mathematics, the analytics with real life operators, that I'd say, you know, Dave, is the secret sauce because those are how we develop the analytics and the expert system to produce the alerts and draw down those false positives. >> Yeah, it was interesting. Last week, we were at Cisco Live, talking a lot about networking, and one of the biggest things for networking people is a lot of the network that they own, they don't actually own it anymore. It's in Amazon, it's in, you know, I've got my SAF stuff, public clouds, all that I'm dealing with. So, you know, where do you sit, are you mostly focused on public clouds like AWS or, you know, where in the network? >> So its a great question because there's clearly a movement, right, from on PRAM solutions to cloud solutions. AWS is part of that. So we're partners with AWS. So we've developed our analytics to run in AWS as one of our key cloud providers. So, we, with some of our customers, we're all on PRAM, we're in their data center. These are companies that want us there inside their perimeter, right? But then, with others, we have the ability to have sensors in their network but then do all the analytics, all the backend work in AWS, in the cloud environment. And that makes a lot of sense for many companies, especially when you talk about companies that are a little smaller maybe or, you know, we're not talking about the biggest companies. So they do, a lot of their applications are running in the cloud, so that's been a key transition for us as we've developed our product. >> Matt, what would you say are the biggest threats to organizations that they should be aware of? >> Yeah, you know, the biggest threats are the obvious ones in some ways, but there's no doubt that the nation-states that are carrying out attacks, whether we're talking about China or Iran or North Korea or Russia, are increasingly active and are especially dangerous in a volatile geopolitical landscape like we face today. So we're concerned in working with our customers to make sure that we're taking on the level of threat that we see from nation-states. And that's something, I think, at IronNet we understand particularly well, given that we were operating at that nation-state level when we were all in government. Of course, the most pervasive problem is the criminals. And you see that in all manner of hacks in cyber attacks, that the most common type of attack, including ransomware are occurring at the hands of criminals. >> So rewarding. But, your behavioral analytics can help with that problem. What about, like, the weaponization of social media? I mean, what do you make of that? And, I don't know, is there an answer to that that you can help with? >> You know, the way that social media has been used, you know, for example in the election in 2016, it's obviously a problem that we all are concerned about as citizens. And part of that is, I think there's a combination of the government working together with the private sector, in particular, the social media companies, to come up with better ways to take on that problem to make sure that people who are using those platforms are actually people, and not bots, not Russian trolls. We need to do an education campaign for American citizens, who are coming into this election cycle that were, you know, better prepared for what we saw happen in 2016. I mean, it's a big effort and, you know, I'm not sure, to be honest, that as a country we've totally come to grips with the nature of that problem. >> Yeah, I think you're right. We're just trying to get our heads around it. I interviewed Robert Gates one time and I asked him this question, and I've asked other security practitioners, and I get all kinds of different responses. He said, I want to tell you what he said and then maybe you can respond. I'm paraphrasing, of course, for Dr. Gates. He said we have to be really careful. I was asking him offense or defense, you know. Should we, we probably have some of the best security people in the world, we could go on offense, is that the future of warfare? He said we have to be really careful because we have a lot to lose as well in critical infrastructure. Others have said, no, we should go on the offense to flex our muscles. What do you think the right posture is there? >> You know, I think that's a great point, Dave. There clearly is a balance. I mean, it begins with defense, right. It begins with hardening our defenses, having the right people with the right experience and the right expertise in place to protect our networks because, you know, the best offense really is a good defense and protecting our networks. But we do need to have the capability, and we do have the capability to take offensive action when warranted. One of the challenges, I think, in this space is that we haven't necessarily developed the rules of engagement. You know, under what circumstances should the United States government take action on offense in cyber? You know, we saw this in going after ISIS. You know, going after some of their capability as a terrorist group, targeting people in the United States and taking out some of that capability. That's one way I think that we've clearly done the right thing in going on the offense. Harder to say when you have some of the cyber attacks going after a critical infrastructure. What's the right role for the government in going on the offense? I think, again, the first step is a good defense. And one element of a good defense is working better together. Companies working together, as well as companies working in close coordination and cooperation with the government. >> So it's not so much the technology. Obviously the technology is there, but it's the process around that, the collaboration with, whether it's within agencies or organizations. >> I think that's right. I think there's a lot of good technology. We're, our company, we provide a common defense platform for companies to work together. That's what we do at IronNet. And we're doing that with a number of energy companies right now. But the, I think it's getting that policy in place so that companies understand the technology exists to be faster and better working together. How can we then break down whatever barriers there are to sharing information and having that sort of collaborative approach? And we see that happening more and more across the critical infrastructure, whether we're talking finance or healthcare or energy. >> Matt, what's IronNet's relationship with Amazon? Are you part of the market place? How do you go to market together? >> Yeah, we're a registered partner with Amazon. Amazon is our, one of our cloud providers for our, as I mentioned, for where we run our analytics. I also mentioned this common defense platform. We run the correlations that we do for companies working together. That's all done in AWS, in the cloud. We've found Amazon to be a, really an extraordinary partner as an industry leader and a cloud provider. And so we're very close to, and with Amazon, in both going to market but also in developing our product, so it's been a great partnership for us. >> What do you think of the show? I mean, it's insane, isn't it? >> Yeah, it's amazing, right? Just the parking, finding a parking space was incredible. But once I got in. >> We didn't have to park. >> Yeah, once I got in, it's a fantastic show. >> We did have to register. (laughter) >> Likewise. No, congratulations, it's a great show and Amazon has been terrific for us at IronNet. >> Well, we're glad to cover it and we appreciate you joining us, Matt, for this segment. Be back with more coverage here from the AWS Public Sector show. For Dave Vallante, I'm Stu Miniman and thanks again for watching theCUBE.

>> Announcer: Live from Austin Texas, it's theCUBE, covering South by Southwest 2017, brought to you by Intel. Now, here's John Furrier. >> Welcome back everyone, we are live at the Intel AI Lounge, end of the day, day one at South by Southwest, I'm John Furrier, this is theCUBE, our flagship programming brought to the events and extract a signal from the noise. What a day it is here, it's the packed venue, AI Lounge, with Intel, it's the hottest spot in South by Southwest, of course, where our theme is AI for social good, and our next guest is Bryce Olson with Intel, and your title officially is, global marketing director health and live services, but you are an amazing story, cancer survivor, but a fighter, you took it to technology to stop your cancer, and also, a composer with your friend, called FACTS, Fighting Advanced Cancer Through Song, the stories. Welcome to theCUBE! >> Thank you, it's great to be here, this is awesome, this is amazing environment that we're in today. But yeah, you're right, when you look at data, genomics data, which is looking at your DNA, and running that out and being able to understand what could potentially be fueling disease, that's the biggest of big data. And when I was working at Intel, I was in a non-healthcare oriented group, and then all of a sudden, I got hit with cancer, like very aggressive, advanced cancer. And I went through the whole standard of care, and I went through that one-size-fits-all spin that wheel of treatments and hopefully you get something kind of thing, nothing-- >> General purpose, chemotherapy, whatever, blah blah blah. >> Nothing worked. And I came to the point where I was start to come to terms with the fact that I may not see my daughter get through elementary school. So, cancer's starting to grow again, I go back to work, at this point, I only want to work in healthcare, because, why would I want to do anything else? I want to try to-- >> John: But you have terminal cancer at this point. >> I have terminal cancer at this point, but I'm not sick yet. You know, I went through all the chemo and all that crap, but I'm not sick yet. So, I asked to get into Intel's healthcare group, because I want to try to help healthcare providers make this digital transformation. They let me in, and what I found out kind of blew my mind. I learned about this new space of genomics and precision medicine. >> Well, it turns out, hold on for a second, you were telling me the story before, but you skipped a step, it turns out Intel has a lot of work going on, so you come into Intel, you're like, they open up the kimono-- >> Open up the kimono, and I learn about this new era called, just basically genomics, so what is genomics? Genomics, essentially, is a way to look at disease differently. Why can't we go in and find out what's fueling disease deep in the DNA? Because every disease is diagnosable by DNA, we just have never had the technology, and the science, combining together to get to that answer before. Now we do. So I found out that Intel is working with all these genomic sequencing companies to increase the throughput so you can actually take something that costs $2 billion dollars back in 2003, and took 10 years to do, get it down to $1,000 and do it in a day, right? So now, it democratizes sequencing, so we can look at what's fueling disease and get the data. Then I learned about Intel working with all these major bioinformatics open stores and commercial providers, the Broad Institute at MIT, Harvard, largest genomic sequencing place on the planet, about how they take that data and then analyze it, get to what is really fueling disease. And then I learn about the cool things we're doing with customers, which I could talk about, like actual hospitals. >> Well, let's hold on for a second on that, your shirt says Sequence Me, but this is really key for the audience out there listening and watching, is that, literally 10 years ago the costs were astronomical, no one could afford it. Big grants, philanthropy-funded R&D centers, now, literally, you had your genome sequenced for thousands of dollars. >> Well, so, and this is what happened, right? I learned about all this stuff that Intel's up to, and I get kind of upset. I get kind of pissed off, right? Because nobody's giving this to me. Nobody's sequencing my cancer, right? So I go back to the cancer center that I was working with, this is January 2015, turns out they were getting ready, they were perfecting their lab diagnostic test on this, it was like a perfect storm, they were ready, I wanted it, they gave it to me, turns out my cancer grows along this particular mutated pathway that we had no idea. >> So the data was, so in your DNA sequence step one, step two is you go in massive compute power, which is available, and you go look at it, and it turns out there's a nuance to your cancer that's identifiable! >> Yeah, a needle in that haystack, right? The signal in the noise, if you will, right? So there's a specific molecular abnormality, and in my case, there was a pathway that was out of control, and the reason why I say it was out of control is, the pathway was mutated, but then there's this tumor suppressor gene that's supposed to stop cancer, he's gone! So it's like a freeway of traffic-- >> So he's checked out, and all of a sudden, this is going wild, but this is cancer for everyone has their own version of this. >> Yes they do. >> So this is now a new opportunity. >> Yes! Now we understand what's fueling my unique cancer. We took data, we took technology and science, and we got to the point where we understand what's fueling my cancer. With that data, I find a clinical trial testing a new inhibitor of that pathway. >> So I just got to stop and just pause, because it's very emotional, and first of all, man, yours is an inspiration to me and everyone watching. I'm looking at some sign this year at the Intel AI booth, and it says, "Your amazing starts with Intel," this is truly an amazing story. >> Yeah, thank you. >> It's really beyond amazing, it's life saving! >> And that's what happened to me. >> This is now at the beginning, so take me through, in your mind, where is the progress bar on this, in the AI evolution, or when I say AI, I mean like machine learning, compute, end-to-end technology innovation. It's available, obviously, when is it going to be mainstream? >> Yeah, so, we're at a point right now where we can go in, if you have advanced cancer, we're at a point now where we can sequence that person's cancer and find out what's driving it, we can do that. But where it's going to get problematic is, look at my case. The mutated pathway hypersegmented by cancer, right, so prostate cancer, a common cancer, now became a rare cancer, because we hypersegmented it by DNA, and I went after a treatment that was targeted, so when my cancer starts to grow again, now I'm a rare cancer. So how are going to find people that are just like me out there in the world? >> So your point about rare being, there's no comparable data to look at benchmarking, so that's the challenge. >> Yeah, no given hospital will ever have enough data in this new molecular genomics-guided medicine world to solve my problem, because the doctors are going to want to look, and they're going to say, "Who out there looks just like Bryce "from a DNA perspective, uniquely? "What treatments were given to people like that, "and what were the outcomes?" The only way we're going to solve that is as all these centers and hospitals start amassing data, it has to work together, it has to collaborate in a way that preserves patient privacy, and also protects individual IP. >> Okay, so Bryce, let me ask you a question, if you could put a bumper sticker or a soundbite around what AI means to this evolution innovation around fighting cancer and using data and technology, what is the impact of AI to this? >> So, where I'm kind of going with this analogy is that without artificial intelligence to sift through my data, and all the other millions of potential cancer patients to start getting DNA data, humans can't do it, it's impossible, humans will not have the mental ability to sift through reams and reams of DNA data that exists for every patient out there to look at treatments and outcomes and synthesize it, we can't do it. The only way someone like me will survive into the long term will be through artificial intelligence. Without it, I will extend my life, but I won't turn cancer into a manageable disease without AI. >> So the AI will extend your life. >> Because AI is going to solve the problems that humans can't. When you have the biggest of big data-- >> Love that soundbite, love that, say that again! AI solves the problems that-- >> AI is going to solve the problems that humans can't, they simply, humans don't have the capability to look at the entire genome, and all this other genomic, molecular, proteomic, all this other data, we can't make sense of it! >> Alright, so let me throw something out at you, 'cause I agree 100%, but also, there's a humanization factor, 'cause now algorithms are also biased by humans, so what's your thoughts, given your experience, the role of the human race, actual human beings, that have a pulse, not robots or algorithms? >> Yeah, so let me give you a real practical example. So, the way that we fought my cancer was through a targeted therapy. Molecular abnormality, targeted drug. The other way that people are fighting cancer is through immunotherapy. Wake up the immune system to fight it. Guess what? Right now, there are 800 combination therapies going on with immunotherapy to try to stop people's cancer. How the heck are we going to know what is the right combination for each person out there? Unless we have like an algorithm marketplace where people are creating these, and taking in predictive biomarkers, prognostic biomarkers, looking at all the data, and then pushing a button to help an oncologist decide which of the 800 combos to use, we'll never get there. So-- >> That's awesome. So let me ask you a question, so for people watching that are younger, like my daughter, she's 16, my other daughter's a premed, she's a sophomore in college, they're like, school's like old, like, school's like linear, they get classes, but this younger generation are hungry for data, they're hungry, they want to, they're young, they're what people do, they disrupt, they're bomb throwers, they want to create value, and so their incentive to go after cancer, and the means are out there, cancer cells, we all have relatives who have died of cancer, it's a sucky situation. There's a motivated force out there of scientists, and young people. How do they get involved? How would you look at, based on your experience, and your experience, obviously, you got these songs here, but on a more practical level, what discovery, what navigation can someone take in their life to just get involved, not a catalog, not the courseware. >> I think, so there's a number of different things that can happen, if you look at the precision medicine landscape, and you start with a patient, patients don't understand this. "Genomic what? "Sequencing what?" They don't understand that there's a new way to fight cancer, so guess what's going to become a 20% per year growth rate job in the next 10 to 20 years? Genomics counselors. You don't have to be a doctor, but you have to be able to understand enough about biology-- >> And math. >> To be able to offload doctors, and have a discussion with patients to say, "Let me explain something to you. "There's a way to understand your disease, it's in DNA, "this is what it means," and then help them guide them into new clinical trials and other therapy that's got it by that, huge growth opportunity for kids. >> But also, it's compounded by the fact we just said earlier, where these become rare cases on paper, are also need to be aggregated into a database of some sort so you can understand the data, so there's also a data science angle here. >> Absolutely, and it's not just cancer, by the way, I mean, little kids in the NICU, pediatric ailments. Have you ever know anybody who's got a kid with a very rare neurodevelopmental disorder, and the parents are on a diagnostic odyssey for 10 years, they can't figure out what it is? So they go from specialist to specialist, specialist, $100,000 dollars later, guess what, the answer's in the DNA. >> DNA sequencing, number one. >> DNA sequencing, number one, and then, once you start sequencing that, you got to make sense of all this data, so there's going to be tons of jobs, not only in biology, but in analytics, to take all this data and start finding-- >> Alright, we got a few minutes left, I want to get a plugin for your little album here, it's called FACTS, Fighting Against Cancer Through Song. >> So here's the story on that. So, when you go through something that could be terminal, it's really nice when you can have something productive to channel that energy. So for me, to be able to channel feelings of sadness and frustration, I started writing songs. Music was therapeutic for me. I took that, started collaborating with a bunch of musicians throughout Portland, including cancer survivors, and we said, why don't we use music as a way to reach people about a new message of how to fight cancer? So we created that, I have an organization that is raising awareness for a new way to fight cancer, and raising funds, to bring sequencing to more people. >> So the URL is factsmovement.com, so factsmovements.com, check it out. Okay, now, I'm so impressed with you, one, you are on a terminal track, you go back to work. >> But I don't look like I'm terminal! >> You look great, you look great. Now, you're at Intel, Intel's got technology, you harness it, now, you're on a mission now, your passion, it's obvious, the songs, now, what's going on in Intel, 'cause now you're out doing the Intel thing, gives us the Intel update. >> I can talk to you about this precision medicine, it's personalizing diagnostic and treatment plan, which I've already done, I could talk to you about other things that we're doing to help hospitals transform. Predictive clinical analytics, let's look at something like rapid response teamed events. Have you ever been in the hospital and heard the alarms go off? That's usually somebody having a heart attack unexpected. Data is out there, if you look at all the data about people that have had rapid response teams events, we can create predictive signals to actually predict that an hour before it would happen! So predictive clinical analytics, and enabling hospitals to look at populations as a whole to treat them better in this new value-based care, is a technology-driven thing, so we're working on that as well. Yeah. >> Well Bryce, thanks for coming on to theCUBE, we appreciate it, really inspirational, great to meet you in person, and I'm looking forward to following up with you when you get back to Portland, we'll get our gang in Palo Alto to get you on the horn Skype in, and keep in touch, really inspirational, but more importantly, this is very relevant, and the technology's now surfacing to change, not only people's lives in the sense of saving them, but other great things. >> And I'm so proud to be able to work for a company that is using its brand and its technology to basically change people's lives, it's amazing. >> Bryce Olson, my hero here at South by Southwest, amazing story, really, really, you can choose to be a victim or you can choose to go after it, so excited to have met you, it's theCUBE, breaking it all down here at South by Southwest at Intel's AI Lounge, it's hopping, music tonight, music tomorrow night, CUBE tomorrow, panels, AI changing the future powered by Intel, #IntelAI, I'm John Furrier, you're watching theCUBE, thanks for watching, we'll see you tomorrow.

(bright music) >> Welcome back to AWS re:Invent. You're watching theCUBE. And we're here with Steve Mullaney, who is the president and CEO of Aviatrix. Steve, I got to tell ya, great to see you man. >> We started the whole pandemic, last show we did was with you guys. >> Steve: Don't say we started, we didn't start it. (steve chuckles) >> Right, we kicked it off (all cross talking) >> It's going to be great. >> Our virtual coverage, that hybrid coverage that we did, how ironic? >> Steve: Yeah, was as the world was shutting down. >> So, great to see you face to face. >> Steve: Great to see you too. >> Wow, so you're two years in? >> Steve: Two and a half years yeah. >> Started, the company was standing start $2 billion valuation, raised a bunch of dough. >> Steve: Yeah. >> That's good, you got to feel good about that. >> We were 38 people, two and a half years ago, we're now 400. We had a couple million in ARR, we're now going to be over a 100 million next year, next calendar year, so significant growth. We just raised $200 million, three months ago at a $2 billion valuation. Now have 550 customers, 54 of them are fortune 500, when I started two and a half years ago, we didn't have any fortune 500s, we had probably about a 100 customers. So, massive growth, big growth (indistinct). >> Awesome, I got to ask you, I love to ask CEO's, entrepreneurs, how did you know when to scale? >> You just know it, when you see it. (indistinct) Yeah, there's no formula, you just know it and what you look for is that point where you say, okay, we've now proven the model and until you do that you minimize things and we actually just went through this. We had 12 sales teams, four months ago, we now have 50. 50, five zero and it's that step function as a company, you don't want to linearly grow 'cause you want to hold until you say, it's happening. And then once you say it's happening, okay, the dogs are eating the dog food, this is good then you flip the other way, and then you say, let's grow as fast as we possibly can and that's kind of the mode we're in right now. >> Okay, You've... >> You just know it when you see it. >> Other piece of that is how fast do you scale? And now you're sort of doing that step function as your going. >> Steve: We are going as fast as we possibly can. >> Wow, that's awesome, congratulations and I know you've got to long way to go. So okay, let's talk about the big trends that you're seeing that Aviatrix has taken advantage of, maybe explain a little bit about what you guys do. >> Yeah. So we are, what I like to call Multi- Cloud Native Networking and Network Security. So, if you think of... >> David: What is multicloud native? You got to explain that. >> I got to to explain that. Here's what's happened, it's happening and what I mean by it's happening is, enterprises at two and a half years ago, this is why I joined Aviatrix, all decided for the first time, we mean it now, we are going into Cloud 'cause before that they were just mouthing it. And they said, "We're going into the Cloud." And oh by the way, I knew two and a half years ago of course it was going to be multicloud, 'cause enterprises run workloads where they run best. That's what they do, it's sometimes it's AWS, sometimes it's ads or sometimes it's Google, it's of course going to be multicloud. And so from an enterprise perspective, they love the DevOps, they love the simplicity, the automation, the infrastructure is code, the Terraform, that Cloud operational model, because this is a business transformation, moving to Cloud is not a technology transformation it's the business. It's the CEO saying we are digitizing we have an existential threat to the survival of our company, I want to grow a market share, I want to be more competitive, we're doing this, stop laying across the tracks technology people, will run you over, we're doing this. And so when they do that as an enterprise, I'm BNY Mellon, I'm United Airlines, you name it, your favorite enterprise. I need the visibility and control from a networking and network security perspective like I used to have on-prem. Now I'm not going to do it in the horrible complex operational model the Cisco 1994 data center, do not bring that crap into my wonderful Cloud, so that ain't happening but, all I get from the Native constructs, I don't get enough of that visibility and control, it's a little bit of a black box, I don't get that. So where do I get the best of the Cloud from an operational model, but yet with the visibility and control that I need, that I used to have on-prem from networking network security, that's Aviatrix. And that's where people find us and so from a networking and network security, so that's why I call it multicloud Native because what we do is, create a layer basically an abstraction layer above all the different Clouds, we create one architecture for networking and network security with advanced services not basic services that run on AWS, Azure, Google, Oracle, Ali Cloud, Top Secret Clouds, GovClouds, you name it. And now the customer has one architecture, which is what enterprises want, I want one network, I want one network security architecture, not AWS Native, Azure Native, Google Native. >> David: Right. >> We leverage those native constructs, abstract it, and then provide a single common architecture with demand services, irrespective of what Cloud you're on. >> Dave, I've been saying this for a couple of years now, that Cloud Native... >> Does that make sense Dave? >> Absolutely. >> That abstraction layer, right? And I said, "The guys who do this, who figure this out are going to make a lot of dough." >> Yeah. >> Snowflakes obviously doing it. >> Yeah. >> You guys are doing it, it's the future. >> Yeah. >> And it's really an obvious construct when you look back at the world of call it Legacy IT for a moment... >> Steve: Yeah. >> Because did we have different networks to hookup different things in a data center? >> No, one network. >> One network of course. I don't care if the physical stack comes from Dell, HP or IBM. >> Steve: That's right, I want an attraction layer above that, yeah. >> Exactly. >> So the other thing that happens is, everybody and you'll understand this from being at Oracle, everybody wants to forget about the network. Network security, it's down in the bowels, it's like plumbing, electricity, it's just, it has to be there but people want to forget about it and so you see Datadog, you see Snowflake, you see HashiCorp going IPO in early December. Guess what? That next layer underneath that, I call it the horsemen of the multicloud infrastructure is networking and network security, that's going to be Aviatrix. >> Well, you guys make some announcements recently in that space, every company is a security company but you're really deep into it. >> Well, that's the interesting thing about it. So I said multicloud Native Networking and Network Security, it's integrated, so guess where network security is going to be done in the Cloud? In the network. >> David: Network. >> Yeah in the network. >> What a strange concept but guess what on-prem it's not, you deflect traffic to this thing called a firewall. Well, why was that? I was at Synoptics, I was at Cisco 'cause we didn't care about network security, so that's why firewall companies existed. >> Dave: Right. >> It should be integrated into the infrastructure. So now in the Cloud, your security posture is way worse than it was on-prem. You're connected to the internet by default so guess what? You want your network to do network security, so we announced two things in security; one, we're now a security competency partner for AWS, they do not give that out lightly. We were networks competency four years ago, we're now network security competency. One of the few that are both, they don't do that, that took us nine months of working with them to get there. And they only do that for the people that really are delivering value. And then what we just announced what we call, 'ThreatIQ with ThreatGuard.' So again, built into the network because we are the network, we understand the traffic, we're the control plane and the data plane, we see all traffic. We integrate into the network, we subscribe to threat databases, public databases, where we see what are the malicious IPS. If we have any traffic anywhere in your overall, and this is multicloud, not just AWS, every single Cloud, if we see that malicious traffic going some into IP guess what? It's probably BIT Mining, Bitcoin, crypto mining, it's probably some sort of data ex filtration. It could be some tour thing that you're connected to, whatever it is, you should not have traffic going. And so we do two things we alert and we show you where that all is and then with ThreatGuard, we actually will do a firewall rule right at that gateway, at that point that it's going out and immediately gone. >> You'll take the action. >> We'll take the action. >> Okay. >> And so every single customer, Dave and David, that we've shown this new capability to, it lights up like a Christmas tree. >> Yeah al bet. Okay, but now you've made some controversial statements... >> Steve: Which time? >> Okay, so you said Cisco, I think VMware... >> Dave: He's writing them down. >> I know but I can back it up. >> I think you said the risk, Cisco, VMware and Arista, they're not even in the Cloud conversation now. Arista, Jayshree Ullal is a business hero of mine, so I don't want to... >> Steve: Yeah, mine too. >> I don't want to interrogate her, she's awesome. >> Steve: Yeah. >> But what do you mean by that? Because can't Cisco come at this from their networking perspective and security and bring that in? What do you mean by they're not in the Cloud conversation? >> They're not in the conversation. >> David: Okay, defend that. >> And the reason is they were about four years ago. So when you're four years ago, you're moving into the Cloud, what's the first thing you do? I'm going to grab my CSR and I'm going to try to jam it in the Cloud. Guess what? The CSR doesn't even know it's in the Cloud, it's looking for ports, right? And so what happens is the operational model is horrendous, so all the Cloud people, it just is like oil and water, so they go, oh, that was horrendous. So no one's doing that, so what happens in the Cloud is they realize the number one thing is the Cloud operational model. I need that simplicity, I have to be a single Terraform provider, infrastructure is code. Where do I put my box with my wires? That's what the on-prem hardware people think. >> David: The selling ports your saying? >> The selling boxes. >> David: Yeah. >> And so they'll say, "Oh, we got us software version of it, it runs as a VM, it has no idea it's in the Cloud." It is not Cloud Native, I call that Cloud naive, they don't understand so then the model doesn't work. And so then they say, "Okay, I'm not going to do that." Then the only other thing they can do, is they look at the Cloud providers themselves and they say, "All right, I'm going to use Native constructs, what do you got?" And what happens basically is the Cloud providers say, "Well, we do everything and anything you'll ever need and networking and network security." And the customers, "Oh my God, it's fantastic." Then they try to use it and what they realize is you get very basic level services, and you get no visibility and control because they're a black box, you don't get to go in. How about troubleshooting, Packet Captures, simple things? How about security controls, performance traffic engineering, performance controls, visibility nothing, right? And so then they go, "Oh shit, I'm an enterprise, I'm not just some DevOps Danny three years ago, who was just spinning up workloads and didn't care about security." No, that was the Cloud three years ago. This is now United, BNY, Nike. This is like elite of elite. So when my VC was here, he said, "It's happening." That's what he meant, it's happening. Meaning enterprises, the dogs are eating the dog food and they need visibility and control, they cannot get it from the Cloud providers. >> It's happening in early days Dave. >> So Steve, we're going to stipulate that you can't jam this stuff into Cloud, but those dinosaurs are real and they're there. Explain how you... >> Steve: Well you called them dinosaurs not me but they're roaming the earth and they're going to run out of food pretty soon. (all laughing) The comet hit the earth. >> Hey, they're going to go down fighting. (all laughing) >> But the dinosaurs didn't all die the day after the comet hit the earth... >> Steve: That's right. >> They took awhile. >> Steve: They took a while. >> So, how are you going to saddle them up? That's the question because you're... >> Steve: It's over there walking dead, I don't need to do anything. >> Is it the captain Kirk to con, let them die. >> Steve: Yeah. >> Because you're in the Cloud, you're multicloud... >> Steve: Yeah. >> That's great, but 80% of my IT still on-prem and I still have Cisco switches. Isn't that just not your market or? >> When IBM and DEC did we have to do anything with IBM and DEC in the 90s, early 90s, when we created BC client server, IP architectures? No, they weren't in the conversation. >> David: Yeah. >> So, we dint compete with them, just like whatever they do on-prem, keep doing it, I wish you the best. >> But you need to integrate with them and play with them. >> Steve: No. >> Not at all? >> No, no we integrate, here is the thing that's going to happen, so to the on-prem people, it's all point of reference. They look at Cloud as off-prem, I'm going to take my operational model on-prem and I'm going to push it into the Cloud. And if I push it into multiple Clouds, they're going to call that multicloud, see we are multicloud. You're pushing your operational model into the Cloud. What's happening is Cloud has won, it won two and a half years ago with every enterprise. It's like a rock in the water. And what's going to happen is that operational model is moving out to the edge, it's moving to the branch, it's moving to the data center and it's moving into edge computing. That's what's happening... >> So outpost, so I put an outpost in my data center... >> Outpost looks like... >> Is that Aviatrix? >> Absolutely, we're going to get dragged with that... >> Dave: Okay, alright. >> Because we're the networking and network security provider, and as the company pushes out, that operational model is going to move out, not the existing on-prem OT, IT branch office then pushing in. And so, what's happening is you're coming at it from the wrong perspective. And this wave is just going to push over and so I'm just following behind this wave of AWS and Azure and Google. >> Here's the thing, you can do this and you don't have a bunch of legacy deductible debt... >> Steve: Yeah. >> So you can be Cloud Native, multicloud native, I think you called it? >> Steve: Yeah, yeah. >> I love it, you're building castles on the sand. >> Steve: Yeah. >> Jerry Chen's thing. >> Steve: Yeah. >> Now, the thing is, today's executives, they're not as naive as Ken Olsen, UNIX as, "Snake oil," who would need a PC, so they're not in denial. >> They're probably not in denial, yeah. >> Right, and so they have some resources, so the problem is they can't move as fast as you can. So, you're going to do really well. >> Steve: Yeah. >> I think they'll eventually get there Steve, but you're going to be, I don't know how many, four or five years ahead, that's a nice lead. >> That's a bet I'll take any day. >> David: Then what you don't think they'll ever get there? >> No, 10 years. (steve laughing) >> Okay, but they're not going out of business. >> No, I didn't say that. >> I know you didn't. >> What they're doing, I wish them all the best. >> Because a lot of their customers move... >> I don't compete with them. >> Yeah. We were out of time. >> Yeah. >> What did you mean by AWS is like Sandals? You mean like cool like Sandals? >> Steve: Oh, no, no, no. I don't want to... >> You mean like the vacation place? >> Have you ever been to Sandals? >> I never done it. What do you mean by that? >> There coming, there coming. Which version of sandals (indistinct)? (people cross talking) >> This is for an enterprise by the way, and look, Sandals is great for a lot of people but if you're a Cloud provider, you have to provide the common set of services for the masses because you need to make money. And oh, by the way, when you go to Sandals, go try it, like get a bottle of wine, they say, "We got red wine or white wine?" "Oh, great, what kind of red wine?" "No, red wine and it's in a box." And they hope that you won't know the difference. The problem is some people in enterprises want Four Seasons, so they want to be able to swipe the card and get a good bottle of wine. And so that's the thing with the Cloud, but the Cloud can't offer up a 200 bottle of wine to everybody. My mom loves box wine, so give her box wine. Where ISBs like us come in, is great but complimentary to the Cloud provider for that person who wants that nice bottle of wine because if AWS had to provide all this level of functionality for everybody, their instant sizes would be too big, >> Too much cost for that. (people cross talking) You're right on. And as long as you can innovate fast and stay ahead of that and keep adding value... >> Well, here's the thing, they're not going to do it for multicloud either though. >> David: I wouldn't trust them to do it with multicloud. >> No. >> David: I wouldn't. >> No enterprise would and I don't think they would ever do it anyway. >> That makes sense. Steve, we've got to go man. You're awesome, love to have you on theCUBE, come back anytime. >> Awesome, thank you. >> All right, keep it right there everybody. You're watching theCUBE, the leader in enterprise tech coverage. (bright music)

