[Music] hello everyone welcome back to the live coverage here in monaco for the monaco crypto summit i'm john furrier host of thecube uh we have a great great guest lineup here already in nine interviews small gathering of the influencers and the people making it happen powered by digital bits sponsored by digital bits presented by digital bits of course a lot happening around decentralization web 3 the metaverse we've got a a powerhouse influencer on the qb ryan gills the founder of openmeta been in the issue for a while ryan great to see you thanks for coming on great to be here thank you you know one of the things that we were observing earlier conversations is you have young and old coming together the best and brightest right now in the front line it's been there for a couple years you know get some hype cycles going on but that's normal in these early growth markets but still true north star is in play that is democratize remove the intermediaries create immutable power to the people the same kind of theme has been drum beating on now come the metaverse wave which is the nfts now the meta verses you know at the beginning of this next wave yeah this is where we're at right now what are you working on tell us what's what's open meta working on yeah i mean so there is a reason for all of this right i think we go through all these different cycles and there's an economic incentive engine and it's designed in because people really like making money but there's a deeper reason for it all and the words the buzzwords the terms they change based off of different cycles this one is a metaverse i just saw it a little early you know so i recognized the importance of an open metaverse probably in 2017 and really decided to dedicate 10 years to that um so we're very early into that decade and we're starting to see more of a movement building and uh you know i've catalyzed a lot of that from from the beginning and making sure that while everything moves to a closed corporate side of things there's also an equal bottom-up approach which i think is just more important and more interesting well first of all i want to give you a lot of props for seeing it early and recognizing the impact and potential collateral damage of not not having open and i was joking earlier about the facebook little snafu with the the exercise app and ftc getting involved and you know i kind of common new york times guy comment online like hey i remember aol wanted to monopolize dial up internet and look the open web obviously changed all that they went to sign an extinction not the same comparable here but you know everyone wants to have their own little walled guard and they feel comfortable first-party data the data business so balancing the benefit of data and all the ip that could come into whether it's a visualization or platform it has to be open without open then you're going to have fragmentation you're going to have all kinds of perverse incentives how does the metaverse continue with such big players like meta themselves x that new name for facebook you know big bully tons of cash you know looking to you know get their sins forgiven um so to speak i mean you got google probably will come in apple's right around the corner amazon you get the whales out there how do is it proprietary is walled garden the new proprietary how do you view all that because it's it's still early and so there's a lot of change can happen well it's an interesting story that's really playing out in three acts right we had the first act which was really truly open right there was this idea that the internet is for the end user this is all just networking and then web 2 came and we got a lot of really great business models from it and it got closed up you know and now as we enter this sort of third act we have the opportunity to learn from both of those right and so i think web 3 needs to go back to the values of web one with the lessons in hindsight of web 2. and all of the winners from web 2 are clearly going to want to keep winning in web 3. so you can probably guess every single company and corporation on earth will move into this i think most governments will move into it as well and um but they're not the ones that are leading it the ones that are leading it are are just it's a culture of people it's a movement that's building and accumulating over time you know it's weird it's uh the whole web 2 thing is the history is interesting because you know when i started my podcasting company in 2004 there's only like three of us you know the dave weiner me evan williams and jack dorsey and we thought and the blogging just was getting going and the dream was democratization at the time mainstream media was the enemy and then now blogs are media so and then all sudden it like maybe it was the 2008 area with the that recession it stopped and then like facebook came in obviously twitter was formed from the death of odio podcasting company so the moment in time in history was a glimmic glimmer of hope well we went under my company went under we all went under but then that ended and then you had the era of twitter facebook linkedin reddit was still around so it kind of stopped where did it where did it pick up was it the ethereum bitcoin and ethereum brought that back where'd the open come back well it's a generational thing if you if you go back to like you know apple as a startup they were trying to take down ibm right it was always there's always the bigger thing that was that we we're trying to sort of unbundle or unpackage because they have too much power they have too much influence and now you know facebook and apple and these big tech companies they are that on on the planet and they're doing it bigger than it's ever been done but when they were startups they existed to try to take that from a bigger company so i think you know