>> Narrator: From the Bellagio hotel in Las Vegas, it's the Cube, covering UiPath Forward IV, brought to you by UiPath. >> Welcome back to the Cube, live at the Bellagio in Las Vegas. Lisa Martin, with Dave Vellante. We are with UiPath at Forward IV. The next topic of conversation is going to be a good one, and that's because it's automation for good. I've got two guests here joining Dave and me, James Droskoski, Strategic Account Exec at UiPath joins us and Brian Khlochkoff, head of automation at Dentsu. Guys, welcome to the program. >> Yeah. Thank you. >> Thanks for having us. >> Yeah. Happy to be here. >> So we're going to, we're going to to dig into automation for good, which is going to be a really feel-good conversation. We're going to get into what you're doing, but Brian, I wanted you to give the audience an overview of Dentsu as an organization. Who are you, what do you guys do? >> Sure. So Dentsu is a large network of advertising agencies. We're about 45,000 people large, 10 billion plus in revenue, going across for 125 markets. So we're a large enterprise advertising media, creative CXM type business. We're really focused on helping to elevate our clients' value when it comes to the value proposition around marketing, advertising, and media. >> So you think about that as a, as a, as a, a business that maybe, you know, it's hard to understand where automation might fit in. On the other hand, it's like a lot of moving parts, a lot of arms and legs. >> Brian: Hmmm. So how are you applying automation to the business? >> Sure. So when we first started doing proof of concepts level approaches, we approach things in a traditional, hey, let's go look at the shared services groups. Why are we having invoice processing delays? Things like that. And we started being a bit more prescriptive and proactive about how we were applying the limited POC budget we had to go after these problems. And we started doing some root cause analysis to understand the interaction between the back office functions and the mid office functions. And what we uncovered was that we could actually be really good custodians of budget and enable people at the same time by solving for problems at a root cause analysis level. So what I mean by that is even the invoices coming down the pipe, and it's not getting processed because it's missing critical information that could be easily added six processes upstream. So what really helped elevate the conversation that we're having around automation for good and be a catalyst for what we're going to talk about a bit later is, we just started connecting people from the mid office to the back office, helping them understand, hey, if we actually follow process properly, put the right controls in place with RPA to generate critical data elements on those invoices, Shaler in the back office doesn't have to work the weekends because there's not a pipeline backload of invoices for them to process. So we actually connected those mid office people with the back office people, and it really drove that human connection to drive the change management and then our automation journey. And that's kind of been the crux of what we've wanted to do over the past four years, finding ways to elevate our people's potential by integrating automation and AI into their actual day-to-day work. >> Hmm. So tech for good is a theme that you hear a lot and as a, as a media company, that, that, that kind of, we're not gotcha media, you know, we've more want to tell the story of tech athletes, and I think we've done a pretty good job of that over the past decade, but so it goes, tech's under fire constantly. It was basically big tech. We hear the Facebook hearings today and so forth, but so automation kind of early days, oh, you're going to take away my job. I think generally speaking with the fatigue of Zoom and the perpetual workday, people begin to understand that, hey, maybe automation is a good thing, but automation for good, what, what is that, James? >> Yeah, well, it's, it's not doing technology for the sake of technology. You know, at the end of the day, when we implement solutions with our customers like Dentsu, it's about, what's the impact? What's the change? What's the benefit? And what's unique about Dentsu is, because they've grown through acquisition and there are lots of different companies come together, you have to focus on the people first cause there is no one process or one system that we can look and just automate that system or process. So automation for good is about focusing on the people and how do we take the solutions and the programs and the technologies we have, make an impact so that somebody's day is better. Their, their, their job is better. That process are doing is easier and they can focus on more of the things that make them different. You know, specifically as we, we'll uncover in the conversation, you know, we looked at a program that Dentsu is doing around working with different types of people, as far as people with autism and what was the impact we could do there. And that's uncovered a journey that we've been together for the last two years around seeing we can have, we can make an impact with those types of folks who might not get the same types of opportunities that everybody else. >> Brian, talk about the, the catalyst for that program at Dentsu, couple years ago. >> Sure, so it goes back to that foundational layer of elevating people's potential. So the testimonial that we had from our own employees around applying automation, meaningful ways to progress their day to day came from an employee in the mid office who said, I didn't go $160,000 in student debt to copy paste stuff from Excel into this proprietary platform that we use for media. And that really resonated with us as leaders in this space and with our executive leadership, because there was a gap between what our people's skills were and what they were actually doing. They wanted to do Mad Men type stuff. They wanted to be the Don Draper's and the Peggy Olsen's of our industry. And they were losing that opportunity because we weren't tapping into the skills that they had to drive human-centric solutions for our clients. So taking that concept, we looked at the partnerships that we have with our outsourcing providers and Autonomy Works, which we're going to be doing a session later tomorrow with the CEO, Dave Friedman, we're going to spend a lot of time talking about how the unique skill sets of that company and those people can actually elevate them to do more tech-enabled work, but also enabling our own team to focus on building solutions with the skills that we have by allowing them to use the skills that they have to do the machine-learning training of models and things like that, which they really Excel at from a detail-oriented perspective. And that's not only a feel good story, but it's, it's great for our business because the resources on my immediate team are building product, they're building solutions, and we can rely on an excellent partner in them to help us with the maintenance overhead that we're creating through those solutions. And eventually through automation cloud, driving better outcomes through positive, negative reinforcement within machine learning. >> And there's specific examples with individuals with autism, correct? >> Correct. That's right. >> Add some color to that. What is that all about? >> Yeah. Let me tell you a little story. So when, when they first brought the conversation to me, I was terrified because I, the type of work that they were outsourcing was very repetitive rule-based and I'm like, this is perfect for automate. This is exactly what we automate. I was terrified that the program we were going to work on together was going to eliminate the program. And so I was, you know, cautiously, you know, approached it- (Dave laughs) >> How ironic. (laughing) >> I was like, hey, that sounds like a great idea. And I hung up. I was like, oh, how are we going to, how am I going to figure out this one? But through the conversation, and we just started, you know, brainstorming and putting our heads together. What was interesting is, because of the way that automations work, as far as being very structured and repetitive, it lends itself well to workers with autism. It's exactly the way they think and what we actually found after kind of coming up with the collaborative ideas, hey, wait a second. We were already doing these kind of bodathon, hackathon type programs with the Dentsu employees, teaching them the skills, how to build automations for themselves. What if we kind of modified it and adjusted it to cater to these types of individuals who learn differently, we have to approach it differently. And we went through the program, we adjusted everything. And what was incredible to see was they thrived with the ability to learn how to work this way. They built things that made them more productive, that created more capacity. They could do more with less now, work with more customers, do more work for, for their, for their customers because they had this almost assistant that was kind of like them. And it was, it was just so rewarding. You know, we talk about, again, what's automation for good all about? It's about that personal reward. >> Brian: Yeah. I mean, for me, you know, we didn't sell any more licenses or it wasn't about the commercial transaction. It was about, you know, catering to the segment of the workforce that, first of all, it was very educate, enlightening to me to see how many folks are out there that are unemployed. And I got to meet these first 15 individuals that couldn't have been more amazing and more smart and more diligent and hardworking, and that the numbers are something in the lines of between 50% and 90% unemployed because they just don't get the same opportunities as people without autism. It's kind of the world's set up for us. So to know that we could do this kind of program together to go have an impact in this community, was the reward in and of itself. And, you know, we've since been working together on how we continue to expand that, how do we, you know, take that forward and bring that everywhere? Cause that's the end of the day, I think beyond, you know, revenue, this is the stuff that really matters, especially in an organization at Dentsu that, this is important. >> Yeah. And I think building on the missed opportunity piece around 50% to 90% being unemployed, that's a missed opportunity for business as well. So those skills are so niche and they're so necessary for us to thrive within an environment that's moving as rapidly as we are, because we just can't keep pace with the change of feature sets that are being released, coupled with maintaining existing solutions that we've built. So it's in cross enabling people to really compliment each other's unique skills and strengths based off of strong, true partnership. So it really became a beautiful three-way partnership between Dentsu, Autonomy Works and UiPath that we continue to evolve as UiPath makes additional releases with emerging tech that we're officially hearing about right now. So we have a ton of different ideas that we can bring that into the fold. And what resonates with us the most is hearing different perspectives on how to apply that coming from that working group. So just a different way of thinking about things and the diversity of thought really resonates with, hey, are we actually applying this thing the right way? Should we be thinking about this differently? Cause you get a lot of, yes, people, you know, when we come and talk to people about how to apply this technology and when you have somebody with a different perspective, it's able to help us figure out what our long-term strategies are actually going to look like, but taking advantage of the resources and partnerships that we already have in place. >> In terms of that strategic vision, how do you think this three-way partnership that you mentioned is going to influence that percentage of those, these individuals who are unemployed? What are you, any predictions on how much you can bring that down with automation? >> I think that depends on Dave's staffing plan. (James laughs) But, but the goal is to grow, right? So I mean this, this is a, a startup out of Chicago that has, you know, a healthy amount of staff, but finding ways to apply those skills in new ways with technology that's emerging, the horizon is your, is your end point. Right? And I think with the advent of low-code no-code machine-learning, coming into this type of a platform, it's, it's only opportunistic, there's only, there's only things ahead of us to do that. We just have to make sure that we train people properly and give them that opportunity cause they're going to run with it with the right leadership and those skills. >> Yeah. What, what's exciting also is, is, you know, what started as an idea and a conversation that's now turned into a pilot program and a little bit of expansion of the stuff we're working on together, we've taken some of the excitement and spread it beyond that now. So we've got partners like ENY and PWC and Revature that are saying, and Specialisterne and Automattic who helped in the initial program saying, how can we help? What can we do? How can we broaden this and how can we go out to the larger community and make a bigger impact? So, you know, I think it's exciting. We know we can see how fast RPA and these types of technologies are causing change. And we got to make sure that people don't get left behind. Especially, you know, someone as this important part of a segment of a workforce. If we can equip them with these skills to be relevant to their current employers or future employers, I think it's, it's critical. You know, another like, moment for me during this process was, I took for granted, you know, what working actually means, right? It creates independence for us, right? So you get a job, you get paid and generate income. You have the independence now to go live on your own, for, provide for yourself. A lot of these individuals, I learned are still living with their parents because they can't get employment. They don't have that independence that we take for granted. So I think, again, that's the essence of what automation for good is all about is, is being able to go and make an impact like that, to that community. And it's, you know, we talk about cultures and brands and, you know, it's also great to work with an organization like Dentsu cause they get it, right? Their product is ideas. It's human capital is their, their main ingredient of what they generate value for their customers. And so be able to take that and help people is just, I think what it's all about. >> You're lucky both to be in a business that the incentives are aligned. >> Yeah. >> You're not in businesses that are designed to appropriate data and push ads in front of our face or- >> James: Yeah. >> And a lot of big companies, It's almost like, okay, we got to do this. I mean, I don't mean to overstate this, but we have to do this because we're big and we're rich. >> James: Yeah. >> And so, and if we don't, we're going to get attacked. >> James: Yeah. >> Okay, and some of it, I can check, check box and to put somebody in charge of it. >> James: Yep. >> You know, often times a woman or a person of color. And I shouldn't be negative on that. >> James: Yeah. That's fine. That's good to do. But it just seems like there's a nice alignment with automation. >> James: Oh. >> AI could be similar because I mean, yeah. It can be used for really bad. Automation, okay, maybe takes, the perception is that it takes jobs away, but it's a really nice alignment that you can point at a lot of different initiatives. >> Yeah. >> So I think that's really a fortune- >> I know that's, that's what defines a partnership, right? It's that alignment of long-term interests that, you know, you make the investments now and the sacrifices now to drive that. It's not just commercial. It's not just transactional. >> Dave: Yeah. >> We were talking about the opportunities for these types of people and for us as a customer and for UiPath, it's, it exists within that AI conversation that you were just talking about. >> Dave: Yeah. >> Because from a technical perspective, you want to mitigate as much algorithmic bias within your training models. That's what these people are doing. It, it's helping to train models much more rapidly and effectively and objectively than we could have done otherwise. And that's, having that as part of our extended partnership within our network is going to accelerate the type of work that we want to do within the releases that we're seeing coming out of this conference because we don't have to worry about oh, well, we got to focus on tax forms and training the models to notice a signature because Autonomy Works has us covered there. They're enabling us to do more. We're enabling them to do a little more. >> Hmmm. And that's, that's the beauty of this intersection between the partners. >> Brian, I presume you talk with prospective customers of UiPaths. And I presume also that you probably looked at some of their competitors. If you think about what differentiates this fast-moving company, they talked this morning about the cadence that releases. Whew, very fast. (laughing) >> Brian: Yeah, that's a lot. >> Why UiPath for Dentsu? >> UiPath has been a tremendous partner for us since about 2017. And we've been able to move on that journey with UiPath. We've been able to help understand the product roadmaps and move at a similar pace as each other. So we're really lucky in that we have the flexibility as an advertising and media company that we're not beholden to internal audits, external audits, and really defined regulatory bodies. So we made a decision, you know, what, six, seven months ago to collapse six UiPath on-prem instances and migrate to cloud with the sponsorship of our global CTO and our Amaris CTO, just because it was the right thing to do. And because it would enable this type of partnership with external providers. So being able to move at that similar pace from a release cycle, but also from a feature adoption perspective, it's, it just makes the most sense for us. And we have that liberty to go to go do those things as we need to. >> Yeah, so the move to the cloud, you get, you're able to take advantage much faster- >> James: Yeah. >> Because what did, what did we hear this morning? You release every six months? >> James: Yep. >> Yes. Which is typical for an on-prem. >> James: Yeah. >> And then, but you got to prepare for that. >> James: Yeah. I don't know how many N minus ones you support, but it's not infinite. >> James: Yeah. >> You got to move people along. So people have to prep, whereas now in the cloud, there's the feature, boom. >> Oh yeah. So being investing automation for good topic, it's not, it's about automation for good across people in general, within internally to us and externally to us, for our clients, for our employees and for our partners. The automation cloud enables that to happen much more seamlessly because we don't have the technical debt in place that requires people to VPN into our network and go through the bureaucracy of security, legal, and privacy, which we've already done by the way, for those conversations, bureaucratically still needs to happen. With automation cloud, we're able to spin up autonomy Works employees in real time and give them the right set of access to go pursue the use cases that they want to, and that we need them to. So that, that technical debt release that we've experienced through the automation cloud is what's enabling us to do this type of good work. >> It makes sense. A bit more, less friction, obviously, greater scale. >> Yeah. >> Easier to experiment. >> Yeah. >> Fail fast. >> We went from 12 separate programs to one program in a matter of a couple of months. >> It was wild. (Brian laughs) >> And I imagine you're only really scratching the surface here with what you're doing with automation. That really the horizon is the limit as you said. Guys, thank you for joining us, talking about automation for good. What you're doing at Dentsu RPA with autistic adults, there's probably so many other great use cases that will come from this. Guys, we appreciate your time. >> Yeah. >> Thanks for having us. Thank you. >> Thanks you guys, awesome. >> For Dave Vellante, I'm Lisa Martin coming to you from Vegas, UiPath forward IV. [light-hearted music plays]

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> As you know by now, Jeff Bezos, CEO of Amazon, is stepping aside from his CEO role and AWS CEO, Andy Jassy, is being promoted to head all of Amazon. Bezos, of course, is going to remain executive chairman. Now, 15 years ago, next month, Amazon launched it's simple storage service, which was the first modern cloud offering. And the man who wrote the business plan for AWS, was Andy Jassy, and he's navigated the meteoric rise and disruption that has seen AWS grow into a $45 billion company that draws off the vast majority of Amazon's operating profits. No one in the media has covered Jassy more intimately and closely than John Furrier, the founder of SiliconANGLE. And John joins us today to help us understand on theCUBE this move and what we can expect from Jassy in his new role, and importantly what it means for AWS. John, thanks for taking the time to speak with us. >> Hey, great day. Great to see you as always, we've done a lot of interviews together over the years and we're on our 11th year with theCUBE and SiliconANGLE. But I got to be excited too, that we're simulcasters on Clubhouse, which is kind of cool. Love Clubhouse but not since the, in December. It's awesome. It's like Cube radio. It's like, so this is a Cube talk. So we opened up a Clubhouse room while we're filming this. We'll do more live hits in studio and syndicate the Clubhouse and then take questions after. This is a huge digital transformation moment. I'm part of the digital transformation club on Clubhouse which has almost 5,000 followers at the moment and also has like 500 members. So if you're not on Clubhouse, yet, if you have an iPhone go check it out and join the digital transformation club. Android users you'll have to wait until that app is done but it's really a great club. And Jeremiah Owyang is also doing a lot of stuff on digital transformation. >> Or you can just buy an iPhone and get in. >> Yeah, that's what people are doing. I can see all the influences are on there but to me, the digital transformation, it's always been kind of a cliche, the consumerization of IT, information technology. This has been the boring world of the enterprise over the past, 20 years ago. Enterprise right now is super hot because there's no distinction between enterprise and society. And that's clearly the, because of the rise of cloud computing and the rise of Amazon Web Services which was a side project at AWS, at Amazon that Andy Jassy did. And it wasn't really pleasant at the beginning. It was failed. It failed a lot and it wasn't as successful as people thought in the early days. And I have a lot of stories with Andy that he told me a lot of the inside baseball and we'll share that here today. But we started covering Amazon since the beginning. I was as an entrepreneur. I used it when it came out and a huge fan of them as a company because they just got a superior product and they have always had been but it was very misunderstood from the beginning. And now everyone's calling it the most important thing. And Andy now is becoming Andy Jassy, the most important executive in the world. >> So let's get it to the, I mean, look at, you said to me over holidays, you thought this might have something like this could happen. And you said, Jassy is probably in line to get this. So, tell us, what can you tell us about Jassy? Why is he qualified for this job? What do you think he brings to the table? >> Well, the thing that I know about Amazon everyone's been following the Amazon news is, Jeff Bezos has a lot of personal turmoil. They had his marriage fail. They had some issues with the smear campaigns and all this stuff going on, the run-ins with Donald Trump, he bought the Washington post. He's got a lot of other endeavors outside of Amazon cause he's the second richest man in the world competing with Elon Musk at Space X versus Blue Origin. So the guy's a billionaire. So Amazon is his baby and he's been running it as best he could. He's got an executive team committee they called the S team. He's been grooming people in the company and that's just been his mode. And the rise of AWS and the business performance that we've been documenting on SiliconANGLE and theCUBE, it's just been absolutely changing the game on Amazon as a company. So clearly Amazon Web Services become a driving force of the new Amazon that's emerging. And obviously they've got all their retail business and they got the gaming challenges and they got the studios and the other diversified stuff. So Jassy is just, he's just one of those guys. He's just been an Amazonian from day one. He came out of Harvard business school, drove across the country, very similar story to Jeff Bezos. He did that in 1997 and him and Jeff had been collaborating and Jeff tapped him to be his shadow, they call it, which is basically technical assistance and an heir apparent and groomed him. And then that's how it is. Jassy is not a climber as they call it in corporate America. He's not a person who is looking for a political gain. He's not a territory taker, but he's a micromanager. He loves details and he likes to create customer value. And that's his focus. So he's not a grandstander. In fact, he's been very low profile. Early days when we started meeting with him, he wouldn't meet with press regularly because they weren't writing the right stories. And everyone is, he didn't know he was misunderstood. So that's classic Amazon. >> So, he gave us the time, I think it was 2014 or 15 and he told us a story back then, John, you might want to share it as to how AWS got started. Why, what was the main spring Amazon's tech wasn't working that great? And Bezos said to Jassy, going to go figure out why and maybe explain how AWS was born. >> Yeah, we had, in fact, we were the first ones to get access to do his first public profile. If you go to the Google and search Andy Jassy, the trillion dollar baby, we had a post, we put out the story of AWS, Andy Jassy's trillion dollar baby. This was in early, this was January 2015, six years ago. And, we back then, we posited that this would be a trillion dollar total addressable market. Okay, people thought we were crazy but we wrote a story and he gave us a very intimate access. We did a full drill down on him and the person, the story of Amazon and that laid out essentially the beginning of the rise of AWS and Andy Jassy. So that's a good story to check out but really the key here is, is that he's always been relentless and competitive on creating value in what they call raising the bar outside Amazon. That's a term that they use. They also have another leadership principle called working backwards, which is like, go to the customer and work backwards from the customer in a very Steve Job's kind of way. And that's been kind of Jobs mentality as well at Apple that made them successful work backwards from the customer and make things easier. And that was Apple. Amazon, their philosophy was work backwards from the customer and Jassy specifically would say it many times and eliminate the undifferentiated heavy lifting. That was a key principle of what they were doing. So that was a key thesis of their entire business model. And that's the Amazonian way. Faster, cheaper, ship it faster, make it less expensive and higher value. While when you apply the Amazon shipping concept to cloud computing, it was completely disrupted. They were shipping code and services faster and that became their innovation strategy. More announcements every year, they out announced their competition by huge margin. They introduced new services faster and they're less expensive some say, but in the aggregate, they make more money but that's kind of a key thing. >> Well, when you, I was been listening to the TV today and there was a debate on whether or not, this support tends that they'll actually split the company into two. To me, I think it's just the opposite. I think it's less likely. I mean, if you think about Amazon getting into grocery or healthcare, eventually financial services or other industries and the IOT opportunity to me, what they do, John, is they bring in together the cloud, data and AI and they go attack these new industries. I would think Jassy of all people would want to keep this thing together now whether or not the government allows them to do that. But what are your thoughts? I mean, you've asked Andy this before in your personal interviews about splitting the company. What are your thoughts? >> Well, Jon Fortt at CNBC always asked the same question every year. It's almost like the standard question. I kind of laugh and I ask it now too because I liked Jon Fortt. I think he's an awesome dude. And I'll, it's just a tongue in cheek, Jassy. He won't answer the question. Amazon, Bezos and Jassy have one thing in common. They're really good at not answering questions. So if you ask the same question. They'll just say, nothing's ever, never say never, that's his classic answer to everything. Never say never. And he's always said that to you. (chuckles) Some say, he's, flip-flopped on things but he's really customer driven. For example, he said at one point, no one should ever build a data center. Okay, that was a principle. And then they come out and they have now a hybrid strategy. And I called them out on that and said, hey, what, are you flip-flopping? You said at some point, no one should have a data center. He's like, well, we looked at it differently and what we meant was is that, it should all be cloud native. Okay. So that's kind of revision, but he's cool with that. He says, hey, we'll revise based on what customers are doing. VMware working with Amazon that no one ever thought that would happen. Okay. So, VMware has some techies, Raghu, for instance, over there, super top notch. He worked with Jassy, directly in his team Sanjay Poonen when they went to business school together, they cut a deal. And now Amazon essentially saved VMware, in my opinion. And Pat Gelsinger drove that deal. Now, Pat Gelsinger, CEO, Intel, and Pat told me that directly in candid conversation off theCUBE, he said, hey, we have to make a decision either we're going to be in cloud or we're not going to be in cloud, we will partner. And I'll see, he was Intel. He understood the Intel inside mentality. So that's good for VMware. So Jassy does these kinds of deals. He's not afraid he's got a good stomach for business and a relentless competitor. >> So, how do you think as you mentioned Jassy is a micromanager. He gets deep into the technology. Anybody who's seen his two hour, three hour keynotes. No, he has a really fine grasp of the technology across the entire stack. How do you think John, he will approach things like antitrust, the big tech lash of the unionization of the workforce at Amazon? How do you think Jassy will approach that? >> Well, I think one of the things that emerges Jassy, first of all, he's a huge sports fan. And many people don't know that but he's also progressive person. He's very progressive politically. He's been on the record and off the record saying things like, obviously, literacy has been big on, he's been on basically unrepresented minorities, pushing for that, and certainly cloud computing in tech, women in tech, he's been a big proponent. He's been a big supporter of Teresa Carlson. Who's been rising star at Amazon. People don't know who Teresa Carlson is and they should check out her. She's become one of the biggest leaders inside Amazon she's turned around public sector from the beginning. She ran that business, she's a global star. He's been a great leader and he's been getting, forget he's a micromanager, he's on top of the details. I mean, the word is, and nothing gets approved without Andy, Andy seeing it. But he's been progressive. He's been an Amazon original as they call it internally. He's progressive, he's got the business acumen but he's perfect for this pragmatic conversation that needs to happen. And again, because he's so technically strong having a CEO that's that proficient is going to give Amazon an advantage when they have to go in and change how DC works, for instance, or how the government geopolitical landscape works, because Amazon is now a global company with regions all over the place. So, I think he's pragmatic, he's open to listening and changing. I think that's a huge quality >> Well, when you think of this, just to set the context here for those who may not know, I mean, Amazon started as I said back in 2006 in March with simple storage service that later that year they announced EC2 which is their compute platform. And that was the majority of their business, is still a very large portion of their business but Amazon, our estimates are that in 2020, Amazon did 45 billion, 45.4 billion in revenue. That's actually an Amazon reported number. And just to give you a context, Azure about 26 billion GCP, Google about 6 billion. So you're talking about an industry that Amazon created. That's now $78 billion and Amazon at 45 billion. John they're growing at 30% annually. So it's just a massive growth engine. And then another story Jassy told us, is they, he and Jeff and the team talked early on about whether or not they should just sort of do an experiment, do a little POC, dip their toe in and they decided to go for it. Let's go big or go home as Michael Dell has said to us many times, I mean, pretty astounding. >> Yeah. One of the things about Jassy that people should know about, I think there's some compelling relative to the newest ascension to the CEO of Amazon, is that he's not afraid to do new things. For instance, I'll give you an example. The Amazon Web Services re-invent their annual conference grew to being thousands and thousands of people. And they would have a traditional after party. They called a replay, they'd have a band like every tech conference and their conference became so big that essentially, it was like setting up a live concert. So they were spending millions of dollars to set up basically a one night concert and they'd bring in great, great artists. So he said, hey, what's been all this cash? Why don't we just have a festival? So they did a thing called Intersect. They got LA involved from creatives and they basically built a weekend festival in the back end of re-invent. This was when real life was, before COVID and they turned into an opportunity because that's the way they think. They like to look at the resources, hey, we're already all in on this, why don't we just keep it for the weekend and charge some tickets and have a good time. He's not afraid to take chances on the product side. He'll go in and take a chance on a new market. That comes from directly from Bezos. They try stuff. They don't mind failing but they put a tight leash on measurement. They work backwards from the customer and they are not afraid to take chances. So, that's going to board well for him as he tries to figure out how Amazon navigates the contention on the political side when they get challenged for their dominance. And I think he's going to have to apply that pragmatic experimentation to new business models. >> So John I want you to take on AWS. I mean, despite the large numbers, I talked about 30% growth, Azure is growing at over 50% a year, GCP at 83%. So despite the large numbers and big growth the growth rates are slowing. Everybody knows that, we've reported it extensively. So the incoming CEO of Amazon Web Services has a TAM expansion challenge. And at some point they've got to decide, okay, how do we keep this growth engine? So, do you have any thoughts as to who might be the next CEO and what are some of their challenges as you see it? >> Well, Amazon is a real product centric company. So it's going to be very interesting to see who they go with here. Obviously they've been grooming a lot of people. There's been some turnover. You had some really strong executives recently leave, Jeff Wilkes, who was the CEO of the retail business. He retired a couple of months ago, formerly announced I think recently, he was probably in line. You had Mike Clayville, is now the chief revenue officer of Stripe. He ran all commercial business, Teresa Carlson stepped up to his role as well as running public sector. Again, she got more power. You have Matt Garman who ran the EC2 business, Stanford grad, great guy, super strong on the product side. He's now running all commercial sales and marketing. And he's also on the, was on Bezos' S team, that's the executive kind of team. Peter DeSantis is also on that S team. He runs all infrastructure. He took over for James Hamilton, who was the genius behind all the data center work that they've done and all the chip design stuff that they've innovated on. So there's so much technical innovation going on. I think you still going to see a leadership probably come from, I would say Matt Garman, in my opinion is the lead dog at this point, he's the lead horse. You could have an outside person come in depending upon how, who might be available. And that would probably come from an Andy Jassy network because he's a real fierce competitor but he's also a loyalist and he likes trust. So if someone comes in from the outside, it's going to be someone maybe he trusts. And then the other wildcards are like Teresa Carlson. Like I said, she is a great woman in tech who's done amazing work. I've profiled her many times. We've interviewed her many times. She took that public sector business with Amazon and changed the game completely. Outside the Jedi contract, she was in competitive for, had the big Trump showdown with the Jedi, with the department of defense. Had the CIA cloud. Amazon set the standard on public sector and that's directly the result of Teresa Carlson. But she's in the field, she's not a product person, she's kind of running that group. So Amazon has that product field kind of structure. So we'll see how they handle that. But those are the top three I think are going to be in line. >> So the obvious question that people always ask and it is a big change like this is, okay, in this case, what is Jassy going to bring in? And what's going to change? Maybe the flip side question is somewhat more interesting. What's not going to change in your view? Jassy has been there since nearly the beginning. What are some of the fundamental tenets that he's, that are fossilized, that won't change, do you think? >> I think he's, I think what's not going to change is Amazon, is going to continue to grow and develop their platform business and enable more SaaS players. That's a little bit different than what Microsoft's doing. They're more SaaS oriented, Office 365 is becoming their biggest application in terms of revenue on Microsoft side. So Amazon is going to still have to compete and enable more ecosystem partners. I think what's not going to change is that Bezos is still going to be in charge because executive chairman is just a code word for "not an active CEO." So in the corporate governance world when you have an executive chairman, that's essentially the person still in charge. And so he'll be in charge, will still be the boss of Andy Jassy and Jassy will be running all of Amazon. So I think that's going to be a little bit the same, but Jassy is going to be more in charge. I think you'll see a team change over, whether you're going to see some new management come in, Andy's management team will expand, I think Amazon will stay the same, Amazon Web Services. >> So John, last night, I was just making some notes about notable transitions in the history of the tech business, Gerstner to Palmisano, Gates to Ballmer, and then Ballmer to Nadella. One that you were close to, David Packard to John Young and then John Young to Lew Platt at the old company. Ellison to Safra and Mark, Jobs to Cook. We talked about Larry Page to Sundar Pichai. So how do you see this? And you've talked to, I remember when you interviewed John Chambers, he said, there is no rite of passage, East coast mini-computer companies, Edson de Castro, Ken Olsen, An Wang. These were executives who wouldn't let go. So it's of interesting to juxtapose that with the modern day executive. How do you see this fitting in to some of those epic transitions that I just mentioned? >> I think a lot of people are surprised at Jeff Bezos', even stepping down. I think he's just been such the face of Amazon. I think some of the poll numbers that people are doing on Twitter, people don't think it's going to make a big difference because he's kind of been that, leader hand on the wheel, but it's been its own ship now, kind of. And so depending on who's at the helm, it will be different. I think the Amazon choice of Andy wasn't obvious. And I think a lot of people were asking the question who was Andy Jassy and that's why we're doing this. And we're going to be doing more features on the Andy Jassy. We got a tons, tons of content that we've we've had shipped, original content with them. We'll share more of those key soundbites and who he is. I think a lot of people scratching their head like, why Andy Jassy? It's not obvious to the outsiders who don't know cloud computing. If you're in the competing business, in the digital transformation side, everyone knows about Amazon Web Services. Has been the most successful company, in my opinion, since I could remember at many levels just the way they've completely dominated the business and how they change others to be dominant. So, I mean, they've made Microsoft change, it made Google change and even then he's a leader that accepts conversations. Other companies, their CEOs hide behind their PR wall and they don't talk to people. They won't come on Clubhouse. They won't talk to the press. They hide behind their PR and they feed them, the media. Jassy is not afraid to talk to reporters. He's not afraid to talk to people, but he doesn't like people who don't know what they're talking about. So he doesn't suffer fools. So, you got to have your shit together to talk to Jassy. That's really the way it is. And that's, and he'll give you mind share, like he'll answer any question except for the ones that are too tough for him to answer. Like, are you, is facial recognition bad or good? Are you going to spin out AWS? I mean these are the hard questions and he's got a great team. He's got Jay Carney, former Obama press secretary working for him. He's been a great leader. So I'm really bullish on, is a good choice. >> We're going to jump into the Clubhouse here and open it up shortly. John, the last question for you is competition. Amazon as a company and even Jassy specifically I always talk about how they don't really focus on the competition, they focus on the customer but we know that just observing these folks Bezos is very competitive individual. Jassy, I mean, you know him better than I, very competitive individual. So, and he's, Jassy has been known to call out Oracle. Of course it was in response to Larry Ellison's jabs at Amazon regarding database. But, but how do you see that? Do you see that changing at all? I mean, will Amazon get more publicly competitive or they stick to their knitting, you think? >> You know this is going to sound kind of a weird analogy. And I know there's a lot of hero worshiping on Elon Musk but Elon Musk and Andy Jassy have a lot of similarities in the sense of their brilliance. They got both a brilliant people, different kinds of backgrounds. Obviously, they're running different things. They both are builders, right? If you were listening to Elon Musk on Clubhouse the other night, what was really striking was not only the magic of how it was all orchestrated and what he did and how he interviewed Robin Hood. He basically is about building stuff. And he was asked questions like, what advice do you give startups? He's like, if you need advice you shouldn't be doing startups. That's the kind of mentality that Jassy has, which is, it's not easy. It's not for the faint of heart, but Elon Musk is a builder. Jassy builds, he likes to build stuff, right? And so you look at all the things that he's done with AWS, it's been about enabling people to be successful with the tools that they need, adding more services, creating things that are lower price point. If you're an entrepreneur and you're over the age of 30, you know about AWS because you know what, it's cheaper to start a business on Amazon Web Services than buying servers and everyone knows that. If you're under the age of 25, you might not know 50 grand to a hundred thousand just to start something. Today you get your credit card down, you're up and running and you can get Clubhouses up and running all day long. So the next Clubhouse will be on Amazon or a cloud technology. And that's because of Andy Jassy right? So this is a significant executive and he continue, will bring that mindset of building. So, I think the digital transformation, we're in the digital engine club, we're going to see a complete revolution of a new generation. And I think having a new leader like Andy Jassy will enable in my opinion next generation talent, whether that's media and technology convergence, media technology and art convergence and the fact that he digs music, he digs sports, he digs tech, he digs media, it's going to be very interesting to see, I think he's well-poised to be, and he's soft-spoken, he doesn't want the glamorous press. He doesn't want the puff pieces. He just wants to do what he does and he puts his game do the talking. >> Talking about advice at startups. Just a quick aside. I remember, John, you and I when we were interviewing Scott McNealy former CEO of Sun Microsystems. And you asked him advice for startups. He said, move out of California. It's kind of tongue in cheek. I heard this morning that there's a proposal to tax the multi-billionaires of 1% annually not just the one-time tax. And so Jeff Bezos of course, has a ranch in Texas, no tax there, but places all over. >> You see I don't know. >> But I don't see Amazon leaving Seattle anytime soon, nor Jassy. >> Jeremiah Owyang did a Clubhouse on California. And the basic sentiment is that, it's California is not going away. I mean, come on. People got to just get real. I think it's a fad. Yeah. This has benefits with remote working, no doubt, but people will stay here in California, the network affects beautiful. I think Silicon Valley is going to continue to be relevant. It's just going to syndicate differently. And I think other hubs like Seattle and around the world will be integrated through remote work and I think it's going to be much more of a democratizing effect, not a win lose. So that to me is a huge shift. And look at Amazon, look at Amazon and Microsoft. It's the cloud cities, so people call Seattle. You've got Google down here and they're making waves but still, all good stuff. >> Well John, thanks so much. Let's let's wrap and let's jump into the Clubhouse and hear from others. Thanks so much for coming on, back on theCUBE. And many times we, you and I've done this really. It was a pleasure having you. Thanks for your perspectives. And thank you for watching everybody, this is Dave Vellante for theCUBE. We'll see you next time. (soft ambient music)

(thoughtful electronic music) >> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Enterprise applications are an enormous market, and they're enormously important to organizations globally. Essentially, the world's businesses are running on enterprise applications. Companies' processes are wired into these systems, and the investments that they make in people, process, and technology are vital to these companies' success. But it's complicated because many of these systems are decades old. Markets have changed, but the ERP system for example fundamentally hasn't. Hello everyone, and welcome to this week's Wikibon CUBE Insights, powered by ETR. This week, we're going to do a data download on the enterprise software space, and put forth some themes in our thesis around this very important segment. I'd like to do a shout-out to my friend Sarbjeet Johal, who helped me frame this segment, and he's a strategic thinker and he shared some excellent insights for this episode. What I'd first like to do is let's lay out the scope of what we're going to talk about today. So we're going to focus on the core enterprise apps that companies rely on to run their businesses. Talkin' about the systems of record here, the ERP, the financial systems, HR, CRMs, service management we'll put in there. We may touch on some of the other areas, but this is core that we're going to drill into. This is a big, big market. Customers spend many hundreds of billions of dollars in this area, you could argue about a half a trillion. And it's a mature market, as you'll see from the data. Look, it's good to be in the technology business today. This business is doing better than most, and within the technology business, it's better to be in software because of the economics and scale. And if you have a SaaS cloud model, it's even better. But the market, it is fragmented, not nearly as much as it used to be, but there are many specialized areas where leaders have emerged. ServiceNow and ITSM or Workday and HCM are good examples of companies that've specialized and then exploded, first as we saw ServiceNow blow past Workday's valuation. It was nearly 2x at one point. Now, that was before Workday crushed its earnings this week. It's up 15% today. ServiceNow took a slight breather earlier this month, but it's up on Workday sympathy today. Salesforce also beat earnings, and of course replaced Exxon Mobile on the DOW Industrials, can you imagine that? But let's bring it back to this digital transformation that you hear about. This is the big cliche from all the tech companies and especially software players. Now a lot of this DX, I sometimes call it, is related to old systems. It's especially true for the mega-caps like Oracle, SAP, PeopleSoft, JD Edwards, and even Microsoft. Take ERP and some of the mature products for example, like Oracle R12, or SAP R3 or R4. Many of these systems were put in place 15 years ago, and yeah, they're going to need to transform. They are burnt in. They were installed in what, 2005? It was before the iPhone, before social media, before machine learning and AI made its big comeback, and before cloud. These systems were built on the 1.0 of cloud. The businesses have changed but the software really hasn't. It happens every 10 to 15 years, companies have to upgrade or re-implement their systems, and optimize for the way business now runs, because they had to be more competitive and more agile. They can't do it on their old software. And God help you if you made a bunch of custom modifications. Good lucking tryin' to rip those out. And this is why pure play companies in the cloud like ServiceNow and Workday have done so well. They're best-of-breed and they're cloud, and it sets up this age-old battle that we always talk about, best-of-breed versus integrated suites. So let's bring in some of the other themes and feedback that we get from the community. Now we've definitely seen this schism play out between on-prem and cloud plays. And that's created some challenges for the legacy players. People working remotely has meant less data center, less on-prem action for the legacy companies. Now, they have gone out and acquired to get to the cloud and/or they've had to rearchitect their software like Oracle has done with Fusion. But think about something like Oracle Financials. Oracle is tryna migrate them to Fusion, or think about SAP R3, with R4, SAP pushing HANA. All this is going to cloud-based SaaS. So the companies that've been pure play SaaS are doing better, and I say quasi-modern on this slide because Salesforce, ServiceNow, Workday, even Coupa, NetSuite which is now Oracle, SuccessFactors which SAP purchased, et cetera, these are actually pretty old companies, the earlier part of the 2000s or in the case of Salesforce, 1999. And you're seeing some really different pricing models in the market. Things are moving quickly to an OPEX model. You have the legacy perpetual pricing, and it's giving way to subscriptions, and now we even see companies like Datadog and Snowflake with so-called consumption-based pricing models, priced as a true cloud. And we think that that's going to eventually spill into the core SaaS applications. Now one of the concerns that we've heard from the community is that some of the traditional players that were able to hide from COVID earlier this year might not have enough deferred revenue dry powder to continue to power through the pandemic, but so far the picture continues to look pretty strong for the software companies. We'll get into some of that. Now, finally, this is a premise that I talked to Sarbjeet about, the disruption perhaps comes from cloud and developer ecosystems. Y'know I remember John Furrier and I had a conversation awhile back with Jerry Chen from Greylock. It was on theCUBE, and it was kind of like, went like this. People were talking about whether AWS was going to enter the applications market, and the thesis here is no, or not in the near future. Rather, the disruptive play, and this is really Sarbjeet's premise, is to provide infrastructure for innovation, and a PaaS layer for differentiation, and developers will build modern cloud-native apps to compete with the SaaS players on top of this. This is intriguing to me, and is likely going to play out over the next decade, but it's going to take a while, because these SaaS players are, they're very large, and they continue to pour money into their platforms. Now let's talk about the shift from CAPEX to OPEX and bring in some ETR data. Of course, this was well in play pre-COVID, but the trend has been accelerating. This chart shows data from the August ETR survey, and it was asking people to express their split between CAPEX and OPEX spend, and as you can see, the trend is clear. Goes from 48% last year, 55% today, and moving to over 62% OPEX a year from now. It's no surprise, but I think it could happen even faster depending on the technical debt that organizations have to shed. And hence, the attractiveness again of the SaaS cloud players. So now let's visualize some of the major players in this space, and do some comparisons. Here we show one of our favorite views, and what we're doing here is we juxtapose net score on the vertical axis with market share on the horizontal plane. Remember, net score is a measure of spending momentum. Each quarter, ETR asks buyers, are you planning to spend more or less, and they essentially subtract the lesses from the mores to derive net score. Market share on the other hand is a measure of pervasiveness in the dataset, and it's derived from the number of mentions in the sector divided by the total mentions in the survey, and you can see each metric in that embedded table that we put in there. So I said earlier, this was a pretty mature market and you can see that in the table. Eh, kind of middle-of-the-road net scores with pretty large shared ends, i.e. responses in the dataset, but a lot of red. There are some standouts, however, as you see in the upper right, namely, ServiceNow and Salesforce. These are two pretty remarkable companies. ServiceNow entered the market as a help desk or service management player, and has dramatically expanded its TAM, really to the point where they're aiming at $5 billion in revenue. Salesforce was the first in cloud CRM, and is pushing 20 billion in revenue. I've said many times, these companies are on a collision course, and I stand by that, as any of the next great software companies, and these are two, are going to compete with all the mega-caps, including Oracle, SAP, and Microsoft, and they'll bump into each other. Which brings us to those super-cap companies. You see Microsoft with Dynamics, they show up like they always do. I'm like a broken record on Microsoft. I mean they're everywhere in the survey data. Now Oracle and SAP, they've been extremely acquisitive over the years, and you can see some of their acquisitions on this chart. I've said many times in theCUBE that Larry Olsen used to denigrate his competitors for writing checks instead of code, but he saw the consolidation trend happening in the ERT, ERP space before anyone else did, and with the $10 billion PeopleSoft acquisition in 2005, set off a trend in enterprise software that did a few things. First, it solidified Oracle's position further up the stack. It also set Dave Duffield and Aneel Bhusri off to create a next-generation cloud software company, Workday, which you can see in the chart has a net score up there with ServiceNow, Salesforce, and Coupa, and it also led to Oracle Fusion Middleware, which is designed as an integration point for all these software components, and this is really important because Oracle is moving everything into its cloud. And you can see that its on-prem net score, which puts it deep into negative territory. Now SAP, take a look at them, they have much higher net scores than Oracle, and you can see it's acquired SaaS properties like Ariba, Concur, and SuccessFactors, which have decent momentum. But you know, SAP, and we've talked about this before, is not without its challenges. With SAP, HANA is the answer to all of its problems. The problem is that it's not necessarily the answer to all of SAP's customers' problems. Most of SAP's legacy customers run SAP on Oracle or other databases. HANA is used for the in-memory query workload, but most customers are going to continue to use other databases for their systems of record. So this adds complexity. But HANA is very good at the query piece. However, SAP never did what Oracle did with Fusion, which as you might recall, took more than a decade to get right. HANA is SAP's architectural attempt to unify the SAP portfolio and get, (laughs) really get off of Oracle, but it's many years away, and it's unclear when or if they'll ever get there. All right, let's move on. Here's a look at a similar set of companies, but I wanted to show you this view because it gives you a detailed look at ETR's net score approach, and it tells us a few things more. And remember, this is a survey of almost 1,200 technology buyers. That's the N, that's the respondent rate. So this chart shows the net score granularity for the enterprise players that we were just discussing. Let me explain this. Net score is actually more detailed than what I said before. It comprises responses in four categories. The lime green is new adoptions. The forest green is growth in spending of 6% or more, the gray is flat spend, the pink is a budget shrink of 6% or greater, and the red is retiring the platform. So what this tells us is that there's a big fat middle of stay the same. The lime green is pretty small, but you can see, NetSuite jumps out for new adoptions because they've been very aggressive going after smaller and mid-sized companies, and Coupa, the spend management specialist, shows reasonably strong new adoptions. Now ServiceNow is interesting to me. Not a ton of new adoptions. They've landed the ship and really penetrated larger organizations. And while new adoptions are not off the charts, look at the spending more categories, it's very very strong at 46%. And the other really positive thing for ServiceNow is there's very little red. This company is a beast. Now Salesforce similarly, not tons of new adoptions, but 40% spend more. For a company that size, that's pretty impressive. Workday similarly has a very strong spending profile. At the bottom of the chart, you see a fair amount of red, as we saw on the XY graph. But now, let's take another view of net score. Think of this as a zoom in, which takes those bar charts but shows it in a pie format for individual companies. So we're showing this here for ServiceNow, Workday, and Salesforce, and we've superimposed the net score for these three in green, so you can see ServiceNow at 48%, very good for a company headed toward five billion. Same with Workday, 40% for a company of similar size, and Salesforce has a comparable net score, and is significantly larger than those two revenue-wise. Now this is the same view, this next chart's the same view for SAP and Oracle, and you can see substantially lower than the momentum leaders in terms of net score. But these are much larger companies. SAP's about 33 billion, Oracle's closer to 40 billion. But Oracle especially has seen some headwinds from organizations spending less which drags its net score down. But you're not seeing a lot of replacement in Oracle's base because as I said at the top, these systems are fossilized and many are running on Oracle. And the vast majority of mission-critical workloads are especially running on Oracle. Now remember, this isn't a revenue-weighted view. Oracle charges a steep premium based on the number of cores, and it has a big maintenance stream. So while its net score is kind of sucky, its cashflow is not. All right, let's wrap it up here. We have a very large and mature market. But the semi-modern SaaS players like Salesforce and ServiceNow and Workday, they've gone well beyond escape velocity and solidified their positions as great software companies. Others are trying to follow that suit and compete with the biggest of the bigs, i.e. SAP and Oracle. Now I didn't talk much about Microsoft, but as always they show up prominently. They're huge and they're everywhere in this dataset. What I think is interesting is the competitive dynamics that we talked about earlier. These kind of newer SaaS leaders, they're disrupting Oracle and SAP, but they're also increasingly bumping into each other. You know, ServiceNow has HR for example, and they say that they don't compete with Workday, and that's true. But y'know, these two companies, they eye each other and they angle for account control. Same thing with Salesforce. It's that software mindset. The bigger a software company gets, the more they think they can own the world, because it's software, and if you're good at writing code and you see an opportunity that can add value for your customers, you tend to go after it. Now, we didn't talk much about M&A, but that's going to continue here, especially as these companies look for TAM expansion and opportunities to bring in new capabilities, particularly around data, analytics, machine learning, AI and the like, and don't forget industry specialization. You've seen Oracle pick up a number of industry plays and as digital transformation continues, you'll see more crossing of the industry streams because it's data. Now, the disruption isn't blatantly obvious in this market right now, other than SaaS clouds going after SAP and Oracle, and it's because these companies are deeply entrenched in their customer organizations and change is risky. But the cloud developer, the open source API trend, it could lead to disruptions, but I wouldn't expect that until the second half of this decade as cloud ecosystems really begin to evolve and take hold. Okay, well that's it for today. Remember, these Breaking Analysis episodes, they're all available as podcasts wherever you listen so please subscribe. I publish weekly on Wikibon.com and SiliconANGLE.com, so check that out, and please do comment on my LinkedIn posts. Don't forget, check out ETR.plus for all the survey action. Get in touch on Twitter, I'm @dvellante, or email me at David.Vellante@siliconangle.com. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching everybody. Be well, and we'll see you next time. (thoughtful electronic music)

>> Narrator: From theCUBE's studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey everybody, this is Dave Vellante and welcome to this episode of "CXO Insights." As you know, we've been grabbing the perspectives of leaders throughout this pandemic and assessing their tips for managing in a crisis and of course, managing in good times as well. But now, as we enter the post-isolation economy, we really want to look at not just how you manage through the crisis but how you manage beyond the crisis. And I'm really excited to have Chris Riley here. He's the newly minted Chief Revenue Officer at Automation Anywhere. Chris, my friend, how you doing? I hope you and the family are well. >> Thank you, David. I wish the same for you. I think getting by as most folks are, it's the new normal, we're all getting used to it but I'm happy to be here and happy to be at Automation Anywhere. >> Yeah, I want to talk about that in detail. Eddie Walsh calls it the new abnormal but so congratulations on the new role. I want to start with your career. I met you in 1987, which ironically was the same year I met Dave Donatelli, the same year I met Michigan I. and Saul Koi, talk about great timing. And then, you came into the industry at a time, really different time. It was, the IBM people don't remember this but IBM was the dominant player and you guys unseated them amazing 12-year career at EMC and then you kind of went to the .com boom. That was amazing. You relive that ride, did a stint at HP and really turned that business around and then came back to Dell, top go to market executive. One of the top in the industry that I know and now, of course at Automation Anywhere we're going to talk about. My first question to you is, a lot of changes have occurred since 1987. What has changed the most? Now we're talking diversity, we're talking all kinds of your different sales models. From your career looking back, what's changed the most? >> I think everything has changed and candidly for the better, Dave. You just led with one of the key areas in an area I'm deeply passionate about and that is diversity and inclusion and I think there's no stronger time, at least in our country's history where the inequalities that exist have been so exposed. So I view this as an opportunity, as I did at Dell to make a difference, lead from the front and make this a destination and a company whose culture really supports and drives diversity and inclusion. So I'd say that's one area, and I know it's very passionate for you as well. The others, it was a time before laptops, desktops. I think Ken Olsen once said, who would ever need a laptop in their home and boy, the world has changed. So I think some of the things though that haven't changed and this is why I'm so excited about Automation Anywhere. At the manual processes we have our workers doing and I think there is a real opportunity. I've lived through explosive growth at EMC, top company performing stock during the 90s, I get to see VMware firsthand. I've seen what's happened with ServiceNow and I believe this RPA space, as to you is in its infancy. It's seeing 30% compounded annual growth and we're just at the beginning and I think it's going to change the way people work and really lead to that digital transformation that so many of us have been talking about for the last decade. >> Yeah and you know kind of my position. Quick aside, I don't know if you saw the Netflix announcement this morning and I've been wondering as a small business, what can we do? What more can we do for inclusion and diversity? Netflix announced they're going to take 2% of their cash and put it into banks, financial institutions that support black causes and I just talked to our CFO. I said, look, why don't we take some of our cash, let's take 2% and stick it into banks, community banks. There's 30 million small businesses in the United States. If just 1% puts 10 grand in each, that's $3 billion that go into black community. So I'm going to start a mission and I just thought I'd share that 'cause I know it's a passion of yours. >> Yeah, and we all need to be in a position to provide equal opportunity for employment and that is reaching out into those communities and starting early on in creating the opportunities for advancement professionally, mentorship and just the path forward. And I'm excited to see what Netflix is doing. I'm sure you'll come up with the right answer for your company and I think all of us are searching, what's the right answer for our respective companies? >> Yeah, so now let's get into it. You're a month in and I want to talk about this project. I've learned a lot about not only RPA but about automation. I've just had a deep dive with your team and it really brought some things into focus. Guys, if you bring up the first slide, I want to get some thoughts on the table here. So this is a chart that sort of came into my focus with a friend of mine, Dave Moschello, who really big thinker on this stuff and he pointed out, this is data from the US Bureau of Labor and Statistics and the EU and it shows the lackluster productivity that's going on in the past decade. So you can see, we had the boost in the 80s and the 90s, we had this sort of productivity uptick from laptops but now, look what's happened since 2007. And the point that Moschello made on the right hand side is we have all these huge issues that we face, whether it's climate change, we have this massive debt, healthcare, an aging population, feeding everyone, et cetera, et cetera, et cetera, and his point was, there's no way we're going to be able to solve all these problems by throwing humans at the problem. So I've really begun to sort of think about this just in terms of machines and the roles that machines will play. I think overnight, Chris, we've gone from, wow, I'm afraid that machines are going to take my job to you can't operate if you're not digital. >> Yeah, well digital transformation is not a new term. I think it's meant something different each year for the last 10 years and I look at this as an opportunity. The World Economic Forum projected that IA and RPA will create 58 million new jobs. It's an astounding number. What COVID-19 has exposed is this work from home phenomenon that really exposes the risk of manual processes within the enterprise. So I think those two things combined is almost a perfect storm and I think what it'll do is accelerate the adoption of RPA and IPA. So something that might've taken years or decades to really be adopted in force, in this new normal, I think is going to be accelerated quite dramatically. >> So, the combination of your go to market execution, you managed complex sales motions before. Automation Anywhere obviously has some great product capabilities. Guys, I want to bring up the next slide and Chris, you might have seen this in some of the stuff that I wrote but this is data from ETR Enterprise technology research. They're a data partner of ours. Now it's clear that Automation Anywhere has the right product market fit and you can see on this chart, this is a methodology that we use. ETR goes out and they ask people, are you adopting a platform new? Are you increasing spending relative to last year? Are you flat, decreasing or replacing? And you can see here, there is zero churn in the Automation Anywhere base. And so obviously you got product market fit. Churn is the silent killer, obviously of SAS companies and so, you've picked a winner and I'm learning more about this. At first I thought the team office is quite large, I sized it. I actually think it's bigger than I originally thought. Chris, I thought this was going to be a winner-take-all type of market. I'm really rethinking that after, especially the deep dive I've had with your team in terms of how you guys go to market with an end-to-end sort of life cycle approach as opposed to sort of putting point products in. So I wonder if that narrative that I just laid out, resonates with you, is it sort of consistent with what you're seeing and then maybe some of the reasons why you joined Automation Anywhere? >> Yeah, I would say the most aggressive software growth that I've seen in the last decade or so, and two companies stand out for me. That's VMware and ServiceNow. They don't have a point product, they have a platform and that's what attracted me to Automation Anywhere is this platform approach. And Dave as you know, I've spent most of my career calling on the enterprise' strong relationships with those types of companies and they aren't looking to develop a point product solution and then cobble together lots of disparate islands of solutions. They're looking for a platform that can grow as they grow. They can extend from the back office to the front office but all centered around workforce, transformation, productivity and just as importantly, resiliency. And as we start to develop more and more capabilities that will be delivered through this platform approach, I think we're going to see explosive growth as the industry goes through its explosive growth. >> Well, I know your approach and your approach is to stay very close to customers. So as you were doing your due diligence on Automation Anywhere and as you enter your sort of first part of your 100-day journey here, I'm sure you've talked to a lot of customers. What are they telling you? What are the big takeaways right now that you're hearing? >> Yeah, so I think some of the data you pointed out with 4,000 customers in essence, zero churn, the opportunity to upsell those customers with more products and solutions clearly is there. New account acquisition has been a tremendous source of growth for the company in a strong professional services organization that actually is able to deliver the outcomes that our customers expect. From an enterprise perspective, I couldn't have come into a better situation with 4,000 customers, 50% of the fortune 500, 2 million bots deployed, those types of strategic relationships are going to be more and more critical as this company continues to accelerate its growth. Most of the RPA solutions really got in through the back office and candidly, really weren't even a component of an IT solution. Now, as you go to the front of the house, where you're looking at customer facing applications and worker productivity, these are CEO, CFO, COO and IT initiatives. So I really believe we're coming into our own, at the front of the house with senior executives that really want to create a better working environment for their employees and de-risk a lot of these manual processes that have existed for years. >> So I know why you chose Automation Anywhere. My question is, why did Automation Anywhere choose Chris Riley? I know your capabilities but obviously when somebody hires a executive like yourself, they say, "Hey, Chris, we want you to help us "get to the next level," but what does that mean? Are we talking about changes in the go to market? Are we talking about your ability to recruit and coach, to manage complex of sales motions? What is it that you want to bring to Automation Anywhere? >> I think it's all those, Dave. Having built a reputation throughout my 30 plus year career around a people-centric focus, a customer-centric focus and those two things kind of aren't interchangeable. When you look at customers, they put their faith and confidence in people and they put their faith and confidence in companies. And what I see here at Automation Anywhere is that the ability to kind of expand upon the great culture that the company already has but do it with someone whose role in a company at scale globally and can put a lot of the best practices and disciplines in place to do that 'cause it is difficult but also, how do we start to do larger, more complex deals and build relationships with the CIO, the CFO, the CEO? And I think a big reason why I'm here is, that experience in doing that, doing large complex multi-year opportunities but also being able to deliver upon the outcomes that we told our customers we could achieve and do that together with our customers and again we have a strong professional services organization and an incredible ecosystem of partners that have demonstrated year over year, the company's ability to actually deliver upon its promise. >> That was my next question to you, was the ecosystem. One of the big changes from when you started in this business, was it used to be just belly to belly, hardcore, direct sales, the importance and leverage that you get from a partner ecosystem. You point out VMware. In fact ServiceNow, it's interesting. When we first started covering ServiceNow, one of the things we said is we want to see as an indicator of success, the partner ecosystem evolve and then sure enough, it exploded with the SIs and all the kinds of developers. So maybe talk about AA's ecosystem, The Partner System. You obviously have a lot of experience there in your career, how do you see that as a leverage point? >> Yeah, it's huge. This market is far larger than we can cover with a direct sales organization and requires substantial services engagements that go well beyond the kind of professional services organization we want to build out organically in the company. So when you look at that, the company today has 1,900 partners. The global systems integrators are key, especially those with VPO type practices, the regional SIs and candidly, the regional VARs who've built out a strong service malpractice, a strong VMware practice and have the professional services capabilities to do some of these complex automation or automation type work that have also built the trust and confidence of their customers. So, in partnership with these types of companies, we believe we can expand our reach. We believe we can offer a more comprehensive outcome and solution to our customers and we, what I'm going to be looking at is, how do we enhance our channel programs to be the kind of company that the channel partners want to engage with, built upon the reputation of the company, the leadership position we have in the technology and also our willingness to go after this space together. >> So I got to go but last question is, what can you share with us about your 100-day plan? Where are you going to focus? >> On the people. There is a strong culture here, there's incredible sales talent and there's talent throughout the organization. I think Dave, you've seen for me over the years, a clarity of our mission, keep things simple and try and drive a repetitive process to deliver results. I'm very accountability focused. So I think what I'm going to look to assess is where the organization is today, how to get more out of the great talent we have, build stronger and deeper relationships with our customers and really scale and grow through our ecosystem of channel partners. >> Well, Chris, I'm super excited for you. A great hire by Automation Anywhere obviously got my attention. I think it'll get the industry's as well. Best of luck, and of course we'll be watching. >> Good, always great to see you, Dave, take care. >> Yeah, ditto, thanks so much for coming on and thank you for watching everybody. Keep it here because this month we're going to be really digging into the ETR data we've been reporting on that horse race between Automation Anywhere and UI Path. The ETR data is in the field and we'll be reporting on that. So look for that. This is Dave Vellante for theCUBE and we'll see you next time. (gentle music)