it's not an it's not a fact that like facebook or zuckerberg is is the villain here it's just the fact that we're reaching peak centralization anything past this point it becomes more and more unhealthy right and an open metaverse is just a way to build a solution instead of more of a problem and i think if we do just allow corporations to build and own them on the metaverse these problems will get bigger and larger more significant they will touch more people on earth and we know what that looks like so why not try something different so what's the playbook what's the current architecture of the open meta verse that you see and how do people get involved is there protocols to be developed is there new things that are needed how does the architecture layout take us through that your mindset vision on that and then how can people get involved yeah so the the entity structure of what i do is a company called crucible out of the uk um but i i found out very quickly that just a purely for-profit closed company a commercial company won't achieve this objective there's limitations to that so i run a dao as well out of switzerland it's called open meta we actually we named it this six months before facebook changed their name and so this is just the track we're on right and what we develop is a protocol uh we believe that the internet built by game developers is how you define the metaverse and that protocol is in the dao it is in the dow it's that's crucial crucible protocol open meta okay you can think of crucible as labs okay no we're building we're building everything so incubator kind of r d kind of thing exactly yeah and i'm making the choice to develop things and open them up create public goods out of them harness things that are more of a bottom-up approach you know and what we're developing is the emergence protocol which is basically defining the interface between the wallets and the game engines right so you have unity and unreal which all the game developers are sort of building with and we have built software that drops into those game engines to map ownership between the wallet and the experience in the game so integration layer basically between the wallet kind of how stripe is viewed from a software developer's campaign exactly but done on open rails and being done for a skill set of world building that is coming and game developers are the best suited for this world building and i like to own what i built yeah i don't like other people to own what i build and i think there's an entire generation that's that's really how do you feel about the owning and sharing component is that where you see the scale coming into play here i can own it and scale it through the relationship of the open rails yeah i mean i think the truth is that the open metaverse will be a smaller network than even one corporate virtual world for a while because these companies have billions of people right yeah every room you've ever been in on earth people are using two or three of facebook's products right they just have that adoption but they don't have trust they don't have passion they don't have the movement that you see in web3 they don't have the talent the level of creative talent those people care about owning what they create on the on what can someone get involved with question is that developer is that a sponsor what do people do to get involved with do you and your team and to make it bigger i mean it shouldn't be too small so if this tracks you can assume it gets bigger if you care about an open metaverse you have a seat at the table if you become a member of the dao you have a voice at the table you can make decisions with us we are building developing technology that can be used openly so if you're a game developer and you use unity or unreal we will open the beta this month later and then we move directly into what's called a game jam so a global hackathon for game developers where we just go through a giant exploration of what is possible i mean you think about gaming i always said the early adopters of all technology and the old web one was porn and that was because they were they were agnostic of vendor pitches or whatever is it made money they've worked we don't tell them we've always been first we don't tolerate vaporware gaming is now the new area where it is so the audience doesn't want vapor they want it to work they want technology to be solid they want community so it's now the new arbiter so gaming is the pretext to metaverse clearly gaming is swallowing all of media and probably most of the world and this game mechanics under the hood and all kinds of underlying stuff now how does that shape the developer community so like take the classic software developer may not be a game developer how do they translate over you seeing crossover from the software developers that are out there to be game developers what's your take on that it's an interesting question because i come to a lot of these events and the entire web 3 movement is web developers it's in the name yeah right and we have a whole wave of exploration and nfts being sold of people who really love games they're they're players they're gamers and they're fans of games but they are not in the skill set of game development this is a whole discipline yeah it's a whole expertise right you have to understand ik retargeting rigging bone meshes and mapping of all of that stuff and environment building and rendering and all these things it's it's a stacked skill set and we haven't gone through any exploration yet with them that is the next cycle that we're going to and that's what i've spent the last three or four years preparing for yeah and getting the low code is going to be good i was saying earlier to the young gun we had on his name was um oscar belly he's argo versus he's 25 years old he's like he made a quote i'm