>> from Austin, Texas. It's Theo Cube, covering your storage. Accelerate 2019. Brought to you by pure storage. >> Welcome to Austin, Texas. I'm Lisa Martin at Pure Accelerate 2019. This is the fourth pure accelerate. I'm here with my co host, David. Dante and David are pleased to be welcoming back to the Cube, the chairman and CEO of Pier storage. Charlie Giancarlo. Charlie, Welcome back to the Cube. >> Thank you. Such a pleasure to be here >> already. Getting loud on the keynote. Just rapping about 3000 folks here. Standing room only. We just came from the keynote. Something symbolic. Besides, the location of this event is that you are just about to celebrate the 10th anniversary of pure storage >> of our founding. October 1st. >> Yes, just around the corner. Tremendous innovation. As you say it. Overnight success in 10 years delivering 10 X and prevents us a little bit of a preview about what you shared in the Kino. What's to come in the next 10 years? >> Exactly right. It is wonderful to be able to sell. They celebrated birthday and able to talk about what you've delivered over the 1st 10 years. But it also gave us the opportunity to really say Okay, what's the second decade going to be about? What is it gonna be like? And way were planning not only for this, but for the year that we were gonna put in place of development. We said, Well, you know, we've brought a lot of things to storage and to the storage array. We made it much simpler. We made it upgradeable, non disruptive Lee, meaning that customers would have a continuously new product in their environment. Andi started to bring it into the cloud. And we said, You know, for our second decade, we want to transform the entire storage experience. We don't want it to be about boxes and a raise. We wanted to be about a storage system for the entire enterprise. That's multi protocol, multi cloud, multi tearing or what we call storage classes and entirely automated so that when an application calls for storage, service is it's delivered automatically without humans getting involved. That is completely as a service consumed as a service, delivered as a service entirely automated in the back end. So this is the goal that we have for our second decade. We think we're going to deliver it over the next several years. But of course, for us to go down the entire customer journey is a great mission for us for next decade. >> So in terms of, you know, I don't want to make it sound like the first decade was easy because you were really the only all flash array company. Thio reach escape velocity and many. But at the same time you caught DMC flat footed. You drove a truck through their install base and obviously the rest is history. I feel like the main job of the CEO is too. Is Tam expansion, right? You're focused on that. There's a I there's new workloads. There's the cloud, there's multi cloud. And in your entering new territory now, yes, maybe no. Guys like eight of us, they're not flat footed, right? You've got Europe against Google and Cisco and Microsoft in the multi cloud arena. But you're a specialist on one. If you could talk about your vision in terms of tam expansion, >> thank you very much for that question. The TAM expansion really is following where solid state takes us. You know, we've gone from a world that was where believe it or not, most computers still had mechanical systems operating them. It's sort of like having a mechanical calculator rather than Elektronik calculator, right? We had mechanical discs in our computers literally spinning rust, right? And it's only been in the last decade where a semiconductor, you know, where solid state has taken the place of that called Flash, right? Well, as that continues to get less expensive, we now can bring not only flash performance into disc economics, but more importantly, now we can finally have modern software that is driving the need for having greater flexibility with our data. As data grows it. Now we say it has gravity. That is, it gets heavy. It gets hard to manage hard, hard to move between different environments. And now a lot of infrastructure operators are spending much more time managing their data, managing the storage systems for their data than they are managing anything else in the data center environment. We want to eliminate all that. We want to automate all of that, you know, on the theme of decades. Two decades ago, every application had its own individual communication stack. There were dozens of different protocols and a dozen different networks in every company. One decade ago, every application had its own custom hardware stack and custom operating system stack. Well, today there's one network. It's called the Internet. Today, everything, every application, every server is virtualized, allowing mobility. And yet storage is still static way want this decade a bit to be about making storage and data dynamic and really responsive to the needs of the application environment? >> So >> what if you >> could compare this opportunity to some other mega trends that you've been part of? You were there in the early days of wireless when nobody wanted to buy wireless saw the I P changeup. People think the minicomputer was killed by the microprocessor in apart. It was, but it was I p. It was destroyed. Many computer everybody had their own networks. >> Where do you >> put so that the trend that you're after? How do you compare and what are your expectations? >> I think it's an analogous trend, and it's you know, this long term trend of vertical, whether it's vertical industries or vertical technology's going to becoming horizontal. So let's just give a couple of examples again. Networking was tightly tied to the application, and every application had its own network and its own set of protocols right that was vertically tied. Now networking is horizontal. It's all I P. Right again, we'll go back to applications. Applications had a vertical stack. The entire stack hardware and software was tied to specific application today that's been made virtualized and therefore horizontal. You could move applications among different servers. Storage is still vertical. It's still tied very tightly to the to the rack. And there are a lot of good reasons for that. You needed a high speed interface. High speed networking didn't exist. Disks were slow. They could only support one application at a time, with solid state that no longer exists. So now weaken, make storage free. We can make it ah, horizontal layer rather than tightly tied to any individual application. And that's what the next decades gonna be about >> Business leaders today, I feel there's so much more open than when we started in this. In this industry, where you know the famous line about Ken Olsen, Unix is snake oil and those that you old enough to remember that business leaders today they recognize the trend is your friend right. So gentleman from AWS at 88% of the customers and a gardener survey said their cloud first, but 86% are still spending on Prem. Right In the old days, when I said I'll keep it on Prime and Amazon so we'll keep it in the cloud. And yet you guys, customers, they're sort of forcing you to come together. Yes, I wonder if you could talk about that dynamic and specifically your cloud strategy? >> Absolutely So our cloud strategy is really quite simple. We want to make the cloud and every cloud appear to an application developer to be the same as it is on Prem. With all the advanced service is the advanced applications. It interfaces the same AP eyes because largely applications have been especially primary to your applications have been developed for with on Prem interfaces and on Prem service is the cloud, while wonderful from the standpoint of being able to be dynamic, does not have sophisticated service is for data. And so by making it appear to be the same to the application into the developer on premise in the cloud, it just makes the entire system or dynamic it allows for for companies to more easily move applications to the cloud or to another cloud or back on Prem. And it changes the dynamic and the decision making of enterprises not to. How much work do we have to do to move something to the cloud? But where is it best placed economically and based on service is we take it out of being a technology decision and make it more of an economic decision. >> Why were you in a unique position relative to your competition? I mean, why can't deli emcee or net app for IBM sort of take that same AP I economy mentality and drive it through their portfolio and get to market fast? And why is your pure unique? >> Well, for one, it takes investment will invest 18% of revenue in R and D this year. Nearly all of our competitors are spending less than 5% there, really viewing storage as an old antiquated market, not as a high tech market. They're reaping, if you will, rather than selling on re really view storage as next frontier off great innovation and our competitors largely don't see that. >> Let's talk about a little bit digging into the evolution of your Amazon Web service is relationship. We talked about that a minute ago when you guys talked about Announce Cloud Block store. There's dozens of customers in beta. Are they viewing it as this bridge, the hybrid cloud? And what are some of the benefits? If you could talk about it from any of those customers that are abated, what are they? What are you starting to see so far? That's really exciting, that this is the delivering or will be the modern data experience Way had >> a great speaker from eight of us onstage today, and I think he summed it up really well. At the end of his talk, he said that now the migration to cloud is easy because pure has done all the heavy listed lifting for you to take your enterprise applications and move them into the cloud. I mean, I think all the cloud players recognize that while they have provided some great capabilities, especially for Dev ops, that the level of of sophistication and the completion of service is for things like very complex enterprise. APS have not been fully accomplished yet, and so they recognize that experts like pure who have been delivering against enterprise primary tier applications for a long time have a lot to add in terms of the sophistication of our product in their environment. I think what they also recognize is that it's hard for customers to rewrite their applications to a completely different set of data. AP eyes and mind. You'd not only does, for example, he ws have different AP eyes in their cloud than customers have on Prem. But Azure has different AP eyes and then Amazon. Google has yet different, and so for a customer to write their application three or four times is really beyond what is in the interest of most customers. We have taken all that heavy lifting and enabled a customer to take their applications. They've already written, whether on cloud or in the print on Prem, and to move it in those other environments with much less investment. >> And let me let me try to explain, as I understand it, and make sure I got a right is essentially, What you've done is take the pure software stack and management framework and then using AWS Service's E C two High Priority E. C two's front ended on s3 cheap Best three created block storage. That's higher availability, probably faster rights, right? Three Real Boat reads and writes, are probably comparable with the pure experience. That's right on, Baby. You got to pay a little bit more for that. But you get you get better availability and there's value there. >> Actually, the beautiful thing is that we create an environment in AWS where it's faster, that is, the storage is faster. That it has a very higher reliability has. All of the service is that customers want tohave such as snapshots, replication and encryption. And the entire bill between what they pay for pure and what they pay for eight of us is no more than what they would pay for A W S on its own. For those storage service is >> because you're using cheaper s3. To me, this is brilliant. Eight others is happy because they're selling E. C. To an s3. You're happy because you're making money on your software. Stock was happy because they get the pure experience in the cloud. It's exactly actually quite innovative. >> It's almost matching >> quickly. Talk about Nan pricing. I know that was an issue this quarter. It hurt revenues a little bit on the stock drop, but then when you saw everybody else announced, the stock went back up because you're was 28% growth to everybody else's minus 16 minus 21 0 was the best. But to me, lower Nan pricing is a is an opportunity for you. It's a tailwind to go eat into more of the spinning dis market. Do you see it that way? >> No. Absolutely right. I mean, when it all hits in 1/4 it could be a challenge. But over time, the consistent and fast decrease in Nan pricing simply means that we will eventually get to solid state for all storage. I have no doubt about that. The days of disk are certainly numbered, and what that does is open up the entire storage market. Today, disc is only by terabytes. 15% of the storage market flashes only 15%. So it eventually we have 85% of storage market still to go after, and we believe that one day that will be all solid state. >> I want to ask you about the macro you guys said on the call. You really not concerned about the macro. You don't win on pricing. You don't lose on pricing that even a downturn. You guys feel like you can gain share. And I would agree with that. By the way, of course, we don't want a downturn. Got it? But if you don't have a downturn, But what are your thoughts on your ability to compete independent of Of of the macro. >> Right. So, you know, we have from day one, obviously, we had no sales when we got started. Right? So every sale we've made has always been a competitive sale. There was always someone that we had to displace, right? Some some incumbent. And that speaks to the type on the quality of the sales and marketing team that we have, right? Not only they aggressive, but you know, in the parlance of the industry, they're hunters. I think a lot of companies, once you become more mature, you develop more farmers in your in your sales force, right? Managing the customer account, managing the install base and so forth. And when the macro is flat or down, you suffer. You know, from you suffer overall from that because you haven't been used to expanding your footprint. In our case, I think even when the Makri is down not that we won't be hurt by it. We will. But because we have a team of hunters, we continue to gain market share away. Will >> you >> change it? It's hard to predict, right, But But Frank's Lupin once told me, Hey, if things change, I can turn this on. And we could become an a T. M when he was running the service. Now, right now, you're going for growth in the street rewards growth. You got a three plus X revenue multiple. Everybody else is lucky to get one X so that they're rewarding you for growth. Do you feel like if things change that you might turn those knobs a little bit? Or is it you know, >> So I don't expect things to change for quite some time, but, you know, we produce 70% gross margin in the last quarter, right? I mean, most of our competitors are in the fifties, right? If not, if not the forties. So clearly growth costs money in this business, right? You have to build your sales force before they start producing for you. You have to invest in marketing before they start producing. And because of our high focus around R and D right, which is all about new products again, your front ending your costs before the before the growth actually comes in. So now we're gonna continue to focus on growth. And as long as we believe that the medium to long term growth for us is in the thirties, you know, high twenties, thirties, even maybe even forties, we're going to continue to operate profitably but relatively lower profit once growth slows down. Yeah. I mean, it will all start flowing. >> Reassess it at that time. At least our data and the data shows that pure is in a position from a spending intention standpoint to continue to gain share. We don't see any change to that in the next several quarters. >> Last question for you, Charlie. We got to talk about a I we talked about at every conference. When we're looking at pure and customer conversations, it's about data data. Is oil lifeblood gold, currency, whatever you wanna call it? How? What is that conversation that that tape, urine and video have together in customers about? How can data ignite our workloads. Help companies identify new products. New service is deliver more automation. This is >> probably one of my favorite topics. When I'm talking to customers is how to make data actually useful. Not so much the, you know, the bits and bytes of how do you actually store it? But you know, what does it mean to them is a business but also to their customers because a lot of times they're using it for overall customer benefit. And the great part of that conversation and whether it's us or in video or both of us together, is we both use it for our to improve our business and our customers lives as well. You know, we talk today about how we have 15 petabytes of operational data from our customers, a raise, right, how they're performing. And we analyze that on a on an hour by hour basis toe look to see. Is the customer getting to the point where they need where they didn't need to modify how they're operating or where they need to upgrade, or where they need to add or even reduce more capacity so that they don't fall? You know they don't trip over things that will get their business in trouble. So it And now we even allow the customer to analyze their business. And do what if scenario plant planning to say, Well, if I'm going to double the amount of customer transactions I have, you know, what will that mean from an infrastructure Sandpoint? You know? Well, I need to change your upgrade. So, you know, this has been great fun because we are in the same boat as our customers, depending on a I to improve our our mutual customers experience. But >> this conversation is best. Very insightful. Charlie, Thank you for joining David Me on the Cube today. Again. Happy 10th anniversary. Here we look forward to the next two days >> and happy 10th year to you. >> Thanks very much. >> That's right for day, Volante. I'm Lisa Martin. You're watching the Cube from pure accelerate. 19

>> Live from Las Vegas, it's theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight, along with my co-host John Furrier. We're joined by Ali Ghodsi, he is the CEO of Databricks, thank you so much for coming on, for returning to theCUBE. You're a CUBE veteran. >> Yes, thank you for having me. >> So I want to pick up on something that you said up on the main stage, and that is that every enterprise on the planet wants to add AI capabilities, but the hardest part of AI is not AI, it's the data. >> Yeah. >> Can you riff on that a little bit for our viewers? Elaborate? >> Yeah, actually, the interesting part is that, if you look at the company that succeeded with AI, the actual AI algorithms they're using, are actually algorithms from the 70s, you know, they're actually developed in the 70s, that's 50 years ago. So then how come they're succeeding now? When actually the same algorithms weren't working in the 70s, so people gave up on them. Like, these things called neural nets, right? Now they're en vogue and they're, you know, super successful. The reason is you have to apply orders of magnitude more data. If you feed those algorithms that we thought were broken orders of magnitude more data, you actually get great results, but that's actually hard. You know, dealing with petabyte scale data and cleaning it, making sure that it's actually the right data for the task at hand is not easy. So that's the part that people are struggling with. >> I saw you up on stage, I'm like ah, Ali's here, Databricks is here, that's awesome. Psyched that you stopped by theCUBE. Been a while. I wanted to get a quick update, 'cause you guys have been on a tear, doing some great work at Cal, we were just told before we came on camera. But what are you doing here? What's the, is there any announcements or news with Informatica? What's the story? >> Yeah, it's, we're doing partnership around Delta Lake, which is our next generation engine that we built, so we're super excited about that. It integrates with all of the Informatica platform. So their ingestion tools, their transformation tools, and the catalog that they also have. So we think together, this can actually really help enterprises make that transition into the AI era. >> So you know, we've been followers, our 10th year, so remember when we were in the cloud era office of Mike Olsen and Amr Awadallah when we first started and now, Hadoop movement started, and then the cloud came along. Right when you guys started your company, the cloud growth took off. You guys were instrumental in changing the equation in dealing with data, data lakes, whatever they're calling it back then. So now, data, holistically, is a systems architecture. On premise it's a huge challenge, cloud native, well no real challenge, people love that. Data feeds AI, lot of risk taking, lot of reward. We're seeing the SaaS business explode, Zoom communications. The list goes on and on. Do you know, enterprise that's trying to be SAS is hard. So you can't just take data from an enterprise and make it SaaS-ified. You really got to think differently. What are you guys doing? How have you guys evolved and vectored into that challenge, because this is where your core value proposition initially started change. Take us through that Databricks story and how you're solving that problem today. >> Yeah, it's a great question. Really what happened is that people started collecting a lot of our data about a decade ago. And the promise was, you can do great things with this. There are all these aspirational use cases around machine learning, real time, it's going to be amazing. Right? So people started collecting it. They started storing one petabytes, two petabytes, and they kept going back to their boss and saying this project is real successful I now have five petabytes in it. But at some point the business said, okay that's great but what can you do with it? What business problems are you actually addressing? What are you solving? And so, in the last couple years there's been a push towards let's prove the value of these data lakes. And actually, many of these projects are falling short. Many are failing. And the reason is, people have just been dumping this data into data lakes without thinking about, the structure, the quality, how it's going to be used. The use cases have been an afterthought. So the number one thing in the top of mind for everyone right now is how do we make these data lakes that we have successful so we can prove some business value to our management? Towards this, this is the main problem that we're focusing on. Towards this, we built something called Delta Lake. It's something you situate on top of your data lake. And what it does is it increases the quality, the reliability, the performance, and the scale of your data lake. >> (John) So it's like a filter. >> Yeah. >> The cream rises to the top. >> (Ari) Exactly. >> Let's the sludge, the data swamp stay below the clean water, if you will. >> Exactly actually you nailed it. So basically, we look at the data as it comes in, filter as you said, and then look at, if there's any quality issues we then put it back in the data lake. It's fine, it can stay there. We'll figure out how to get value out of it later. But if it makes it into the Delta Lake, it will have high quality. Right? So that's great. And since we're anyway already looking at all the data as it's coming in, we might as well also store a lot of inducees and a lot of things that let us performance optimize it later on. So that, later, when people are actually trying to use that data they get really high performance, they get really good quality. And we also added asset transactions to it so that now you're also getting all those transactional use cases working on your existing data lake. >> I saw, at my daughter's graduation in Cal Berkley this weekend and yesterday, people around with Databricks backpacks. Very popular in academic. You guys got the young generation coming in. What's the update on the company? How many employees? What's the traction? Give us a quick business update. >> Yeah we're about 800 employees now. About 100 people in Europe, I would say, and maybe 40-50 people in Asiapac. We're expanding the ME and the Asia business. >> (John) Growth mode. >> Yeah, growth mode. So it's expanding as fast as possible. I mean, I actually, as a CEO, I try to always, slow the hiring down to make sure that we keep the quality bars. So that's actually top of mind for me. But yeah we're-- >> (John) You did Delta Lake on that one. >> Yeah (laughing) >> Exactly. Yeah and we're super excited about working with these universities. We get a lot of graduate students from top universities-- >> And Cal had the first ever class in college of data analytics, what was that? Data analytics are the first inagaural class graduated. Shows how early it is. >> Yeah, yeah, yeah. And actually used Databricks, the community edition, for a class of over a thousand students at Cal used the platform. So they're going to be trained in data science as they come out. >> So I want to ask about that because as you said you're trying to slow down the hiring to make sure that you are maintaining a high bar for your new hires. But yet, I'm sure there's a huge demand because you are in growth mode. So what are you doing? You said you're working with universities to make sure that the next generation is trained up and is capable of performing at Databricks. So tell us more about those efforts. >> Yeah I mean, so, obviously university recruiting is big for us. Cal, I think Databricks has the longest line of all the companies that come there on the career fair day. So, we work very closely with these universities. I think, next generation, as they come out, this generation that's coming out today actually is data science trained. So it's a big difference. There is a huge skills gap out there. Every big enterprise you talk tells you my biggest problem is actually, I don't have skilled people. Can you help me hire people? I say, hey we're not in the recruiting business. But, the good news is, if you look at the universities, they're all training thousands and thousands of data scientists every year now. I can tell you just at Cal, because, I happpen to be on the faculty there, is, almost every applicant now, to grad school, wants to do something AI related. Which has actually led to, if you look at all the programs in universities today, people used to do networking, professors used to do networking, say we do intelligent networks. People who do databases say, we do intelligent databases. People who do systems research say, hey we do intelligent systems, right? So what that means is, in a couple years you'll have lots of students coming out and these companies, that are now struggling hiring, then will be able to hire this talent and will actually succeed better with these AI projects. >> As they say in Berkley, nothing like a good revolution once in a while. AI is kind of changing everyone over. I got to ask you for the young kids out there, and parents who have kids either in elementary school or high school, everyone is trying to figure out, and there's no yet clear playbook, we're starting to see first generation training, but is there a skill set, because there's a range in surface area, you got hardcore coding to ethics, and everything in between from visualization, multiple dimensions of opportunities. What skills do you that people could hone or tweak that may not be on a curriculum that they could get, or pieces of different curriculums in school that would be a good foundation for folks learning and wanting to jump in to data and data value, whether it's coding to ethics? >> Yeah, just looking at my own background and seeing how, what I got to learn in school, the thing that was lacking, compared to what's needed today, is statistics. Understanding of statistics, statistical knowledge, That I think, it's going to be pervasive. So I think, 10, 15 years from now, no matter which field you're in, actually whatever job you have, you have to have some basic level of statistical understanding 'cause the systems you're working with will be, they'll be spitting out statistics and numbers and you need to understand what is false positives, what is this, what is the sample, what is that? What do these things mean? So that's one thing that's definitely missing and actually it's coming, that's one. The second is computing will continue being important. So, in the intersection of those two is, I think a lot of those jobs. >> In all fields, we were talking about earlier, biology, everything's intersecting, biochemistry to whatever right? >> (Ali) Yeah. >> I got to ask you about, well I'm a little old school, I'm 53 years old but I remember when I broke into the business coding, I used to walk into departments, they were called DP, data processing. So we're getting into the data processing world now, you've got statistics, you've got pipeline, these are data concepts. So I got to ask you as companies that are in the enterprise may be slower to move to the cutting edge like you guys are, they got to figure out where to store the data. So can you share your opinion or view on how customers are thinking and how they maybe should be architecting data on premise, in the cloud. Certainly cloud's great, if you're getting cloud native for pure SAS, and born in the cloud like a start-up. But if you're a large enterprise, and you want to be SAS-like, to have all that benefit, take the risk with the reward of being agile, you got to have data because if you don't the data into the machine learning or AI, you're not going to have good AI. So you need to get that data feeding in fast. And if it's constrained with regulation compliance you're screwed. So what's your view on this? Where should it be stored? What's your opinion? >> Yeah, we've had the same opinion for five, six years, right? Which is the data belongs in the cloud. Don't try to do this yourself. Don't try to do this on prem. Don't store it in, at Duke, it's not built for this. Store it in the cloud. In the cloud, first of all, you get a lot of security benefits that the cloud vendors are already working on. So that's one good thing about it. Second, you get it, it's realiable. You get the 10, 11 lines of availability, so that's great, you get that. Start collecting data there. Another reason you want to do it in the cloud is that a lot of the data sets that you need to actually get good quality results, are available in the cloud. Often times what happens with AI is, you build a predictive model, but actually, it's terrible. It didn't work well. So you go back, and then the main trick, the first tricks you use to increase the quality is actually augmenting that data with other data sets. You might purchase those data sets from other vendors. You don't want to be shipping hard drives around or, you know, getting that into your data center. Those will be available in the cloud, so you can augment that data. So we're big fans of storing your data in data lakes, in the cloud. We obviously believe that you need to make that data high quality and reliable. With that we believe the Delta Lake platform, open-source project that we created is a great vehicle for that. But I think moving to the cloud is the number one thing. >> (John) And hybrid works with that if you need to have something on premise? >> In my opinion the two worlds are so different, that it's hard. You hear a lot of vendors that say we're the hybrid solution that works on both and so on. But the two models are so different, fundamentally, that it's hard to actually make them work well. I have not yet seen a customer yet or enterprise. You see a lot of offerings, where people say hybrid is the way. Of course, a lot of on prem vendors are now saying, hey, we're the hybrid solution. I haven't actually seen that be successful to be frank. Maybe someone will crack that nut but-- >> I think it's an operational question to see who can make it work. Ali, congratulations on all your success. Great to see you. >> Yeah it's been great having you on the show. >> Thank you so much for having me. >> You are watching theCUBE, Informatica 2019. I'm Rebecca Knight, for John Furrier, stay tuned.