too old to get into esports like 22 old 25 come on i'd love to be in esports i was commenting that there could be someone sitting next to us in the metaverse here on tv on our digital tv program in the future that's going to be possible the first party citizenship between physical experience absolutely and meta versus these cameras all are a layer in which you can blend the two yeah so that that's that's going to be coming sooner and it's really more of the innovation around these engines to make it look real and have someone actually moving their body not like a stick figure yes or a lego block this is where most people have overlooked because what you have is you have two worlds you have web 3 web developers who see this opportunity and are really going for it and then you have game developers who are resistant to it for the most part they have not acclimated to this but the game developers are more of the keys to it because they understand how to build worlds yeah they do they understand how to build they know what success looks like they know what success looks like if you if you talk about the metaverse with anyone the most you'll hear is ready player one yeah maybe snow crash but those things feel like games yeah right so the metaverse and gaming are so why are game developers um like holding back is because they're like ah it's too not ready yet i'm two more elite or is it more this is you know this is an episode on its own yeah um i'm actually a part of a documentary if you go to youtube and you say why gamers hate nfts there's a two-part documentary about an hour long that robin schmidt from the defiant did and it's really a very good deep dive into this but i think we're just in a moment in time right now if you remember henry ford when he he produced the car everybody wanted faster horses yeah they didn't understand the cultural shift that was happening they just wanted an incremental improvement right and you can't say that right now because it sounds arrogant but i do believe that this is a moment in time and i think once we get through this cultural shift it will be much more clear why it's important it's not pure speculation yeah it's not clout it's not purely money there's something happening that's important for humanity yeah and if we don't do it openly it will be more of a problem yeah i totally agree with you on that silent impact is number one and people some people just don't see it because it's around the corner visionaries do like yourselves we do my objective over the next say three to six months is to identify which game developers see the value in web 3 and are leaning into it because we've built technology that solves interoperability between engines mapping ownership from wallets all the sort of blueprints that are needed in order for a game developer to build this way we've developed that we just need to identify where are they right because the loudest voices are the ones that are pushing back against this yeah and if you're not on twitter you don't see how many people really see this opportunity and i talked to epic and unity and nvidia and they all agree that this is where the future is going but the one question mark is who wants it where are they you know it's interesting i talked to lauren besel earlier she's from the music background we were talking about open source and how music i found that is not open it's proprietary i was talking about when i was in college i used to deal software you'd be like what do you mean deal well at t source code was proprietary and that started the linux movement in the 80s that became a systems revolution and then open source then just started to accelerate now people like it's free software is like not a big deal everyone knows it's what it was never proprietary but we were fighting the big proprietary code bases you mentioned that earlier is there a proprietary thing for music well not really because it's licensed rights right so in the metaverse who's the proprietary is it the walled garden is the is it is it the gamers so is it the consoles is it the investment that these gaming companies have in the software itself so i find that that open source vibe is very much circulating around your world actually open maps in the word open but open source software has a trajectory you know foundations contributors community building same kind of mindset music not so much because no one's it's not direct comparable but i think here it's interesting the gaming culture could be that that proprietary ibm the the state the playstation the xbox you know if you dive into the modding community right the modding community has sort of been this like gray area of of gaming and they will modify games that already exist but they do it with the values of open source they do it with composability and there's been a few breakthroughs counter-strike is a mod right some of the largest games of all time came from mods of other games look at quake had a comeback i played first multiplayer doom when it came out in the 90s and that was all mod based exactly yeah quake and quake was better but you know i remember the first time on a 1.5 cable mode and playing with my friends remember vividly now the graphics weren't that good but that was mod it's mod so then you go i mean and then you go into these other subcultures like dungeons and dragons which was considered to be such a nerdy thing but it's just a deeply human thing it's a narrative building collective experience like these are all the bottom-up type approaches modding uh world building so you're going to connect so i'm just kind of thinking out loud here you're going to connect the open concept of source with open meta bring game developers and software drills together create a fabric of a baseline somewhat somewhat collected platform tooling and components and let it just sell