>> Narrator: Live, from Las Vegas, it's The Cube, covering IBM Think 2018. Brought to you by IBM. >> Hello, everyone and welcome back to our day two of coverage here in Las Vegas, where IBM Think 2018's The Cube's three days of wall-to-wall coverage day two. Yesterday, we had kick-off, kind of partner day. Today's really the kick-off of the event. CEO of IBM up on stage for the keynote. I'm John Furrier with Dave Vellante. Dave, we're doing seven years or so plus all these six shows coming down to one for IBM Think. It's a packed house; you can't even get through the hallways. Looks like they need to go to Sands Convention Center. >> Dave: (laughs) or Moscone. >> Or Moscone, or somewhere bigger, they need a bigger boat, but the keynote kicked off, Ginni Rometty was up there. Interesting, putting smart to work, quantam, blockchain, AI data and she kind of laid out the cloud strategy, you know, using data in public cloud and private. It's clear where they're going with the cloud. Your analysis of the keynote, what's your thoughts? >> Well, first of all, John, as viewers know, I mean, I'm a big fan if Ginni Rometty. I think she's been overly criticized, but I think she's a great presenter. When I compare Ginni's presentation skills with some of the other CEOs in the industry, I think she's far superior. She connects with the audience, she looks great, she's really cogent, she's well prepared, so, I really like her as a presenter and as an executive, and, you know, another women in tech, you know we love that. Yes, you're right, putting smarter to work was her theme. She's talkin' about 30 to 40,000 people at the event. There's too many people to count I guess. You can't really figure that out, and, so, it's big, it's packed. She also did a theater in the round which was different. I noticed last year ServiceNow did that. I really like that style, so that was kind of an interesting thing. Ginni talked about three exponential growth areas. So, I'll lay 'em out and then, we can talk about it. She said they come every 25 years. The first was Moore's law, and we all know what that is, and the second was Metcalfe's law, the value of the network increases exponentially if the nodes in network increase, and then, the third, which is upon us now, is data plus AI. Her supposition was that is going to usher in a next era of incremental growth, because you're going to out-learn the competition, and she used this term of incumbent disruptors, and I heard that and went okay, hold on, (Dave laughs) 'cause I don't see it that way. >> Yeah. >> I don't see the incumbents as the disruptors. So, that was my first reaction, and then, she brought up three customers, Verizon, and I'm like, "Verizon? "A big telco is a disruptor, come on! "They're gettin' a disruptor by over the top.", but the CEO came on, Lowell McAdam, talkin' about 5G, so we'll talk about that, and then, Maersk, IBM has a joint venture with Maersk, so, Michael White came up, he's the CEO of that. Now, Maersk is using blockchain, and Maersk we all know is the container company and they're attacking inefficiencies with blockchain, so I thought that was actually a really good example, and then, Royal Bank of Canada, RBC, came up. You know, banking, to me, is an industry that has not been disrupted yet, and, so, I, again, was initially negative toward this idea of incumbent disruptors, 'cause I don't think the incumbents are disruptors, and we'll talk about why I think that, but I thought IBM did a pretty good job of showing how incumbents can actually take AI and blockchain and, at least, defend against the disruptors. >> I mean, it's clear to me that she's obviously playing to the crowd with the digital debt transformation. I mean, we talk about these traditional companies, they need to transform, and she brings up Moore's law and Metcalfe's law kind of to take a view of the past, but to look forward, she's kind of saying, "Lookit, Moore's law make things smaller, faster, "cheaper, doubling every six months." That's just on the, I mean, this applies to IoT, quantum makes everything else. Metcalfe's law I think is very relevant, 'cause if you look at blockchains about decentralized internet, you're talkin' about decentralized applications, that's where blockchain will play the major enablement there, that's about network effects, so you bring network effects in with Metcalfe's law, Moore's law on the equipments on the hardware side, I like that, so, that worked for me. The disruptors, I think it's more of overplaying her hand on that, because I just haven't seen any evidence of any incumbents truly disrupting themselves. So, maybe you can talk with Microsoft, IBM's trying to transform, but at the end of the day, they got to look back and learn from the internet era. If you don't jump on these next waves, you could be driftwood, right? So, you got to surf the new waves, and I think that's what I heard her say is IBM is putting data at the center of the value proposition using AI as a front end for that, make it smarter, and then, using blockchain as an infrastructure and protocol level opportunity to take the IBM software and data plane and wrap 'em together. So, if you look at it, you got data at the center, blockchain on one side, and AI on the other, it's the innovation sandwich. That, for me, works for me, now, let's unpack that. How real is it, and that's going to be what we're going to talk about, and I think that's a good strategy. All the elements are in play. >> Well, I think the other piece of that sandwich, maybe it's the dressing on top, is the cloud, 'cause you have to have scale and network effects in order to achieve that innovation. I just want to mention, she talked about three other things that you are going to do as a customer. You're going to, one, leverage digital platforms, you're going to, two, embed learning in, virtually, every process that you do, and, three, you're going to empower humans. So, she put forth this idea of augmented intelligence, and, as I predicted yesterday, she, unlike Larry Olsen, she doesn't come right out and slam her competition, she does it in a classy way. She said, quote, "IBM is not "in conflict with your business." In other words, we're not taking your data and then, remonetizing it at the back end. That's a big deal, IBM makes a lot of noise about that. So, it's really augmenting humans, not in conflict with your business, and bringing advanced security to things like blockchain, >> Yeah. >> and cloud, and AI. >> I like her term security to the core, I like that, but that kind of gives the impression that's core to all things, but if you look at the megatrends that are impacting the incumbents and the people trying to do digital transformation, as well as the new startups, Dave, that are trying to get a new position in the landscape is clear. You got blockchain, you got decentralized apps, you got AI, but the data's critical, and she mentioned some cool things I like with the cloud which was she's saying, "Lookit, we'll make "the data a really big thing for you. "If you want it in public cloud, "you can have it in private cloud." So, she's looking at cloud as much more of a hybrid approach on private, kind of hinting at the GDPR problem that we know's out there. So, if you want to move your data around, that's a critical asset. Also, if you look at what's going on in the news today, these days, is Facebook is getting slammed because how they were hacked with the election, and other weaponization of data, this is a big deal for companies, and I think if IBM can play that card to leverage the data and have the confidence of the companies that they serve to say, "Lookit, data's got to be owned by you, "but has to be managed in a way that's dynamic, "whether it's a GDPR or some other regulatory issue.", and, believe me, blockchain's going to have some. So, you know, they could come out and get in the front of this new wave, and I think that's a good play. So, it wasn't just a recycled cloud show, it wasn't just AI Watson, I like how she put it together. >> So, just touching on a thing, you mentioned Facebook. So she talked about Moore's law ushering in this era of back office productivity. She didn't mention Wintel; I think it's still, probably, too painful for IBM to think about that. Metcalfe's law, she said ushered in, sort of, the Facebook era. I think that's fair, the network effect of Facebook, and then, she said, "Hopefully, you know, "they'll call this Watson's law." I don't know if that's going to happen, but that notion of, >> Wishful thinking. >> hey, hey, you got to be power of positive thinking, but that notion of exponential learning. I want to talk about cloud for a minute. You and I had some interesting debates yesterday in our open about cloud. Oracle announced its earnings yesterday, cloud growth 30%. I see Oracle and IBM as very similar in their cloud strategies; both companies would vehemently disagree with that, >> Yeah. >> but I think they are very similar in that sense. The street didn't like it, because Oracle cloud only grew at 30%, stock's down, okay, great, but, to me, IBM and Oracle are similar in that they're basically cloudifying their business. They're allowing their clients to onboard customers to the cloud, putting their applications portfolios, their SAS products, their middleware into the cloud, IBM putting mainframe class stuff in the cloud, they're putting power into the cloud, storage into the cloud, pretty much everything into the cloud if you want it. Now, that's not easy to do >> Yeah. >> if you've got, you know, legacy businesses, obviously, AWS has a blank sheet of paper, that was kind of your point yesterday, >> Yeah, yeah. >> but I like the differentiation that I see from the companies like IBM and Oracle, and there really aren't many others like that. >> Yeah. I mean, my point yesterday was the definition of cloud has been totally mangled, right? Like, it's different, if you're Amazon, they have a slew of services, they have more services than anyone else on the planet, and they have more people using those services, so, by that standard, Amazon is clearly kicking everyone's butt, but that's just their perspective. If you look at IBM, their services are applications, same with Oracle. So, if you look at what IBM's doing is they're taking the same approach. Services and applications are going to be IBM's view of the cloud, but IBM's taking a multicloud approach, and I think that's different, and, when you put the data as the central component of the architecture, you're basically saying, "I'm going to look "at the cloud as more of a commodity layer. "I'll let the customers decide which cloud to use.", and that's a better strategy, now, it's hard to do multicloud, so maybe they're buying some time, but I think that's a good, solid strategy to take if they're not going to be trying to push their own cloud as 100%, because not all customers will sole source cloud unless there's functionality that that cloud does. For instance, Amazon is winning the public sector business like it's nobody's business, because they have the only cloud that has the ability to do classified and non-classified cloud. Nobody else has it, so, from a log speck standpoint, they're winning everything and from the DOD, CIA, and government. What IBM has to do is go into customer requirement saying, "We're the only company that can provide this." That's a unique opportunity for IBM. I think that's a winning approach rather than going on a frontal arms race of services with Amazon, and that's what all the big guys are doing. Microsoft, Oracle, IBM are not taking on Amazon directly, because they're going to have to match feature for feature, and then, Amazon wins that game every time. >> So, I want to go back to something Sam Palmisano said when he was CEO of IBM in 2012 on his way out. HP was the hot company, Hurd was running the company, and he was asked, "Do you worry about HP?" He said, "I don't worry about HP, "'cause they don't invest in R&D. "I worry about Oracle, 'cause they invest in R&D.", and, again, what I like about Oracle and IBM, they both invest in R&D, IBM even, you know, core stuff around blockchain, certainly quantum computing and the like. So, I think that is a very positive dynamic for both of those companies. >> Well, I mean, IBM's R&D is a secret weapon, I think, for them; they don't overplay that much. They do talk about it, but we look at what blockchain potentially could be, and I think, you know, IBM's certainly doing the messaging on blockchain. It still has a bunch of ads on T.V., and they're trying to make that a kind of a global brand, but blockchain speaks to a new infrastructure, right? It's not just distributed computing, it's decentralized computing, and we were saying on the Cube and we've been reporting there is a new wave of software developers coming on the market that are going to be writing decentralized applications for token economics. The notion of tokens isn't about ICOs and those scams, although there's a lot of those going on. The notion of token economics fit with a mobile cloud decentralized architecture whether it's IoT, or end users, or applications, token economics is going to change the impact in efficiencies up and down the stat. So, to me, the developer community that's rushing into the market on the decentralized applications will be a major opportunity, but you got to nail the blockchain and that tech is just a moving train from a protocol standpoint to an infrastructure. So, to me, I like what IBM's doing with blockchain. I think that's going to be an opportunity to move the ball down the field. >> So, the exponential innovation formula, in my view of the next ten years, is going to, and you nailed it, going to combine data with artificial intelligence, or machine intelligence, and cloud economics, and there is a set of digital services emerging. >> Well, cloud and token economics, both, it's two. >> But, so, yes, but, so, and that's part of it, but there's a set of digital services emerging in this fabric, and they're not bespoke services, they're part of this integrated fabric. The extent to which people leverage those services, those digital services, to create new business models is going to determine success or failure. Data, at the core, is critical. >> Yeah, yeah. >> I think you're right on on that, but what I like is that IBM is trying to solve some hard problems with AI. >> I mean, lookit, I was tweeting yesterday all day on some highlights from my Puerto Rico trip on the cryptocurrency events we've been covering, and one thing that we reported was the killer app for blockchain and cryptocurrency and decentralized apps is money. Money is the killer app, and we see that with the hype cycle with the ICOs, but, if you look at what IBM's doing with the supply chain side of their business, perfect storm for supply chain innovation. Blockchain is about money, marketplaces, and nailing inefficient incumbents. So, if the incumbents want to be disruptive, they're going to have to disrupt themselves by removing inefficiencies out of the system. >> Well, and the Maersk example was a good one where there's inefficiencies, you know, 20% of the cost of moving containers is admin stuff. Sometimes the admin costs exceed the shipping costs. So, that was a good example, but, again, I see blockchain as one component in this fabric, in this puzzle. >> Day two, Cube here, kicking off wall-to-wall coverage. Three days of live broadcast talking to the thought leaders. Extracting the signal from the noise, the Cube, the number one leader in live tech coverage. Go to cube.net to check out all the footage and siliconangle.com to check out all of our articles. We're reporting and the team reporting all week, and that analysis of Ginni's keynote, well done, Dave. More coverage after this short break. (techno beat) >> Narrator: Robert Herjavec.