form see what happens better self form that's your imposing composability is much faster yeah than a closed system and you got what are your current building blocks you have now you have the wallet and you have so we built an sdk on both unity and unreal okay as a part of a system that is a protocol that plugs into those two engines and we have an inventory service we have an avatar system we basically kind of leaned into this idea of a persona being the next step after a pfp so so folks that are out there girls and boys who are sitting there playing games they could build their own game on this thing absolutely this is the opportunity for them entrepreneurs to circumvent the system and go directly with open meta and build their own open environment like i said before i i like to own the things i built i've had that entrepreneurial lesson but i don't think in the future you should be so okay with other companies or other intermediaries owning you and what you build i think i mean opportunity to build value yeah and i think i think your point the mod culture is not so much going to be the answer it's what that was like the the the the dynamic of modding yes is developing yes and then therefore you get the benefit of sovereign identity yeah you get the benefit of unbanking that's not the way we market this but those are benefits that come along with it and it allows you to live a different life and may the better product win yeah i mean that's what you're enabling yeah ryan thanks so much for coming on real final question what's going on here why are we here in monaco what's going on this is the inaugural event presented by digital bits why are we here monaco crypto summit i'm here uh some friends of mine brittany kaiser and and lauren bissell invited me here yeah i've known al for for a number of years and i'm just here to support awesome congratulations and uh we'll keep in touch we'll follow up on the open meta great story we love it thanks for coming on okay cube coverage continues here live in monaco i'm john furrier and all the action here on the monaco crypto summit love the dame come back next year it'll be great back with more coverage to wrap up here on the ground then the yacht club event we're going to go right there as well that's in a few hours so we're going to be right back [Music] you

>>Welcome everyone. I'm Dave Nicholson with the cube. This is a special Q conversation. That is part of the AWS startup showcase. Season two. Got a very interesting conversation on deck with Steve Francis who joins us from Instaclustr. Steve is the chief revenue officer and executive vice president for go-to-market operations for Insta cluster. Steve, welcome to the cube. >>Thank you, Dave. Good to be here. >>It looks like you're on a, uh, you're you're you're coming to us from an exotic locale. Or do you just like to have a nautical theme in your office? >>No, I'm actually on my boat. I have lots of kids at home and, uh, it can be very noisy. So, uh, we call this our apartment in the city and sometimes when we need a quiet place, this, this does nicely >>Well, fantastic. Well, let's, let's talk about Instaclustr. Um, first give us, give us a primmer on Instaclustr and, uh, and what you guys do. And then let's double click on that and go into some of the details. >>Sure. So in sip cluster, we offer a SAS platform for data layer, open source technologies. And what those technologies have in common is they scale massively. We re curate technologies that are capable of massive scale. So people use them to solve big problems typically. And so in addition to SAS offerings for those open source projects where people can provision themselves clusters in minutes, um, we also offer support for all of the technologies that we offer on our SAS platform. We offer our customer support contracts as well. And then we have a consulting team, a global consulting team who are expert in all of those open source projects that can help with implementations that can help with design health checks, uh, you name it. So most of what they do is kind of short term expert engagements, but we've also done longer-term projects with them as well. >>So your business model is to be a SAS provider as opposed to an alternative, which would be to, uh, provide what's referred to as, uh, open core software. Is that, is that right? >>Yeah, that's exactly right. So you, so when, when our customers have an interest in using community open source, we're the right partner for them. And so, you know, really what that means is if they, whether it's our SAS platform, if, if they want the flexibility to say, we want to take that workload off of your SAS platform, maybe at some point operated ourselves because we're not throwing a bunch of PROPRICER proprietary stuff in there. They have the flexibility to do that. So they always have an exit ramp without being locked in and with our support customers, of course, it's very easy. What we support is both the open source project. And if there's a gap in that open source project, what we'll do is rather than create a proprietary piece of software to close the gap, we'll source something from the community and we'll support that. Or if it, or if something does not exist in the community, in many cases, we'll write it ourselves and open source it and then, and then support it. >>Yeah, it's interesting. Uh, supposedly Henry Ford made a comment once that if you ask customers what they want, they'll tell you they want a faster horse, uh, but he was inventing the automobile and some people have, have likened open core to sort of the faster mechanical horse version of open source where you're essentially substituting an old school legacy vendor for a new school vendor. That's wrapping their own proprietary stuff around a delicious core of open source, but it sort of diminishes the value