(orchestral music) >> Hello everyone. Welcome to the special CUBE conversation here in theCUBE's Palo Alto studio. I'm John Furrier. Where conversation around venture capital, entrepreneurship, crypto currencies, block chain, and more, Jonathan Ebinger our friend with BRV, formerly Blue Run Ventures, but BRV for short, sounds better, welcome to theCUBE. >> Thanks John, looking forward to it. >> Great to see you, we've known each other for a long time and you've been a great investor, your firm has done a lot of great stuff, deals are really famous deals, but also you dig into the companies and you really stand by your portfolio companies, but you've also done a lot of work in China. >> Yes. >> So you have a good landscape of what's going on. What's the, what's going on in China? >> Well China is really expanding in ways which we had not foreseen when we first started investing there almost 15 years ago. We were really active for five to 10 years, investing in companies that initially were considered copycat companies, you can't really use that term anymore. In fact what's happening more and more, you're seeing Chinese ideas coming to the United States. Businesses like We Chat are being copied as fast as they can, you're seeing Snapchat, Messenger and so forth, they're quickly trying to amalgamate as many assets as they can within their viewership much like we're seeing in a lot of the other Chinese analogs over there. It's exciting to see, it's very much an arms race. >> It's been interesting to watch. We were at the Ali Baba Cloud Conference last year, at the end of last year, it's interesting the innovation and entrepreneurial thirst has really changed. If you go back just 10 years ago when you guys were first getting in there, I remember the conversations were what's going on in China, it's very developmental but what's going on 10 years ago, they are dominating the mobile space, they're mobile usage is really much different makeup in how they do startups, the apps. How much of that has influenced some of their success just the demand? >> Always on, location always available, it opens up a whole new level of communication services. The idea of the larger screen format, people used to think in the United States, these large devices coming out of Korea first and then China, we thought these would never play in the United States, now Apple 10, larger screen size, it makes sense, it's mobile first right from the get go for a now billion plus users. >> So BRV, how many active portfolio companies do you guys have and what's the profile that you're looking for for entrepreneurs, what are some of the kind of companies? >> We're about 45 active companies right now. We're putting about, we're putting money in about 10 new companies a year at this point. We have a very disciplined approach of investing in Series A style companies, Series A of course means a lot of different things to people, but generally, we like to put $3 to $5 million to work early on and then follow on. >> How much do take for that, just a third? >> Typical in the 20%-25% range. There's a lot of companies out there that still fit that profile. Of course you're seeing some super sized Series A's that happen, we don't play in those but for the traditional software companies, evaluations are really right in our sweet spot. >> How big is the fund now, just what's the number in terms of capital? >> We're in fund six, we're just over $150 million. >> And you got to save some for follow on rounds. >> Exactly. >> Talk about the changes in venture capital because what's interesting, I had a conversation with Greg Sands with Costanoa Ventures, another great investor, formerly I think the first employee of Netscape I think or the business plan. Great guy, he talked about the dynamics of, you don't need that much cash anymore because if you can get unit economic visibility into what the business is working, you can do so much more with that and I'm calling it the hourglass effect, you get through that visibility, you're in control, you own your own destiny, versus the old Silicon Valley model which seems to be fading away, which is hey, what do you need? $40 million, or here's $100 million. That really limits your exit options and sometimes you can drown in your own capital. Talk about that dynamic. >> You're seeing the $40 million rounds with businesses that are much more capital intensive and that's coming back in vogue now but for the most part, I agree with what Greg's saying and this whole advent of seed funds and super seed funds and angel funds and so forth has been really great for the traditional series A investor. A lot of that early fundamental and foundational work is being done and then when the series A comes, it's more about expansion so we're effectively getting what was a Series B type stage company now we're investing in Series A. We're saying hey, this product works, there's product market fit, let's put dollars to work to really grow the market. >> So you're saying Series B was a kind of prove the business model, shifted down to the A because the cost to get there is lower and hence that's opened up a seed round lower in numbers, so it just shifts down a little bit. >> It really has, it really has and that plays into our sweet spot. We really like working on business models, distribution strategies, things like that. >> And what kind of startups do you want to invest in? What are some of the categories? >> Love financial services, we like health tech, we're doing education, we're really pretty omnivorous when it comes to the sector. What we're looking for is really businesses that are using data, real time data to disrupt the numbers. >> So you're not sector driven, you're disruption oriented. >> That's right. >> Okay let's talk about disruption, my favorite trend. Obviously I love the China dynamic because you're not sure what it is, but it's really doing well so you can't ignore it and they're innovative and they're hustling hard and they've got massive numbers. Block chain, we're super excited about, we love crypto, we think it's the biggest wave coming out there, so a lot of my smart, entrepreneurial friends are jumping on their surfboards literally and jumping out into those waves and there's a lot of action there. At the same time, people are saying, stay away from that crypto thing, it's a scam. Kind of a different perspective, what's your thoughts on that? >> If you look at, you separate the cryptocurrencies from block chain, I think it becomes a lot more clear. Block chain is for real. Tracking provenance on transactions, real estate transactions, multinational transactions, makes a lot of sense, dovetails nicely with security, so there's a real business there. You saw the announcement with IBM and Mersk the other day, what they are taking enterprise level block chain into their whole supply chain. I think that's really important. We have a company in the category called pay stand which is doing the same sort of thing with smaller size businesses, just accelerating the whole process on accounts receivable, taking working capital. >> And they're doing block chain for that? >> Yes block chain is an option, we're not forcing people onto block chain, but the idea of hey, let's give people more cost effective ways to transact, get rid of the paper checks, get rid of the invoicing and just join the modern world, much like you use Venmo if you and I are going to exchange money. >> That's pay stand, that's one of your hot companies. >> Yeah it is, absolutely. >> So are they using block chain or not? >> They are, yes. >> Okay, because it's a physical asset, it's kind of a supply chain thing? >> They use it to track the funds themselves, unlike a credit card where you have to pay a big fee or ACH which you can't really get proof of funds, with their block chain technology, you can be sure that you have the funds available and you get it instantly. >> Let's talk about use cases that you think out there, I'd like you to just weigh in on use cases for block chain that a mainstream person that's not in the tech business would understand, because they say, is it real or not? I agree block chain is legit, what are some use cases that would highlight that? >> I think if you've ever been involved in real estate, bought a home, things like that, just tracking title insurance, you're going all the way back if you live in California, you're going all the way back to pre-statehood days, you have to track the provenance of that land all the way through. You're paying title insurance, title insurance is a business you don't really need if you have accurate provenance tracking through block chain. I think that's one most of us can understand. Obviously bills of weighting with things coming over on ships. That's natural and right now things get held up in port because people are trying to find a clipboard before you can sign off on who, is this bill of weighting actually clean, that stuff can be done automatically with 2D barcodes, block chain usage. >> Certainly with perishable goods too, we learned that with IBM's example. >> Sure. >> Okay let's get into the hot companies you got going on. Name some of the hot investments that you've done. >> Sure, well I talked about pay stand a minute ago, really excited about them, another one we really like is a company called aerobotics. I know you're a fan of autonomous flying. If you think about drones and everyone knows DJI and they're a great company, that's one to one, one person flying one drone, that's not scalable obviously, it scales at one to one. With autonomous flying, you can have a whole army of drones out doing your business, whether they're doing site exploration, checking for chemical spills, looking at traffic and so forth. The company is now operating in three continents, it's just, if you think about what a drone is, effectively it's a flying cell phone. It's a cell phone that goes around, takes pictures, transmits data back, we know something about cell phones at BRV, we've been investing in this category for a long time so when we say aerobotics come along, we said this is just a natural extension of real time data, cellular technology, and location based services. >> You guys don't get a lot of credit as much as you should, in my opinion on that, you guys were very early on the mobile, mobile connectivity side and mobile footprint and device and software. That's playing well into the hottest trend that we see, that's not the sexiest trend, that's IOT. >> Absolutely. >> Because drones are certainly, industrial IOT is a big one. Instrumenting physical plants, equipment, and IOT in general the edge of the network. What's your thoughts on IOT and how would you, how do you see that evolving? It's more than just the edge of the network issue, it's bigger. >> It is, well of course the devices and sensors are important. I think a lot of that's been commoditized. The business that we've been seeing develop and there's a lot of folks, they've moved from analytics of the web to analytics of IOT, so there's a lot of interesting companies coming in the analytic space. We're not playing in that as much, we tend to like to invest in companies that are big enough that you need to have analytics for them. We like companies that have proprietary control of analytics versus necessarily running analytics for company X. >> So you're not poopooing IOT per se, just that from an investment thesis standpoint, it's not on your radar yet. >> That's right, they're either too capital intensive for us as a firm or you're basically managing someone else's data. I want to be in companies that we're managing our own data for a proprietary advantage. >> That's really what I was going to get to next, the role of data driven, so we've lived in dupe world, theCUBE started in 2010 in the offices of Cloud Air actually and people don't know the history and it's been interesting, Hadoop was supposed to save the world, the data, but it really started the data trend, the data driven trend, Mike Olsen, Amar Omadala and the team over there really nailed it but it didn't turn into be just Hadoop, it's everything so we're seeing that now become a bumper sticker, data driven marketer, I'm a data driven executive, I'm a data driven interviewer, all that stuff, what does it actually mean? What does data driven mean to you? >> Data is, there's big data and then there's actionable data obviously people talk about exhaust, the data coming off, we really got started with, as you know, we were investors in Waze, awful lot of data coming out of your cell phone, extracting just the important pieces of it are really what's important. We're investors in a company called Cabbage which looks at every transaction a small business makes to determine their credit worthiness. It's really the science. People talk about data scientists, what do they actually do? What they're actually doing is separating out the wheat from the chaff because it's just a crush of data. I saw your interview with Andy Jazzy to other day from AWS, the amount of data that's being stored, it's almost unfathomable but the important people. >> They have a lot of data. You'd like to invest in them now. >> Exactly, but that's really the thing, it's being able to separate the good data from the bad. >> You look at Amazon, I was talking to Jesse and he didn't really go there because he was kind of on message but when I talked with Swami who runs the AI group over there, we were talking about, I said to him straight up, I'm like, you're running a lot of workloads on your cloud, I'm sure you have data on those workloads. Just the impact of what they could do with that data. This is the virtuous cycle that their business model is made up of, but it's changing the game for what they can become. The thing that we're seeing in the data world is, sometimes the outcome might not be what you think because if you can use the data effectively, it's a competitive advantage, not a department. >> Right and you have to really stay true to your commitment to data. What we've seen happen is when companies, if you've been around for 10 years or so, you start to trust your gut, that's important, but it can also not lead you to see obvious conclusions because the world changes. >> And also committing to data also means from a practitioner's standpoint, investing in the tech, investing in things to be data driven, not just to say it. >> Exactly. >> Okay so what's the future for you guys? What are you looking at next year, what are some of the things you'd like to accomplish for investment opportunities, besides getting all the hot deals, you did Waze, that was an amazing deal, one of my favorite products, how did that go down? How many people passed on Waze? >> I don't know how many people passed, but we were lucky, they wanted to bring us in to the initial syndicate, they wanted to have some folks who understood. >> But it wasn't that obvious though at the beginning. What was the original pitch? >> The initial pitch was that they were going to have folks have the dash devices, the product would sit on your dashboard and they were going to be using it to map Eastern Europe because Eastern Europe was just coming into the Western world and they didn't really have good roads and good maps. We thought, that's interesting but they probably also don't have smartphones, so why don't we come across the Atlantic and let's make this thing work in the US and then from there, the rest took off country by country we were the number one navigation app in I think 150 countries at one point. >> What's the biggest thing that you've learned over the past few years in the industry that's different now I mean obviously there's some context that I'll share which is obviously the big cloud players are becoming bigger, scale's a big thing, you got Google, you got Microsoft and Amazon, you've got Facebook's out there as well. Then you get the political climate. You go to Washington D.C. and New York, Silicon Valley is not really talked highly about these days on the hill in Washington, yet GovCloud is completely changing the game of how the government is going to work with massive innovations and efficiencies, literally overnight, it's almost weird. >> It is and it isn't. If you look at it through a longer term horizon, Silicon Valley is again at the forefront, we're really the first ones with more transparency in the industry, all the different movements which are really important and all the conversations that are happening are important and they're happening here first. I think you're starting to see a ripple effect, you're seeing it going through entertainment, you're going to see it in the government, industry after industry I think is going to start to have to be more open as Silicon Valley has led the way on that. >> That's a great point. Take a minute to describe the folks out there watching that aren't from here, what is Silicon Valley about in your opinion? >> Silicon Valley is, of course it's more than a mindset, but folks who are here are here on purpose. They come here intentionally. There are very few people that I know who were born and raised here, so they're coming here because they want to be part of a shared ethos around success, around success, around shared values and competition so it's a very healthy environment, I came, I used to live in Washington D.C. and I couldn't be happier to be 3000 miles away. >> If you're a technology entrepreneur, this is where all the sports and action is, as I always say, we always love sports analogies. Okay, I got to ask you about the VC situation around ICOs, initial coin offerings are being talked about as an alternative to fundraising, there's some security options on token sales as a utility, the SEC has started to put some guidelines down on what that looks like, but the general sentiment is, it's a new way to raise money and some people are doing private rounds with venture capital and doing token sales through ICOs. You see some hybrids, but for the most part, the hard core I don't want to say right or left wing, is there a wing of the political spectrum, but the hard core ICO guys are like, this is all about disrupting the VC community and you're a VC, so you got to take that a little bit personal but the point is, what do you think about that? Is that talked about? >> I think that's good salesmanship. The VC industry such as it is, you can fit every VC into one section of Stanford stadium. There just aren't that many VCs to really go after. We're a small group of folks. I think that going after maybe disrupting the way folks are raising money through Kickstarter and things like that, that's all great. We're not going to stop it, we're going to embrace it. I think that there's plenty of different ways to raise capital, I have no compunction about those things. >> Do you think it's more of a democratization trend or a new asset class, so you don't see it disrupting the VCs per se, but if it's only a handful of VCs that could fit into Stanford Stadium, for instance, then certainly there's more options, it's a dilution. >> I think you look at it as it's just an alternative financing method, do I take debt, do I take equity, do I take venture, do I take friends and family? It's just one more arrow in the quiver of the entrepreneur, I think you have to be smart about it because thinking that you're going to get the same level of attention from an investor in your ICO that you are going to get from a series A investor who owns 20% of your company, those are two very different value propositions. >> So you see a lot of pitches and sometimes, you have to say no a lot and that's the way the game is, but a lot of times, you want the best deals. But the founders' side of the table, they're looking at the VC, I need money. So that's one of the options, what they really want is a value added partner, so what's your current take on what that means these days? Sometimes it means a firm, sometimes it means a partner, sometimes it means the community. How are you guys looking at BRV as value add versus the worst case scenario which is value subtract, you just want to have that be positive. >> I see that written about venture too. >> I know, some people experienced it. >> I think it helps that we've been around now for almost 20 years, we got started in '98 so you have to look at our body of work and the continuum of investments and founders and CEOs and CTOs that we've invested in. There's hundreds and hundreds of people who have taken money from BRV, and so that's one of the real positives about this current state we're in is that there's so much transparency. The fact that we are, I like to think we're good actors and have been for a long time, that comes out, now through our words but through the words of. >> What would they say about you guys? What would your entrepreneurs say about BRV? >> Aside from using buzzwords like value add, they say, they know their industry, they're not afraid to ask for help, they try to call problems when they see it, things like that. >> You stand by your companies. >> Absolutely. >> Awesome, well what's your favorite trend that you're personally interested in? >> I think you have to go after health care right now. It is just such a big market right now. People have been nibbling all different sides of it right now, there's been folks who are trying to expedite processing, there's actual innovations happening on the medical side, I think there is just, technology is just now starting to get into that, technology has gotten into education. >> How about the startup you guys funded that's related to the health care field. >> Yes, we're in a company called Hello Heart which is really at the confluence of a number of trends. It starts off, what Hello Heart is, it's a personal blood pressure cuff for you as an employee of a big company, more and more companies are starting to self insure. If you're a big enough company, 10,000 plus employees or even fewer, you're going to want to self insure to save money but also, your employees get very much more comfortable with you as an employer, you care about my well being, so it's a very virtuous cycle for the employees. >> So companies themselves insuring their own employees. >> Absolutely. >> They have to be super big, this company. >> This is just one component of a self insured business. You also, of course you still have access to doctors and stuff, I'm not making the pitch for being self insured as a company, I'm just saying that. >> But that's a trend. >> It's absolutely a trend and you're seeing a lot of what I would call point solutions stepping in, whether it's psychiatric, whether it's opioid help, whether it's working on heart conditions, these are all different point solutions which are being amalgamated together to help companies which are self insuring. >> So is Hello Heart for consumers or for business? >> It's sold to businesses but individual employees have it so they can keep track of their blood pressure. >> But I can't buy one if I wanted one? >> Not today, but I'll make sure I can get one to you. >> I need one, get all of our employees instrumented. >> Exactly. >> Drug tested all that stuff going on. People worry about the privacy, that's something I would be concerned with, putting. >> That's taken a really fast pendulum swing. A few years ago, Generation X was privacy, there is no privacy, the default was, location is always on, that's just flipped 180 degrees in the last few years. >> Well Jonathan, thanks for coming into this CUBE conversation, I want to ask you one final question, one thing we're passionate about is women in tech and underserved minorities, obviously Silicon Valley has to do a better job, it's out on the table, and it's working but we're still seeing a lot more work to be done, we're seeing titles not being at the right level, but pay's getting there in some places but titles aren't, some paying still below for women, still a lot more to do, what are you guys doing for the women in tech trend, how are you guys looking at that? Certainly it's a sensitive topic these days, but more importantly, it's one that's super important to society. >> It is, I think like a lot of things that have long term value, it's really about your actions versus your words, so our firm has two out of the five investment professionals are female, one of the last three CEO's we've founded is a female CEO, we have technologists, we have marketing people, we have CEO's that are females it's very much of a cross the board, sex, race and so forth. >> You guys are indiscriminate, a good deal's a good deal. >> Exactly right. >> It's about making money, VC's are in the business of making money, a lot of people don't understand, you guys have a job to do but you do a good job. >> We're in the business of making money but our investors for the most part are not for profits. Large universities, our biggest investor is the Red Cross, so when we do well, the Red Cross does well and the country does well. >> You're mission driven at this point. >> Exactly. >> Is that by design or is that just, your selection? >> We're delighted with our LP's, it's important that we have synergies aside from just finances with our investors. >> That's super well, I appreciate you coming on, I think it's super great that you're tying society benefits into money making and entrepreneurship, great stuff Jonathan Ebinger here on theCUBE, BRV check them out, great VC firm here in Silicon Valley. It's a CUBE conversation, we're talking about startups and entrepreneurship I'm John Furrier, thanks for watching. (dramatic music)

>> Hello, Wikibon community, and welcome to our 2017 predictions for the technology industry. We're very excited to be able to do this, today. This is one of the first times that Wikibon has undertaken something like this. I've been here since about April, 2016, and it's certainly the first time that I've been part of a gathering like this, with so many members of the Wikibon community. Today I'm joined with, or joined by, Dave Vellante, who's our co-CEO. So I'm the Chief Research Officer, here, and you can see me there on the left, that you can see this is from our being on TheCube at big data, New York City, this past September, and there's Dave on the right-hand side. Dave, you want to say hi? >> Dave: Hi everybody; welcome. >> So, there's a few things that we're going to do, here. The first thing I want to note is that we've got a couple of relatively simple webinar housekeeping issues. The first thing to note is everyone is muted. There is a Q&A option. You can hit the tab and a window will pop up and you can ask questions there. So if you hear anything that requires an answer, something we haven't covered or you'd like to hear again, by all means, hit that window, ask the question, and we'll do our best to get back to you. If you're a Wikibon customer, we'll follow up with you shortly after the call to make sure you get your question answered. If, however, you want to chat with your other members of the community, or with either Dave or myself, you want to comment, then there's also a chat option. On some of the toolbars, it's listed under the More button. So if you go to the More button, and you want to chat, you can probably find that there. Finally, we're also recording the webinar, and we will turn this into a Wikibon deliverable for the overall community. So, very excited to be doing this. Now, Dave, one of the things that we note on this slide is that we have TheCube in the lower left-hand corner. Why don't you take us through a little bit about who we are and what we're doing? >> Okay, great; thanks, Peter. So I think many of you or most of you know that SiliconANGLE Media Inc is sort of the umbrella company, and underneath SiliconAngle, we have three brands: the Wikibon research brand, which was started in the 2007 time frame. It's a community of IT practitioners. TheCube is, some people call it the ESPN of tech. We'll do 100 events this year, and we extensively use TheCUBE as a data-gathering mechanism and a way to communicate to our community. We've got some big shows coming up, pretty much every week, but of course we've got Amazon Reinvent coming up, and we'll be in London with HPE Discover. And so, we cover the world and cover technology, particularly in the enterprise, and then there's the SiliconANGLE publishing team, headed up by Rob Hoaf. It was founded by my co-CEO John Ferrier, and Rob Hoaf, former Business Week, is now leading that team. So those are the three main brands. We've got a new website coming out this month, on SiliconANGLE, so really excited about that and just thank the community for all your feedback and participation, so Peter, back to you. >> Thank you, Dave, so what you're going to hear today is what the analyst team here at Wikibon has pulled together for what we regard as some of the most interesting things that we think are going to happen over the next two years. Wikibon has been known for looking at disruptive technologies, and so while the focus, from a practical standpoint, in 2017, we do go further out. What is the overarching theme? Well, the overarching theme of our research and our conversations with the community is very simple. It's: put more data to work. The industry has developed incredible tools to gather data, to do analysis on data, to have applications use data and store data. I could go on with that list. But the data tends to be quite segmented and quite siloed to a particular application, a particular group, or a particular other activity. And the goal of digital business, in very simple terms, is to find ways to turn that data into an asset, so that it can be applied to other forms of work. That data could include customer data, operational data, financial data, virtually any data that we can imagine. And the number of sources that we're going to have over the next few years are going to be astronomical. Now, what we want to do is we want to find ways so that data can be freed up, almost like energy, in a physical sense, to dramatically improve the quality of the work that a firm produces. Whether it's from an engagement standpoint, or a customer experience standpoint, or actual operations, and increasingly automation. So that's the underlying theme. And as we go through all of these predictions, that theme will come out, and we'll reinforce that message during the course of the session. So, how are we going to do this? The first thing we're going to do is we're going to have six predictions that focus in 2017. Those six predictions are going to answer crucial questions that we're getting from the community. The first one is: what's driving system architecture? Are there new use cases, new applications, new considerations that are going to influence not only how technology companies create the systems and the storage and the networking and the database, and the middleware and the applications, but also how users are going to evolve the way they think about investing? The second one is: do micro-processor options matter? Through 20 years now, we've pretty much focused on one, limited class of micro-processor, the X386, er, the X86 architecture. But will these new workloads drive opportunities or options for new micro-processors? Do we have to worry about that? Thirdly, all this data has to be stored somewhere. Are we going to continue to store it, limited only on HDDs, or are other technologies going to come into vogue? Fourthly, in the 2017 time frame, we see the cloud, a lot's happening, professional developers have flocked to it, enterprises are starting to move to it in a big way, what does it mean to code in the cloud? What kinds of challenges are we going to face? Are they technological? Are they organizational, institutional? Are they sourcing? Related to that, obviously, is Amazon's had enormous momentum over the past few years. Do we expect that to continue? Is everybody else going to be continuing to play catch-up? And the last question for 2017 that we think is going to be very important is this notion of big data complexity. Big data has promised big things, and quite frankly has, except in some limited cases, been a little bit underwhelming. As some would argue, this last election showed. Now, we're going to move, after those six predictions, to 2022, where we'll have three predictions that we're going to focus on. One is: what is the new IT mandate? Is there a new IT mandate? Is it going to be the same old, same old, or is IT going to be asked to do new things? Secondly, when we think about Internet of Things, and we think about Augmented Reality or virtual reality, or some of these other new ways of engaging people, is that going to draw out new classes of applications? And then finally, after years of investing heavily in mobile applications, in mobile websites, and any number of other things, and thinking that there was this tight linkage where mobile equaled digital engagement, we're starting to see that maybe that's breaking, and we have to ask the question: is that all there is to digital engagement, or is there something else on the horizon that we're going to have to do? The last prediction, in 2027, we're going to take a stab here and say: will we all work for AI? So, these are the questions that we hear frequently from our clients, from our community. These are the predictions we're going to attend to and address. If you have others, let us know. If there's other things that you want us to focus on, let us know, but here's where we're starting. Alright. So let's start with 2017. What's driving system architecture? Our prediction for 2017 regarding this is very simple. The IoT edge use cases begin shaping decisions in system and application architecture. Now, the right-hand side, if you look at that chart, you can see a very, very important result of the piece of research that David Foyer recently did. And it shows IoT edge options, three-year costs. From left to right, moving all the data into the cloud over a normal data communications, telecommunications circuit, in the middle, moving that data into a central location, namely using cellular network technologies, which have different performance and security attributes, and then finally, keeping 95 percent of the data at the edge, processing it locally. We can see that the costs are overwhelming, favoring being smarter by how we design these applications and keeping more of that data local. And in fact, we think that so long as data and communications costs remain what they are, that there's going to be an irrevokeable pressure to alter key application architectures and ways of thinking to keep more of that crossing at the edge. The first point to note, here, is it means that data doesn't tend to move to the center as much as many are predicting, but rather, the cloud moves to the edge. The reason for that is that data movement isn't free. That means we're going to have even more distributed, highly autonomous apps, so none of those have to be managed in ways that sustain the firm's behavior in a branded, consistent way. And very importantly, because these apps are going to be distributed and autonomous, close to the data, it ultimately means that there's going to be a lot of operational technology players that impact the key decisions, here, that we're going to see made as we think about the new technologies that are going to be built by vendors and in the application architectures that are going to be deployed by users. >> So, Peter, let me just add to that. I think the key takeaway there is, as you mentioned, and I just don't want it to get lost, is 95 percent of the data, we're predicting, will stay at the edge. That's a much larger figure than I've seen from other firms or other commentary, and that's substantial, that's significant, it says it's not going to move. It's probably going to sit on flash, and the analytics will be done at the edge, as opposed to this sort of first bar, being cloud only. That 95 percent figure has been debated. It's somewhat controversial, but that's where we are today. Just wanted to point that out. >> Yeah, that's a great point, Dave. And the one thing to note, here, that's very important, is that this is partly driven by the cost of telecommunications or data communications, but there also are physical realities that have to be addressed. So, physics, the round trip times because of the speed of light, the need for greater autonomy and automation on the edge, OT and the decisions and the characteristics there, all of these will contribute strongly to this notion of the edge is increasingly going to drive application architectures and new technologies. So what's going to power those technologies? What's going to be behind those technologies? Let's start by looking at the CPUs. Do micro-processor options matter? Well, our prediction is that evolution in workloads, the edge, big data, which we would just, for now, put AI and machine learning, and cognitive underneath many of those big data things, almost as application forms, creates an opening for new micro-processor technologies, which are going to start grabbing market share from x86 servers in the next few years. Two to three percent next year, in 2017. And we can see a scenario where that number grows to double digits in the next three or four years, easily. Now, these micro-processors are going to come from multiple sources, but the factors driving this are, first off, the unbelievable explosion in devices served. That it's just going to require more processing power all over the place, and the processing power has to become much more cost-effective and much more tuned specifically to serving those types of devices. Data volumes and data complexity is another reason. Consumer economics is clearly driving a lot of these factors, has been for years, and it's going to continue to do so. But we will see new, ARM-based processors and other, and GPUs for big data apps, which have the advantage of being also supported in many of the consumer applications out there, driving this new trend. Now, the other two factors. Moore's Law is not out of room. We don't want to suggest that, but it's not the factor that it used to be. We can't presume that we're going to get double the performance out of a single class of technology every year or so, and that's going to remove any and all other types of micro-processor sets. So there's just not as much headroom. There's going to be an opportunity now to drive at these new workloads with more specialized technology. And the final one is: the legacy software issue's never going to go away; it's a big issue, it's going to remain a big issue. But, these new workloads are going to create so much new value in digital business settings, we believe, that it will moderate the degree to which legacy software keeps a hold on the server marketplace. So, we expect a lot of ARM-based servers that are lower cost, tuned and specialized, supporting different types of apps. A lot of significant opportunity for GPUs for big data apps, which do a great job running those kinds of graph-based data models. And a lot of room, still, for RISC in pre-packaged HCI solutions. Which we call: single managed entities. Others call: appliances. So we see a lot of room for new micro-processors in the marketplace over the next few years. >> I guess I'll add to that, and I'll be brief, just in the interest of time, the industry has marched to the cadence of Moore's Law for, as we know, many, many decades, and that's been the fundamental source of innovation. We see the innovation curve shifting and changing to become combinatorial, a combination of technologies. Peter mentioned GPU, certainly visualization's in there. AI, machine learning, deep learning, graph databases, combining to be the fundamental driver of innovation, going forward, so the answer here is: yes, they matter. Workloads are obviously the key. >> Great, Dave. So let's go to the next one. We talked about CPUs, well now, let's talk about HDDs. And more broadly, storage. So the prediction is that anything in a data center that physically moves gets less useful and loses share of wallet. Now, clearly that includes tape, but now it's starting to include HDDs. In our overall enterprise systems, storage systems revenue forecast, which is going to be published very, very shortly, we've identified that we think that the revenue attributable to HDD-based enterprise storage systems is going to drop over the next few years, while flash-based enterprise storage system revenue rises dramatically. Now, we're talking about storage system revenue here, Dave. We're not just talking about the HDDs, themselves. The HDD market starts, continues to grow, perhaps not as fast, partly because, even as the performance side of the HDD market starts to fade a bit, replaced by flash, that bulk, volume part of the HDD marketplace starts to substitute for tape. So, why is this happening? One of the main reasons it's happening is because the storage revenue, the storage systems revenue is very strongly influenced by software. And those software revenues are being bundled into the flash-based systems. Now, there's a couple reasons for this. First off, as we've predicted for quite some time, we do see a flash-only data center option on the horizon. It's coming well into focus. Number two is that, the good news is flash-based products are starting to come down and also are in sight of HDD-based products at the performance level. But very importantly, and here's one of the key notions of the value of data, and finding new ways to increase the use of data: flash, our research shows, offers superior business value, too, precisely because you can make so many copies of it and have a single set of data serve so many different applications and so many users, at scales that just aren't possible with traditional, HDD-based enterprise storage systems. Now, this applies to labor, too, Dave, doesn't it? >> Yeah, so a couple of points here. Yes, labor being one of those, sort of, areas that Peter's talking about are, ah, in jeopardy. We see about $200 billion over the next 10 years shifting from what we often refer to as non-differentiated IT labor, in provisioning and networking configuration and laying cable, et cetera, shifting from where it is today in services and/or on-prem IT labor, to vendor R&D or the cloud. So that's a very important point. I think I just wanted to add some color to what you were talking about before when you talked about HDD revenue continuing to grow, I think you were talking about, specifically, in the enterprise, in this storage systems view. And the other thing I want to add is, Peter, referenced sort of the business value of flash, as you, many of you know, David Floyer and Wikibon predicted, very early on, the impact that flash would have on spinning disk, and not only because of cost related to compression and de-duplication, but also this notion that Peter's talking about, of data sharing. The ability of development organizations to use the same data and minimize the number of copies. Now, the thing to watch, here, and kind of the wildcard is the hyperscale model. Hyperscalers, as we know, are consuming many, many, you know, exabytes and petabytes of data. They do things differently than is done in the enterprise, so that's something that we're watching very closely in terms of that model, that model being the hyperscale model, how it mimics or how it doesn't mimic what traditionally has occurred in the enterprise and how that will affect adoption of both flash and spinning disk. But as Peter said, we'll be releasing this data very shortly, and you'll be able to dig into it with is. >> And very importantly, Dave, in response to one of the comments in the chat, we're not talking about duplication of data everywhere, we're talking about the ability to provide logical and effective copies to single-data sources, so that, just because you can just drive a lot more throughput. So, that's the HDD. Now, let's turn to some of this notion of coding the cloud. What are we going to do with code in the cloud? Well our prediction is that the new cloud development stack, which is centered on containers and APIs, matures rapidly, but institutional habits in development constrain change. Now, why do we say that? I want to draw your attention to the graphic on the right-hand side. Now, this is what we think the mature, or the maturing cloud development stack looks like. As you can see, it's a lot of notions of containers, a lot of notions of other types of technologies. We'll see APIs interspersed throughout here as a primary way of getting to some of these container-based applications, services, microservices, et cetera, but this same, exact chart could be mapped back to SOA from 10 years ago, and even from some of the distributed computing environments that were put forward 20 years ago. The challenge here is that a sizable percentage, and we're estimating about 80 percent of in-house development, is still set up to work the old way. And so long as development organizations are structured to build monolithic apps or take care of monolithic apps, they will tend to create monolithic apps, with whatever technology's available to them. So, while we see these stacks becoming more vogue and more in use, we may not see, in 2017, shops being able to take full advantage of them. Precisely because the institutional work forms are going to change more slowly. Now, big data will partly contravene these habits. Why? Because big data is going to require quite different development approaches, because of the complexity associated of analytic pipelines, building analytic pipelines, managing data, figuring out how to move things from here to there, et cetera; there's some very, very complex data movement that takes place within big data applications. And some of these new application services, like Cognitive, et cetera, will require some new ways of thinking about how to do development. So, there will be a contravening force here, which we'll get to, shortly, but the last one is: ultimately, we think time-to-value metrics are going to be key. As KPI's move from project cost and taking care of the money, et cetera, and move more towards speed, as Agile starts to assert itself, as organizations start to, not only, build part of the development organization around Agile, but also Agile starts bleeding into other management locations, like even finance, then we'll start to see these new technologies really start asserting themselves and having a big impact. >> So, I would add to that, this notion of the iron triangle being these embedded processes, which as we all know, people, processes, and technology, people and process are the hardest to change, I'm interested, Peter, in your thoughts on, you hear a lot about Waterfall versus Agile; how will organizations, sort of, how will that affect organizations, in terms of their ability to adopt some of these, you know, new methodologies like Agile and Scrum? >> Well, the thing we're saying is the technology's going to happen fast, the Agile processes are being well-adopted, and are being used, certainly, in development, but I have had lots of conversations with CIOs, for example, over the last year and a half, two years ago, where they observed that they're having a very difficult time with reconciling the impedance mismatch between Agile development and non-Agile budgeting. And so, a lot of that still has to be worked out, and it's going to be tied back to how we think about the value of data, to be sure, but ultimately, again, it comes back to this notion of people, Dave, if the organization is not set up to fully take advantage of these new classes of technologies, if they're set up to deliver and maintain more monolithic applications, then that's what's going to tend to get built, and that's what's going to get, and that's what the organization is going to tend to have, and that's going to undermine some of the new value propositions that these technologies put forward. Well, what about the cloud? What kind of momentum does Amazon have? And our prediction for 2017 is that Amazon's going to have yet another banner year, but customers are going to start demanding a simplicity reset. Now, TheCUBE is going to be at Amazon Reinvent with John Ferrier and Steve Minnamon are going to be up there, I believe, Dave, and we're very excited. There's a lot of buzz happening about Reinvent. So follow us up there, through TheCUBE at Reinvent. But what I've done on the right-hand side is sent you a piece of Wikibon research. What we did is we wrote up, and we did an analysis of all of the AWS cases put forward, on their website, about how people are using AWS, and there's well over 650, or at least there were when we looked at it, and we looked at about two-thirds of them, and here's what we came up with. Somewhere in the vicinity of 80 percent, or so, of those cases are tied back to firms that we might regard as professional software delivery organizations. Whether they're stash or business services or games, provided games, or social networks. There's a smaller piece of the pie that's dedicated to traditional enterprise-type class of customers. But that's a growing and important piece, and we're not diminishing it at all, but the broad array of this pie chart, folks are relatively able to hire the people and apply the skills and devote the time necessary to learn some of the more complex, 75-plus Amazon services that are now available. The other part of the marketplace, the part that's moving into Amazon, the promise of Amazon is that it's simple, it's straightforward, and it is. Certainly more so than other options, but we anticipate that there will have to be a new type of, and Amazon's going to have to work even harder to simplify it, as it tries to attract more of that enterprise crowd. It's true that the flexibility of Amazon is certainly spawning complexity. We expect to see new tools, in fact, there are new tools on the market from companies like Appfield, for example, for handling and managing AWS billing and services, and that is, our CIOs are telling us, they're actually very helpful and very powerful in helping to manage those relationships, but the big issue here is that other folks, like VM Ware, have done research to suggest that the average shop has two to three big cloud relationships. That makes a lot of sense to us. As we start adding hybrid cloud into this and the complexities of inter-cloud communication and inter-cloud orchestration starts to become very real, that's going to even add more complexity, overall. >> So I'd add to that, just in terms of Amazon momentum, obviously those of you who follow what I read, you know, have been covering this for quite some time, but to me, the marginal economics of Amazon's model continue to be increasingly attractive. You can see it in the operating profits. Amazon's gap, operating profits, are in the mid-20s. 25, 26 percent. Just to give you a sense, EMC, who's an incredibly profitable company, its gap operating profits are in the teens. Amazon's non-gap operating profits are into 30 percent, so it's an incredibly profitable company. The more it grows, the more profitable it gets. Having said that, I think we agree with what Peter's saying in terms of complexity; think about API creep in Amazon. And different proprietary APIs for each of the data services, whether it's Kinesis or EC2 or S3 or Dynamo DB or EMR, et cetera, so the data complexity and the complexity of the data pipeline is growing, and I think that opens the door for the on-prem folks to at least mimic the public cloud experience to a great degree; as great a degree as possible. And you're seeing people, certainly, companies do that in their marketing, and starting to do that in the solutions that they're delivering. So by no means are we saying Amazon takes over the world, despite, you know, the momentum. There's a window open for those that can mimic, to the large extent, the public cloud capabilities. >> Yeah, very important point there. And as we said earlier, we do expect to see the cloud move closer to the edge, and that includes on-prem, in a managed way, as opposed to presuming that everything ends up in the cloud. Physics has something to say about that, as do some of the costs of data movement. Alright, so we've got one more 2017 prediction, and you can probably guess what it is. We've spent a lot of years and have a pretty significant place in spin big data, and we've been pretty aggressive about publishing what we think is going to happen in big data, or what is happening in big data, over the last year or so. One of the reasons why we think Amazon's momentum is going to increase is precisely because we think it's going to become a bigger target for big data. Why? Because big data complexity is a serious concern in many organizations today. Now, it's a serious concern because the spoke nature of the tools that are out there, many of which are individually extremely good, means that shops are spending an enormous amount of time just managing the underlying technology, and not as much time as they need to learning about how to solve big data problems, doing a great job of piloting applications, demonstrating to the business the financial returns are there. So as a result of this bespoked big data tool aggregates, we get multi-source, and we need to cobble it together from a lot of different technology sources, a lot of uncoordinated software and hardware updates that dramatically drive up the cost of on-prem administration. A lot of conflicting commitments, both from the business as well as from the suppliers, and very, very complex contracts. And as a result of that, we think that that's been one of the primary reasons why there's been so many pilot failures and why big data has not taken off the way that it probably should have. We think, however, that in 2017, we're going to see, and here's our prediction, we're going to see failure rates for big data pilots drop by 50 percent, as big vendors, IBM, Microsoft, AWS, and Google, amongst the chief ones, and we'll see if Oracle gets into that list, bring pre-packaged, problem-based analytic pipelines to market. And that's what we mean by this concept, here, of big data, single-managed entities. The idea that we can pull together, a company can pull together, or that it can pull together all the various elements necessary to provide the underlying infrastructure so that a shop can focus more time making sure that they understand the use-case, they understand how to go get the data necessary to serve that use-case, and understand how to pilot and deploy the application, because the underlying hardware and system software is pre-packaged and used. Now, we think that these, the SMEs, that are going to be most successful will be ones that are not predicated only on more proprietary software, but utilize a lot of open-source software. The ones that we see that are most successful today are in fact combining the pre-packaging of technology with the availability, or access, to the enormous value that the open-source market continues to build as it constructs new tools and delivers them out to big data applications. Ultimately, you've seen this before, or you've heard this before, from us: time-to-value becomes the focus. Similar to development, and we think that's one of the convergences that we have, here. We think that big data apps, or app patterns, will start to solidify. George Gilbert's done some leading-edge research on what some of those application patterns are going to be, and how those application patterns are going to drive analytic pipeline decisions, and very important, the process of building out the business capabilities necessary to build out the repeatable big data services to the business. Now, very importantly, some of these app patterns are going to be, are going to look like machine learning, cognitive AI, in many respects, all of these are part of this use-case to app trend that we see. So, we think that big data's kind of an umbrella for all of those different technology classes. It's going to be a lot of marketing done that tries to differentiate machine learning, cognitive AI. Technically, there are some differences, but from our perspective, they're all part of the effort of trying to ensure that we can pull together the technology in a more simple way so that it can be applied to complex business problems more easily. One more point I'll note, Dave, is that, and you adjust that world a lot, so I'd love to get your comments on this, but one of the more successful single-managed entities out there is, in fact, Watson from IBM, and it's actually a set of services and not just a device that you buy. >> Yeah, so a couple comments, there. One is that you can see the complexity in the market data, and we've been covering big data markets for a long time now, and there were two things that stood out when we started covering this. One is that software, as a percentage of the total revenue, is much lower than you would expect, in most markets. And that's because of the open-source contribution and the, you know, the multi-year collapse that we've seen in infrastructure software pricing. Largely due to open-source and cloud. The other piece of that is professional services, which have dominated spending within big data, because of the complexity. I think you're right, when you look at what happened at World of Watson and, you know, what IBM's trying to do, and others, in your prediction, there, are putting together a full, end-to-end data pipeline to do, you know, ingest and data wrangling and collaboration between data scientists, data engineers, and application developers and data quality people, and then bringing in the analytics piece. And essentially, you know, what many companies have done, and IBM included, they've cobbled together sets of tools and they've sort of layered on a way to interact with those tools, so the integration has still been slow in coming, but that's where the market is headed, so that we actually can build commercial, off the shelf applications. There's been a lack of those applications. I remember, probably four years ago, Mike Olsen at a (unintelligible) predicted: this will be the year of the big data app. And it still has not happened, so, and until it does, that complexity is going to reign. >> Yeah, and so it, again, as we said earlier, we anticipate that the big data, the need for developers to become more a part of the big data ecosystem, and the need for developers to get more value out of some of the other new cloud stacks are going to come together and will reinforce each other over the course of the next 24 to 36 months. So those were our 2017 predictions. Now let's take a look at our 2022 predictions, and we've got three. The first one is we do think a new IT mandate's on the horizon. Consistent with all these trends we've talked about, the idea of new ways of thinking about infrastructure and application architecture, based on the realities of the edge, new ways of thinking about how application developers need to participate in the value equation activities of big data, new ways of organizing to try to take greater advantage of the new processes, new technologies for development. We think, very strongly, that IT organizations will organize work to generate greater value from data assets by engineering proximity of applications and data. What do we mean by that? Well, proximity can mean physical proximity, but it also is something that we mean in terms of governance, tool similarity, infrastructure commonality, we think that over the next four to five years, we'll see a lot of effort to try to increase the proximity of not only data assets from a data standpoint, or the raw data, but also understanding from an infrastructure, governance skillset, et cetera, standpoint. So that we can actually do a better job of, again, generating more work out of our data by finding new and interesting ways of weaving together systems of records, big analytics, IOT, and a lot of other new application forms we see on the horizon, including one that I'll talk about in a second. Data value becomes a hot topic. We're going to have to do a better job, as a community, of talking about how data is valuable. How it creates (unintelligible) in the business, how it has to be applied, or has to be thought of as a source of value, in building out those systems. We talked earlier about the notion of people, process, and technology, well, we have to add to that: data. Data needs to be an asset that gets consumed as we think about how business changes. So data value's going to become a hot topic, and it's something we're focused on, as to what it means. We think, as Dave mentioned earlier, it's going to catalyze a true private cloud solutions for legacy applications. Now, I know Dave, you're going to want to talk about, in a second, what this might need. For example, things like the Amazon, VM Ware recent announcement. But it also means that strategic sourcing becomes reality. The idea of just going after the cheapest solution, or cost-optimized solution, which, don't get me wrong, don't get us wrong, is not going to go away, but it means that increasingly we're going to focus on new sourcing arrangements that facilitate creating greater proximity for those crucial aspects that make our shop run. >> Okay, so a couple of thoughts there, Peter. You know, there's a lot of talk, a couple years ago, and it's slowly beginning to happen, of bringing transaction and analytic systems together. What that oftentimes means is somebody takes their mainframe for the transactions and sticks it in finneban pipe into an exodata. I don't think that's what everybody envisioned when you started to sort of discuss that mean. So that's sort of happening slowly. But it's something that we're watching. This notion of data value, and shifting from, really a process economy to a data, or an insight, economy is something that's also occurring. You're seeing the emergence of the chief data officer. And our research shows that there are five things a chief data officer must do to really get started. The first is to understand data value, and how data contributes to the monetization of their company. So not monetizing the data, per se, and I think that's a mistake that a lot of people made, early on, is trying to figure out how to sell their data, but it's really to understand how data contributes to value for your organization. The second piece is how to access that data, who gets access to that data, and what data sources you have. And the third is the quality and trust of that data. And those are sequential things that our research shows a chief data officer has to do. And then the other, sort of parallel items, are relationship with the line of business and re-skilling. And those are complicated issues for most organizations to undertake, and something that's going to take, you know, many, many years to play out. The vast majorities of customers that we talk to say their data-driven, but aren't necessarily data-driven. >> Right, so, the one other thing I wanted to mention, Dave, is that we did some research, for example, on the VM Ware, Amazon relationship, and the reason why we were positive on it is quite simple. That it provides a path for VM Ware's customers, with their legacy applications running under VM Ware, to move those applications and the data associated with those applications, if they choose to, closer to some of the new, big data applications that are showing up in Amazon. So there's an example of this notion of making it more proximate, making applications and data more proximate, based on physics, based on governance, based on overall tooling and skilling, and we anticipate that that's going to become a new design center for a lot of shops over the course of the next few years. Now, coming to this notion of a new design center, the next thing we want to note is that, IoT, the Internet of Things, plus augmented reality, is going to have an impact on the marketplace. We got very excited about IoT, simply by thinking about the things, but our perspective is, increasingly, we have to recognize that people are going to always be a major feature, and perhaps the greatest value-creating feature, of systems. And augmented reality is going to emerge as a crucial actuator for the Internet of Things, and people. And that's kind of what we mean, is that augmented reality becomes an actuator for people. As will Chat Box and other types of technologies. Now, an actuator in an IoT sense is the devices or set of capabilities that take the results of models and actually turn that into a real-world behavior. So, if we think about this virtuous cycle that we have on the right-hand side, the internet, these are the three capabilities that we think people or firms are going to have to build out. They're going to have to build out an Internet of Things and People that are capable of capturing data, and turning analogue data into digital data, so that it can be moved into these big data applications. Again, with machine learning and AI and cognitive, sort of being part of that or underneath that umbrella, so that, then, we can build more models, more insights, more software that then translates into what we're calling systems of enaction. Or systems of "enaction", not "inaction". Systems of enaction. Businesses still serve customers, and these systems of enaction are going to generate real-world outcomes from these models and insights, and these real-world outcomes will certainly be associated with things, but they will also be associated with human being and people. And as a consequence of this, this we think is so powerful and is going to be so important over the course of the next five years that we anticipate that we will see a new set of disciplines focused on social discovery. Historically, in this industry, we've been very focused on turning insights or discovery about physics into hardware. Well, over the next few years, and Dave mentioned moving from the process to some new economy, we're going to see an enormous investment in understanding the social dynamics of how people work together and turn that into software. Not just how accountants do things, but how customers and enterprises come together to make markets happen, and through that social discovery, create these systems of enaction so that businesses can successfully, can successfully attend to and deliver the promises and the, ah, and the predictions that they're making through their other parts of their big data applications. >> So, Peter, you've pointed out many times that the big change, relative to processes, and historically, in the IT business, we've known what the processes are. The technology was sort of unknown, and mysterious. That's flipped. It's now, really the process is the unknown piece. That's the mysterious part. The technology is pretty well-understood. I think, as it relates to what you're talking about here with IoT and AR, what people tell us, the practitioners that are struggling with this, first of all, there's so much analogue data that people are trying to digitize, the other piece is there's a limited budget that folks have, and they're trying to figure out, alright, do I spend it on getting more data, and will that improve my data, increase my observation space? Or do I spend it on better models, and improving my models and iterating? And that's a trade-off that people have to make, and of course the answer is "both", but how those funds are allocated is something that organizations are really trying to better understand. There's a lot of trial and error going on. Because obviously, more data, in theory anyway, means you can make better decisions. But it's that iteration of that model, that trial and error and constant improvement, and both of those take significant resources. And budgets are still tight. >> Very true, Dave, and in fact, George Gilbert's research with the community is starting to demonstrate that more of the value's going to come from the models, as opposed to the raw data. We need the raw data to get to the models, but more of the value's going to come from the models. So that's where we think more people are going to focus their time and attention. Because the value will be in the insights and the models. But to go back to your point: where do you put your money? Well, you got to run these pilots, you got to keep up with your competitors, you got to serve customers better, so you're going to have to build all these models, sometimes in a bespoked way. But George is publishing an enormous amount of research right now that's very valuable to a lot of our community members that really shows how that pipeline, how those analytic pipelines or the capabilities associated with those analytic pipelines are starting to become better understood. So that we can actually start getting experience and generating efficiencies or generating a scale out of those analytic pipelines. And that's going to be a major feature underlying this basic trend. Now, this notion of people is really crucial, because as we think about the move to the Internet of Things and People, we have to ask ourselves: has digital engagement really, fully considered what it means to engage people throughout their customer journey? And the answer is: no, it hasn't. We believe that by 2022, IT will be given greater responsibility for management of demand chains. Working to unify customer journey designs and operations across all engagement functions. And by engagement functions, we mean marketing, sales, we mean product, we mean service, we mean fulfillment. That doesn't mean that they all report to IT. Don't mean that, at all. But it means that IT is going to have to, again, find ways to apply data from all these different sources so that it can, in fact, simplify and unify and bring together consistent design and operations so that all these functions can be successful and support reorganization if necessary, because the underlying systems provide that degree of unity and focus on customer success. Now, this is in strong opposition to the prediction made a few years ago, that marketing was going to emerge as the be-all and end-all, that's going to spend more than IT. That was silly, it hasn't happened, and you'd have to redefine marketing very aggressively to see that actually happening. But, when we think about this notion of putting more data to work, the first thing we note, and this is what all the digital natives have shown us, the data can transform a product into a service. That is the basis for a lot of the new business models we're talking about, a lot of these digital native business models and successes that they've had, and we think it's going to be a primary feature of the IT mandate to help business understand how data, more data can be put to work, transforming products into services. It also means, at a tactical level, that mobile applications have been way too focused on solving the seller's problems. We want to caution folks, don't presume that because your mobile application has gotten lost in some online store somewhere that that means that digital engagement's a failure. No, it means that you have to focus digital engagement on providing value throughout the customer journey, and not just from the problem to the solution, where the transaction for money takes place. Too much mobile applications, or too many mobile applications have been focused, in a limited way, on the marketers' problem within the business, of trying to generate, trying to generate awareness and demand. And it has to be, mobile has to be applied in a coherent and comprehensive way, across the entire journey. And ultimately, I hate to say this, but we think collaboration's going to make a comeback. But collaboration to serve customers. So the business can collaborate better inside, but in support of serving the customers. Major, major feature of what we think is going to happen over the course of the next couple years. >> I think the key point there is we all, there's many mobile apps that we love, and utilize, but there are a lot that are not so great. And the point that we've made to the community, quite often, is that it used to be that the brands had all the power, they had all the information, there was an asymmetry of information, the customer, the consumer didn't really know much about pricing. The web, obviously, has leveled that playing field and what many brands are trying to do is recreate that asymmetry and maybe got over their skis a little bit, before providing value to the customers. And I think your point is that, to the extent that you can provide value to that customer, that information advantage will come back to you. But you can't start with that information advantage. >> Great point, Dave. But it also means that we need to, that IT needs to look at the entire journey and see transactions and the discover, evaluate, buy, apply, use and fix throughout this entire journey and find ways of designing systems that provide value to customers at all times and in all places. So the demand chain notion, which historically has been focused on trying to optimize the value that the buyer gets in the buy process, at a cost-effective way, that notion of demand chain has to be applied to the entire engagement lifecycle. Alright, so that's 2022. Let's take a crack at our big prediction for 2027. And it's at, ah, it's on a lot of people's minds. Will we all work for AI? There've been a lot of studies done, over the course of the past year, year and a half, that have been kind of suggested that 47 percent of jobs are going to go away, for example. And that's not, that's not the only high end. Actually, folks have suggested much more, over the next 10, 15 years. Now, if you take a look at the right-hand side, you see a robot thinker. Now, you may not know this, but when The Thinker was actually first, when Rodan first constructed The Thinker, what he was envisioning was actually someone looking down into the seven levels of Hell as described by Dante. And I think that a lot of people would agree that the notion of no work is a Hell for a lot of people. We don't think that that's going to happen in the same way that most folks do. We believe that AI technology advances will far outpace the social advances. Some tasks will be totally replaced, but most jobs will only be partially replaced. We have to draw a clear distinction between the idea that a job performs only this or that task, as opposed to a job or an individual, an employee, as part of a complex community that ensures that a business is capable of serving customers. It doesn't mean we're not going to see more automation, but automation is going to focus mostly on replacing tasks. And to the degree that that task sets a particular job is replaced, then those jobs will be replaced. But ultimately, there's going to be a lot of social friction that gates how fast this happens. One of the key reasons for the social friction is something in behavioral economics that's known as loss avoidance. People are more afraid of losing something than they are of gaining something. And, whether it's a union or whether it's regulations or any number of other factors, that's going to gate the rate at which this notion that AI crushes employment occurs. AI will tend to compliment, not substitute for labor. And that's been a feature of technology for years. It doesn't, again, mean that some tasks and some task sets, sort of those in line with jobs, aren't replaced; there will be people put out of work as a consequence of this. But for the most part, we will see AI tend to compliment, not fully substitute for most jobs. Now this creates, also, a new design consideration. Historically, as technologists, we've looked at what can be done with technology, and we've asked: can we do it? And if the answer is "yes", we tend to go off and do it. And now, we need to start asking ourselves: should we do it? And this is not just a moral imperative. This has other implications, as well. So, for example, the remarkably bad impact that a lot of automated call centers have had on customer service from a customer experience standpoint. This has to become one of the features of how we think about bringing together, in these systems of enaction, all the different functions that are responsible for serving a customer. Asking ourselves: well, we can do it, from a technical standpoint, but should we do it from a customer experience, from a community relations, and even from a, ah, from a cultural imperative standpoint, as we move forward? >> Okay, I'll be brief, because we're wrapping up here, but first of all, machines have always replaced humans. When, largely with physical tasks, now we're seeing that occur with cognitive tasks. People are concerned, as Peter said. The middle class is obviously under fire. The median income in the United States has dropped from $55,000 in 1999 to just above $50,000 today. So, something's going on, and clearly you can look around and see whether it's an an airport with kiosks or billboards, electronic machines and cognitive functions are replacing human functions. Having said that, we're sanguine, because the, the story I'll tell is that the greatest chess player in the world is not a machine. When Deep Blue beat Gary Kasparov, what Gary Kasparov did is he started a competition to collaborate with other, you know, human chess players with machines, to beat the machine, and they succeeded at that, so this, again, I come back to this combination of technologies. Combinatorial technologies are really what's going to drive the innovation curve over the next, we think, 20 to 50 years. So, it's something that is far out there, in terms of our predictions, but it's also something that is relevant to the society, and obviously the technology industry. So thank you, everybody. >> So, we have one more slide, and it's Conclusions Slide, so let me hit these really quick, and before I do so, let me note that George, our big data analyst is George Gilbert. George Gilbert: G-I-L-B-E-R-T. Alright, so, very quickly, tech architecture question, we think edge IoT is going to have a major effect in how we think about architecture of the future. Micro-processor options? Yup, new micro-processor options are going to have an impact in the marketplace. Whither HDDs? For the performance side of storage, flash is coming on strong. Code in the cloud? Yes, the technologies are great, but development has to change its habits. Amazon momentum? Absolutely going to continue. Big data complexity? It's bad and we have to find ways to make it simpler so that we can focus more on the outcomes and the results, as opposed to the infrastructure and the tooling. 2022, new IT mandate? Drive the value of that data. Get more value out of your data. The Internet of Things and People is going to become the proper way of thinking about how these new systems of enaction work, and we anticipate that demand chain management is going to be crucial to extending the idea of digital engagement. Will we all work for AI? Dave just mentioned, as we said, there's going to be dislocation, there's going to be tasks that are replaced, but not by 2027. Alright, so thank you very much for your time, today. Here is how you can contact Dave and myself. We will be publishing this, the slides and this broadcast. Wikibon's going to deliver three coordinated predictions talks over the course of the next two days, so look for that. Go up to SiliconANGLE, we're up there a fair amount. Follow us on Twitter, and we want to thank you very much for staying with us during the course of this session. Have a great day.

>> live from the Mandalay Bay Convention Center in Las Vegas. It's the King covering via World 2016 brought to you by IBM Wear and its ecosystem sponsors. Now here's your host stew minimum. Welcome back to the Cube here at VM World 2016. Happy to welcome back to the PO program. Christos Caramel analysts. Who's the fellow in CTO of the V A more storage and availability business unit. Thank you for joining us again. >> About to be buck >> Storage is a big focus here. Big announcements around. Not only the sand, but everything happened in the storage room. Tell us what you've been working on the last year. >> Yeah, quite a few things. As you know, Miss Olsen has become practically mainstream product now, especially since we saved the very same 6.2 back in March 2016 with a number of new enterprise grade features for space efficiency. New availability. Fisher's with the razor calls right 56 The product is really taking off. Taking off, especially in old flask configurations, is becoming the predominant model that our customers are using. So ultimately, of course, customers buy a new product like this on and hyper converts product because of the operational efficiencies and brings to their data centers. The way I present this is you have the personal efficiency off public clouds into your private data center now. But this is for me is thus the stepping stone for even a longer term term, bolder vision will have around the stores, the data management. So, the last several months now, I have been working on a new range of projects. Main theme. There is moving up the stock from stores and the physical infrastructure implications. It has two data management on starting with data protection on overall and managing the life cycle of your data for protection, for disaster recovery, for archival, so that you can have tools to be able to effectively and efficiently discover your data. Mine your data. Use them by new applications, including cloud native applications and a dent even know that this may sound a little controversial coming from Vienna, where sitio even moving your data to public clouds and allow application mobility freely between private public clouds. >> Yeah, it's really interesting and wonder if you can packed out a little bit for us, Veum, where, of course, really dominant, the Enterprise Data Center. We're trying to understand where Veum, where fits into the public cloud on how you cut both support the existing ecosystem and move forward. So, you know, it's interesting off >> course. There are silences. There are many open questions. I do not claim that we have the answers to everything. Everything. But you do see that we put a lot of emphasis on that because it is obvious that the I T world is evolving. Our own customers are gradually slowly, but certainly there start incorporating public clouds into the bigger I T organizations that have. So our goal is to start delivering value to our customers based on clouds, starting with what they have today into the data centers. Let me give you a specific example in the case of Virtual San, who have some really cool tools for Mona's in your infrastructure in a holistic way, computer networking and now stores a SZ part of that you have ah solutions and tools that allow the customer to monitor constantly there covered infrastructure, the configuration of that. The class is the network servers controller's down to individual devices, and we provide a lot of data to the customers, not only for the health but also for the performance off the off the infrastructure data to the customer can today used to perform root cause analysis of potential issues to decide how to optimize there. Infrastructure in the world clothes. But that is actually pretty no sophisticated house. You cannot expect a lot 500 thousands 1000 customers. Of'em were to be ableto do this kind of sophisticate analysis. So what we're working on right now is a set off analytics tools that do all this data Kranz ink and analysis a root cause analysis on DDE evaluation of the infrastructure on because of the customer instead of providing data now we're providing answers and suggestions now way want to be able to deliver those analytics in a very rapid cadence. So what we do is we develop all those things in via Morse. Cloud will collect data from the customer side through telemetry, the emir's phone home product, and we get off the data up in our club. We crunch the data on because of the customer, and we use really sophisticated methods that will be evolving over time and eventually will be delivering feedback and suggestions at a kind level to the customer that can be actionable. For example, weekend point out that certain firm were the 1st 1 off certain controllers, and the infrastructure is falling behind. I may have problems or point out to a certain SS thes uh, a problem getting close to the end off life. For more sophisticated thing. Starts us reconfigure your application with a different policy for data distribution to achieve better performers. The interesting thing is that going to be, you're going to be combining data from must multiple sites, multiple customers to be able to do this holistic analytics and say, You know what? Based on trance, I see. Another customer says. It says You also do that. Now they're really coursing out of this is that the customer does not have to go and use yet another portal on a public cloud to take advantage of that. But they in fact, we send all that feedback through the this fear you. I own premise to the customers, so really cool. So you have the best of both wars. There are big development off analytics using actually behind the senses a really complex cloud native application with the existing tools that the customers are usedto in on premise. So this is just one example >> crystals. Could you give us a little bit of insight as the guiding light for your development process? Do you use that kind of core customers that you're pulling in and working in? Is it a mandate from above that says, you know, Hey, we need to build a more robust and move up the stack. You know, what are some of the pieces that lead to the development that you >> know? This is a very interesting point. I must start by stating that vehement has always bean admitting they're driven company. Um, and look for products were, you know, ideas that were, you know, Martin by engineers, while others thought that was not your not even visible, of course, Mutualization in several stages. But features like the Muslim or stores of emotion Oreo even, you know, ideas kind of ritual, son, right. Claiming that I could do very effectively rate six in software was something that was not really, you know, appreciated in the industrial area stages. So a lot of the innovation is a grassroots innovation. We have our engineers exposed directly to customers customer problems off course. They also understand what is happening in the industry. The trends, whether that is encounter as its case these days with a new generation off first or its cover that is emerging, or where that that is a trend. Samoan customers, for example, using public clouds in certain ways where that is for doing testing dead or archiving their data way. Observe those things and then through a grassroots. Therefore, all this get amalgamated into some concrete ideas. I'm not saying that all those ideas result into products, but we definitely have a very open mind in letting engineers experiment and prove sometimes common sense to be wrong. So this is the process thesis. How Virtual Son started were a couple of us went to our CEO back then for marriage and suggested we do this drastic thing that is called no softer stores on that you can run the soft store of stock in software on the same servers that we visualize, and we're under V. M. So this is really how the process has always been working and this is still the case and we're very proud of this culture. This is one way we're actually tracking opens enduring talent in the competent. >> Yeah, I was loved digging into some of the innovation processes. Had a good chat with Steve Harris, former CEO of GM, where if I remember right? One of the thing processes user called flings, whereas you can actually get visibility from the outside it to some of those kind of trials and things that are going on that aren't yet fully supported yet. >> Absolutely. And that is still the case. Probably the best known fling these days is the HTML five days they you I for your sex, which is used extensively, both internally in the humor where it actually started as a tool for that purpose, but now wild by the community. And that Flynn gave us a lot off insides and how to evolve our mainstream user interface for for this fear, proper notes, Astoria sex. So this is exactly this alternative process that leads us to test the water and feel much more confident when we make bigger and investments in in Ireland, >> right architecturally via Moore has been around for quite a while now. I had a good talk with such a Pagani Who? I m f s earlier today and we were talking about, you know, new applications and new architectures when vms foot fest was built. You know, nobody's thinking about containers. You know, they weren't thinking about applications like duper some of these more cloud native applications. How do you take into consideration where things were going? How did these fit into, you know, kind of traditional VM wear V sphere. You know what things need to change? How do you look at kind of the code basis? >> Right. So first of all of'em affairs, I must say it's probably the most mature and most widely adopted class. The file system in the industry for over 10 years now has been used to visualize enterprise grade store, its stores, alien networks, and it was going to have a role for many years to come. But on the other hand, we all are technologists, and we understand that the product is designed with certain assumptions and constraints, and the EM affairs was designed back in the meat to thousands toe address the requirements for ritual izing lungs, and you know the traditional volumes that you'd be consuming from a disgrace. Now the world is changing, right. We have a whole new generation off solid state devices for stores. Servers on softer on commodity servers with Commodity stores Devices is becoming as your own reports that have been indicating the predominant no mortal of delivering stores in there in the enterprise that the sender and off course in even public clouds with copper scale storage. So what? The requirements there? Some things are changing. You need the store. Its plot from that can really take out the violence of the very low latency is off those devices. I was at Intel Developer for form a couple of weeks ago, and their intel announced for first time performance numbers for the new generation off Envy Me devices obtained that include the three D Chris Point technology under the covers. Latents is at around 10 microseconds, right and Iost per second scruples that are in the several kinds of thousands, if not millions so completely young game changer. And that is not the only company that is coming up with this technology. So you need to invest now in new technologies that can take the can harness the capabilities of this new devices, lightweights protocols like Envy me. In fact, I see envy me as the protocol is not just a protocol to accident device, but I can see a future for that off. Replacing Scott Z into the software start soon, and this is committing specific days. But soon will be sipping a vision off this fear that comes with ritual and via me in the guest visual ization of envy Me. So you can see here where we're heading and envy me, becoming a predominant protocol for the transport and for brutalizing stores. >> Interesting. And we've got a long history of things that start on. The guests Usually then takes a lot of engineering work to get them down to the hyper visor themselves. So, you know, without having to give away too much, is that we see that kind of progression sometime in the future. For some of these new memory, architectures >> certainly certainly are the sex store stock, and this is the stuff that is used by Veum infest by ritual son. It has been designed again for another era off stores. Now we are regarding a lot of these things there, and I cannot disclose too much detail, obviously, but I can tell that it's going to be a very different software stock. Much leaner, much more optimized for local, very fast devices and ultimately envying me is going to be a key technology in this new store stock. >> All right, so just last follow up on that topic. I think about kind of a new memory architectures. What's going on? As of September 7th, Del will acquire TMC. There's the relationship between A. M, C and V M wear. So could we expect some of these new memory technologies impacting things to be something that you'll work even closer with a deli emcee? And >> that is definitely case irrespective off the deal between the emcee and Dell, which, as you said, it's going to be closing. It seems pretty soon. From what I read in the newspapers, >> Michael confirmed, it's finally official. Some of the pathetic ALS. >> Yes, we're moving ahead with this new technologists, and we're working closely with all the partners micro intel and many of the other car vendors that are introducing such technologies to incorporate them into our systems into our software, for example, I see great opportunities for this very fast Cayenne dude owns but still quite expensive technologies to be used, for example, to store meta data. Things like duplication. Costabile is those kind off meta data that have an impact through because of my own verification to the performance that is perceived by the application by moving meta data like that into those tears are going to make a great difference in terms of performance consistent, late and see predictability of the day for the application. Now, thanks to the relations with del Auntie em. See, I can hope that some of these technologies will find their way into several platforms sooner than later. So all of us and our customers would benefit from that. >> All right? What? Christos really appreciate getting the update from you. Lots happening on the storage world. We're kind of talking about. One of my things coming into this this'll week was, if we can really simplify storage, we might actually have a storage. This world doesn't mean it reduces the value of storage or the importance of it, but gonna help the users to be able to move beyond that, we'll be back with lots more coverage here from the emerald 2016. You're watching the Cube. Glad to be here. Whatever. Apply from the Mandalay Bay Convention Center in Las Vegas. It's the King covering via World 2016 brought to you by IBM Wear and its ecosystem sponsors. Now here's your host stew minimum. Welcome back to the Cube here at VM World 2016. Happy to welcome back to the PO program. Christos Caramel analysts. Who's the fellow in CTO of the V A more storage and availability business unit. Thank you for joining us again. >> Glad to be back.

>> live from the Congress Centre in London, England. It's the queue at M I t. And the digital economy. The second machine Age Brought to you by headlines sponsor M I T. >> Everybody, welcome to London. This is Dave along with student men. And this is the cube. The cube goes out, we go to the events. We extract the signal from the noise. We're very pleased to be in London, the scene of the first machine age. But we're here to talk about the second Machine age. Andrew McAfee and Erik Brynjolfsson. Gentlemen, first of all, congratulations on this fantastic book. It's been getting great acclaim. So it's a wonderful book if you haven't read it. Ah, Andrew, Maybe you could hold it up for our audience here, the second machine age >> and Dave to start off thanks to you for being able to pronounce both of our names correctly, that's just about unprecedented. In the history of this, >> I can probably even spell them. Whoa, Don't. So, anyway, welcome. We appreciate you guys coming on and appreciate the opportunity to talk about the book. So if you want to start with you, so why London? I mean, I talked about the first machine age. Why are we back here? One of the >> things we learned when we were writing the book is how big deal technological progress is on the way you learn that is by going back and looking at a lot of history and trying to understand what bet the curve of human history. If we look at how advanced our civilizations are, if we look at how many people there are in the world, if we look at GDP per capita around the world, amazingly enough, we have that data going back hundreds, sometimes thousands of years. And no matter what data you're looking at, you get the same story, which is that nothing happened until the Industrial Revolution. So for us, the start of the first machine machine age for us, it's a real thrill to come to London to come to the UK, which was the birthplace of the Industrial Revolution. The first machine age to talk about the second. >> So, Eric, I wonder if you could have with two sort of main vectors that you take away from the book won is that you know, machines have always replaced humans and maybe doing so at a different rate of these days. But the other is the potential of continued innovation, even though many people say Moore's law is dead. You guys have come up with sort of premises to how innovation will continue to double. So boil it down for the lay person. What should we think about? Well, sure. >> I mean, let me just elaborate on what you just said. Technology's always been destroying jobs, but it's also always been creating jobs, you know, A couple centuries ago, ninety percent of Americans worked in agriculture on farms in nineteen hundred is down to about forty one percent. Now is less than two percent. All those people didn't simply become unemployed. Instead, new industries were invented by Henry Ford, Steve Jobs, Bill Gates. Lots of other people and people got rather unemployed, became redeployed. One of the concerns is is, Are we doing that fast enough? This time around, we see a lot of bounty being created by technology. Global poverty rates are falling. Record wealth in the United States record GDP per person. But not everyone's participating in that. Not even when sharing the past ten fifteen years, we've actually to our surprise seem median income fall that's income of the person the fiftieth percentile, even though the overall pie is getting bigger. And one of the reasons that we created the initiative on the digital economy was to try to crack that, not understand what exactly is going on? How is technology behaving differently this time around in earlier eras and part that has to do with some of the unique characteristics of eventual goods? >> Well, your point in the book is that normally median income tracks productivity, and it's it's not this time around. Should we be concerned about that? >> I think we should be concerned about it. That's different than trying to stop for halt course of technology. That's absolutely not something you >> should >> be more concerned about. That way, Neto let >> technology move ahead. We need to let the innovation happen, and if we are concerned about some of the side effects or some of the consequences of that fine, let's deal with those. You bring up what I think is the one of most important side effects to have our eye on, which is exactly as you say when we look back for a long time, the average worker was taking home more pay, a higher standard of living decade after decade as their productivity improved. To the point that we started to think about that as an economic law, your compensation is your marginal productivity fantastic what we've noticed over the past couple of decades, and I don't think it's a coincidence that we've noticed this, as the computer age has accelerated, is that there's been a decoupling. The productivity continues to go up, but the wage that average income has stagnated. Dealing with that is one of our big challenges. >> So what you tell your students become a superstar? I mean, not everybody could become a superstar. Well, our students cats, you know, maybe the thing you know they're all aspired to write. >> A lot of people focus on the way that technology has helped superstars reach global audiences. You know, I had one student. He wrote an app, and about two or three weeks, he tells me, and within a few months he had reached a million people with that app. That's something that probably would have been impossible a couple of decades ago. But he was able to do that because he built it on top of the Facebook platform, which is on top of the Internet and a lot of other innovations that came before. So in some ways it's never been easier to become a superstar and to reach literally not just millions, but even billions of people. But that's not the only successful path in the second machine age. There's also other categories where machines just aren't very good. Yet one of the ones that comes to mind is interpersonal skills, whether that's coaching or underst picking up on other cues from people nurturing people carrying for people. And there are a whole set of professions around those categories as well. You don't have to have some superstar programmer to be successful in those categories, and there are millions of jobs that are needed in those categories for to take care of other P people. So I think there's gonna be a lot of ways to be successful in the second machine age, >> so I think >> that's really important because one take away that I don't like from people who've looked at our work is that only the amazing entrepreneurs or the people with one forty plus IQ's are going to be successful in the second machine age. That's it's just not correct. As Eric says, the ability to negotiate the ability Teo be empathetic to somebody, the ability to care for somebody machines they're lousy of thes. They remain really important things to do. They remain economically valuable things >> love concern that they won't remain louse. If I'm a you know, student listening, you said in your book, Self driving cars, You know, decade ago, even five years ago so it can happen. So how do we predict with computers Will and won't be good at We >> basically don't. Our track record in doing that is actually fairly lousy. The mantra that I've learned is that objects in the future are closer than they appear on the stuff that seem like complete SciFi. You're never goingto happen keeps on happening now. That said, I am still going to be blown away the first time I see a computer written novel that that that works, that that I find compelling, that that seems like a very human skill. But we are starting to see technologies that are good at recognizing human emotions that can compose music that can do art paintings that I find pretty compelling. So never say never is another. >> I mean right, right. If if I look some of the examples lately, you know, basic news computers could do that really well. IBM, you know, the lots of machine can make recipes that we would have never thought of. Very things would be creative. And Ian, the technology space, you know, you know, a decade ago computer science is where you tell everybody to go into today is data scientists still like a hot opportunity for people to go in And the technology space? Where, where is there some good opportunity? >> Or whether or not that's what the job title on the business card is that going to be hot being a numerous person being ableto work with large amounts of data input, particular being able to work with huge amounts of data in a digital environment in a computer that skills not going anywhere >> you could think of jobs in three categories is ready to technology. They're ones that air substitutes racing against machine. They're ones that air compliments that are using technology under ones that just aren't really affected yet by technology. The first category you definitely want to stay away from. You know, a lot of routine information processing work. Those were things machines could do well, >> prepare yourself as a job. Is that for a job as a payroll clerk? There's a really bad wait. >> See that those jobs were disappearing, both in terms of the numbers of employment and the wages that they get. The second category jobs. That compliment data scientist is a great example of that or somebody who's AP Writer or YouTube. Those are things that technology makes your skills more and more valuable. And there's this huge middle category. We talked earlier about interpersonal skills, a lot of physical task. Still, where machines just really can't touch them too much. Those are also categories that so far hell >> no, I didnt know it like middle >> school football, Coach is a job. It's going to be around a human job. It's going to be around for a long time to come because I have not seen the piece of technology that can inspire a group of twelve or thirteen year olds to go out there and play together as a team. Now Erik has actually been a middle school football coach, and he actually used a lot of technology to help him get good at that job, to the point where you are pretty successful. Middle school football coach >> way want a lot of teams games, and part of it was way could learn from technology. We were able to break down films in ways that people never could've previously at the middle school level. His technology's made a lot of things much cheaper. Now then we're available. >> So it was learning to be competitive versus learning how to teach kids to play football. Is that right? Or was a bit? Well, actually, >> one of the most important things and being a coach is that interpersonal connection is one thing I liked the most about it, and that's something I think no robot could do. What I think it be a long, long time. If ever that inspiring halftime speech could be given by a robot >> on getting Eric Gipper bring the Olsen Well, the to me, the more, most interesting examples I didn't realise this until I read your book, is that the best chess player in the world is not a computer, it's a computer and a human. That's what those to me. It seemed to be the greatest opportunities for innovative way. Call a >> racing with machines, and we want to emphasize that that's what people should be focusing. I think there's been a lot of attention on how machines can replace humans. But the bigger opportunities how humans and machines could work together to do things they could never have been done before in games like chess. We see that possibility. But even more, interestingly, is when they're making new discoveries in neuroscience or new kinds of business models like Uber and others, where we are seeing value creation in ways that was just not possible >> previously, and that chess example is going to spill over into the rest of the economy very, very quickly. I think about medicine and medical diagnosis. I believe that work needs to be a huge amount, more digital automated than it is today. I want Dr Watson as my primary care physician, but I do think that the real opportunities we're going to be to combine digital diagnosis, digital pattern recognition with the union skills and abilities of the human doctor. Let's bring those two skill sets together >> well, the Staton your book is. It would take a physician one hundred sixty hours a week to stay on top of reading, to stay on top of all the new That's publication. That's the >> estimate. And but there's no amount of time that watching could learn how to do that empathy that requires to communicate that and learn from a patient so that humans and machines have complementary skills. The machines are strong in some categories of humans and others, and that's why a team of humans and computers could be so >> That's the killer. Since >> the book came out, we found another great example related to automation and medicine in science. There's a really clever experiment that the IBM Watson team did with team out of Baylor. They fed the technology a couple hundred thousand papers related to one area of gene expression and proteins. And they said, Why don't you predict what the next molecules all we should look at to get this tart to get this desired response out on the computer said Okay, we think these nine are the next ones that are going to be good candidates. What they did that was so clever they only gave the computer papers that had been published through two thousand three. So then we have twelve years to see if those hypotheses turned out to be correct. Computer was batting about seven hundred, so people say, didn't that technology could never be creative. I think coming up with a a good scientific hypothesis is an example of creative work. Let's make that work a lot more digital as well. >> So, you know, I got a question from the crowd here. Thie First Industrial Revolution really helped build up a lot of the cities. The question is, with the speed and reach of the Internet and everything, is this really going to help distribute the population? Maur. What? The digital economy? I don't I don't think so. I don't think we want to come to cities, not just because it's the only waited to communicate with somebody we actually want to be >> face to face with them. We want to hang out with urbanization is a really, really powerful trend. Even as our technologies have gotten more powerful. I don't think that's going to revert, but I do think that if you if you want to get away from the city, at least for a period of time and go contemplate and be out in the world. You can now do that and not >> lose touch. You know, the social undistributed workforce isn't gonna drive that away. It's It's a real phenomenon, but it's not going to >> mean that cities were going >> to be popular. Well, the cities have two unique abilities. One is the entertainment. If you'd like to socialize with people in a face to face way most of the time, although people do it online as well, the other is that there's still a lot of types of communication that are best done in person. And, in fact, real estate value suggests that being able to be close toe other experts in your field. Whether it's in Silicon Valley, Hollywood, Wall Street is still a valuable asset. Eric and I >> travel a ton not always together. We could get a lot of our work done via email on via digital tools. When it comes time to actually get together and think about the next article or the next book, we need to be in the same room with the white bored doing it. Old school >> want to come back to the roots of innovation. Moore's law is Gordon Mohr put forth fiftieth anniversary next week, and it's it's It's coming to an end in terms of that actually has ended in terms of the way it's doubling every eighteen months, but looks like we still have some runway. But you know, experts can predict and you guys made it a point you book People always underestimate, you know, human's ability to do the things that people think they can't do. But the rial innovation is coming from this notion of combinatorial technologies. That's where we're going to see that continued exponential growth. What gives you confidence that that >> curve will continue? If you look at innovation as the work, not of coming up with some brand new Eureka, but as putting together existing building blocks in a new and powerful way, Then you should get really optimistic because the number of building blocks out there in the world is only going up with iPhones and sensors and banned weapon and all these different new tools and the ability to tap into more brains around the world to allow more people to try to do that recombination. That ability is only increasing as well. I'm massively optimistic about innovation, >> yet that's a fundamental break from the common attitude. We hear that we're using up all the low hanging fruit, that innovation. There's some fixed stock of it, and first we get the easy innovations, and then it gets harder and harder to innovate. We fundamentally disagree with that. You, in fact, every innovation we create creates more and more building blocks for additional innovations. And if you look historically, most of the breakthroughs have been achieved by combining previously existing innovations. So that makes me optimistic that we'LL have more and more of those building blocks going >> forward. People say that we've we've wrung all of the benefit out of the internal combustion engine, for example, and it's all just rounding error. For here. Know a completely autonomous car is not rounding error. That's the new thing that's going to change. Our lives is going to change our cities is going to change our supply chains, and it's making a new, entirely new use case out of that internal combustion. >> So you used the example of ways in the book, Really, you know, their software, obviously was involved, but it really was sensors and it was social media. And we're mobile phones and networks, just these combinations of technologies for innovation, >> none of which was an invention of the Ways team, none of which was original. Theyjust put those elements together in a really powerful way. >> So that's I mean, the value of ways isn't over. So we're just scratching the surface, and we could talk about sort of what you guys expect. Going forward. I know it's hard to predict well, another >> really important thing about wages in addition to the wake and combined and recombined existing components. It's available for free on my phone, and GPS would've cost hundreds of dollars a few years ago, and it wouldn't have been nearly as good at ways. And in a decade before that, it would have been infinitely expensive. You couldn't get it at any price, and this is a really important phenomenon. The digital economy that is underappreciated is that so much of what we get is now available at zero cost. Our GDP measures are all the goods and services they're bought and sold. If they have zero price, they show up is a zero in GDP. >> Wikipedia, right? Wikipedia, but that just wait here overvalue ways. Yeah, it doesn't. That >> doesn't mean zero value. It's still quite valuable to us. And more and more. I think our metrics are not capturing the real essence of the digital economy. One of the things we're doing at the Initiative initiative, the addition on the usual economy is to understand better what the right metrics will be for seeing this kind of growth. >> And I want to talk about that in the context of what you just said. The competitiveness. So if I get a piece of fruit disappears Smythe Digital economy, it's different. I wonder if you could explain that, >> and one of the ways it's different will use waze is an example here again, is network effects become really, really powerful? So ways gets more valuable to me? The more other ways er's there are out there in the world, they provide more traffic information that let me know where the potholes and the construction are. So network effects lead to really kind of different competitive dynamics. They tend to lead toward more winner, take all situations. They tend to lead toward things that look more not like monopolies, and that tends to freak some people out. I'm a little more home about that because one of the things we also know from observing the high tech industries is that today's near monopolist is yesterday's also ran. We just see that over and over because complacency and inertia are so deadly, there's always some some disruptor coming up, even in the high tech industries to make the incumbents nervous. >> Right? Open source. >> We'LL open source And that's a perfect example of how some of the characteristics of goods in the digital economy are fundamentally different from earlier eras and microeconomics. We talk about rival and excludable goods, and that's what you need for a competitive equilibrium. Digital goods, our non rival and non excludable. You go back to your micro economics textbook for more detail in that, but in essence, what it means is that these goods could be freely coffee at almost zero cost. Each copy is a perfect replica of the original that could be transmitted anywhere on the planet almost instantaneously, and that leads to a very different kind of economics that what we had for the previous few hundred years, >> or you don't work to quantify that. Does that sort of Yeah, wave wanted >> Find the effect on the economy more broadly. But there's also a very profound effects on business and the kind of business models that work. You know, you mentioned open source as an example. There are platform economics, Marshall Banal Stein. One of the experts in the field, is speaking here today about that. Maybe we get a chance to talk about it later. You can sometimes make a lot of money by giving stuff away for free and gaining from complimentary goods. These are things that >> way started. Yeah, Well, there you go. Well, that would be working for you could only do that for a little >> while. You'll like you're a drug dealer. You could do that for a little while. And then you get people addicted many. You start charging them a lot. There's a really different business model in the second machine age, which is just give stuff away for free. You can make enough off other ancillary streams like advertising to have a large, very, very successful business. >> Okay, I wonder if we could sort of, uh, two things I want first I want to talk about the constraints. What is the constraints to taking advantage of that? That innovation curve in the next day? >> Well, that's a great question, and less and less of the constraint is technological. More and more of the constraint is our ability as individuals to cope with change and said There's a race between technology and education, and an even more profound constraint is the ability of our organisations in our culture to adapt. We really see that it's a bottleneck. And at the MIT Sloan School, we're very much focused on trying to relieve those constraints. We've got some brilliant technologists that are inventing the future on the technology side, but we've got to keep up with our business. Models are economic systems, and that's not happening fast enough. >> So let's think about where the technology's aren't in. The constraints aren't and are. As Eric says, access to technology is vanishing as a constraint. Access to capital is vanishing as a constraint, at least a demonstrator to start showing that you've got a good idea because of the cloud. Because of Moore's law and a small team or alone innovator can demonstrate the power of their idea and then ramp it up. So those air really vanishing constraints are mindset, constraints, our institutional constraints. And unfortunately, increasingly, I believe regulatory constraints. Our colleague Larry Lessing has a great way to phrase the choice, he says, With our policies, with our regulations, we can protect the future from the past, or we could protect the past from the future. That choice is really, really write. The future is a better place. Let's protect that from the incumbents in the inertia. >> So that leads us to sort of some of the proposals that you guys made in terms of how we can approach this. Good news is, capitalism is not something that you're you're you're you're very much in favor of, you know, attacking no poulet bureau, I think, was your comments on DH some of the other things? Actually, I found pretty practical, although not not likely, but practical things, right? Yes, but but still, you know, feasible certainly, certainly, certainly intellectually. But what have you seen in terms of the reaction to your proposals? And do you have any once that the public policy will begin to shape in a way that wages >> conference that the conversation is shifting. So just from the publication date now we've noticed there's a lot more willingness to engage with these ideas with the ideas that tech progress is racing ahead but leaving some people behind in more people behind in an economic sense over time. So we've talked to politicians. We've talked to policy makers. We've talked to faint thanks. That conversation is progressing. And if we want to change our our government, you want to change our policies. I think it has to start with changing the conversation. It's a bottom out phenomenon >> and is exactly right. And that's really one of the key things that we learned, you know well, we talked to our political science friends. They remind us that in American other democracies, leaders are really followers on. They follow public opinion and the people are the leaders. So we're not going to be able to get changes in our policies until we change the old broad conversation. We get people recognizing the issues they're underway here, and I wouldn't be too quick to dismiss some of these bigger changes we describe as possible the book. I mean, historically, there've been some huge changes the cost of the mass public education was a pretty radical idea when it was introduced. The concept of Social Security were recently the concept of marriage. Equality with something I think people wouldn't have imagined maybe a decade or two ago so you could have some big changes in the political conversation. It starts with what the people want, and ultimately the leaders will follow. >> It's easy to get dismayed about the logjam in Washington, and I get dismayed once in a while. But I think back a decade ago, if somebody had told me that gay marriage and legal marijuana would be pretty widespread in America, I would have laughed in their face. And, you know, I'm straight and I don't smoke dope. I think these were both fantastic developments, and they came because the conversation shifted. Not not because we had a gay pot smoker in the white. >> Gentlemen, Listen, thank you very much. First of all, for running this great book, well, even I got one last question. So I understand you guys were working on your topic for you next, but can you give us a little bit of, uh, some thoughts as to what you're thinking. What do we do? We tip the hand. Well, sure, I think that >> it's no no mystery that we teach in a business school. And we spent a lot of time interacting with business leaders. And as we've mentioned in the discussion here, there have been some huge changes in the kind of business models that are successful in the second machine age. We want to elaborate on those describe nuts what were seeing when we talk to business leaders but also with the economic theory says about what will and what? What won't work. >> So second machine age was our attempt it like a big idea book. Let's write the Business guide to the Second Machine Age. >> Excellent. First of all, the book is a big idea. A lot of big ideas in the book, with excellent examples and some prescription, I think, for moving forward. So thank you for writing that book. And congratulations on its success. Really appreciate you guys coming in the Cube. Good luck today and we look forward to talking to in the future. Thanks for having been a real pleasure. Keep right. Everybody will be right back. We're live from London. This is M I t E. This is the cube right back