proposition of open source. It sounds like that's, that's the philosophy that you have adopted at this point. That's >>I love that story. I haven't heard that before. One that I like, uh, you know, matching metaphor for metaphor, uh, is, uh, the, um, is the Luddites, right? You know, the Luddites didn't want to lose their weaving jobs. And so they would smash weeding looms and, um, you know, to, to protect their reading jobs. And I think it's the same thing with the open core model they're protecting, uh, you know, they're creating fear, uncertainty and doubt about open, open sourcing. Oh, it isn't secure. And, you know, the, those, those arguments have been used for 15 years or 20 years. And, you know, maybe 15 years ago there were some truth to it. But when you look at who is using open source community open source now for huge projects, you know, if you just do a search for Apache coffee users and go to the Apache Apache website, you know, it's kind of the who's who in big business, and these are people using community open source. And so, um, a lot of the fear and uncertainty and doubt is still used, and it's just, you know, it's just kind of hanging on to a business model that isn't really it's for the benefit of the, of the vendor and not the benefit of the customer. >>Well, so I can imagine being a customer and realizing several years into an open core journey that I basically painted myself into a similar corner that I was in before. Um, and so I can see where that, you know, that can be something that is a realization that, that creeps up over time from a customer perspective, but from your business model perspective, um, if I'm understanding correctly, your, when you scale, you're scaling the ability to, um, take over operations for our customer, uh, that, that some level, I'm sure you've got automation involved in this. Uh, but at some level you've got to scale in terms of really smart people, um, has that limited your ability to scale. So first talk about what have the results been. You guys we've been covering you since 2018. What have your results been over time and has that sort of limited that that limit to your scalability, uh, been an issue at all. >>It's hard to find people, uh, it's hard, it's hard for our customers to find people and it's hard for us to find people. So we have an advantage for two reasons. Number one, we have a really good process for hiring people, hiring graduates, recent computer science graduates typically, and then getting them trained up and productive on our platform and within a pretty short timeframe of three or four months. And, um, you know, so we we've, we've, uh, we have a really well-proven process to do that. And then the other thing that you've already alluded to is automation, right? There's a ton of automation built into our platform. So we have a big cost advantage over our customers. So, you know, our, our customers, you know, if they want to go hire a seasoned, you know, Kafka person or PostGrest personal work, a person, these people are incredibly expensive in the market, but for us, we can get those people for relatively less expensive. And then with the automation that we have built into our platform to do all the operational tasks and handle all the operational burdens on those different open source projects, it's a lot of it's automated. And so, uh, you know, where one of our experts can use, you know, the number of workloads that they can operate is usually, you know, many times more than what someone could do without all of the operational capability or all the automated capabilities that we have. >>So what has your, what is your plan for scaling the business look like into the future? Is it a additional investment in those core operators? Uh, are you looking at, uh, uh, expansion, geographically acquisition? What, what can you share with us? >>We've done some acquisition. We added a Postgres capability. We recently added a last, further Alaska search capability and really buttressed our capabilities there. I think we'll do more of that. And, um, we, we will continue to add technologies that we find interesting and, and federal model, usually what we look for technologies that are pretty popular. They're used to solve big problems and they're complicated to manage, right? If something's easy to manage, people are less likely to perceive our value to be that great. So we look for things that, um, you know, are we kind of take the biggest areas, gnarliest, um, open-source projects for people to manage, and we handle the heavy lifting. >>Well, can you give me an example of something like that? You don't have to, you don't have to share a customer name if you don't, if it's not appropriate, but give us a, give us an example of, of Instaclustr inaction pretend I'm the customer. And, uh, and, uh, you know, you mentioned elastic search. Let's say that, let's say that that is absolutely something that's involved. And I have a choice between some open, open core solution and throwing my people at it to manage it, uh, and, and, and operate at the data layer, uh, versus what you would do. What does that interaction look like? How do, how does the process, >>Um, so one thing that we hear from elastic search customers a lot is, uh, their customers, some of them are unhappy. And what they'll tell us is look, when we get an operational problem with Alaska search, we go to Alaska search. And the answer we get from them is we gotta buy, you know, you gotta buy more stuff, you got to add more nodes, and they're in the business of, uh, you know, that's, that's our business. And, uh, you know, they do have a SAS offering, but, um, you know, they're, they're also in the business of selling software. And so when those customers, those same customers come to us, our answer is often, well, Hey, we can help you optimize your environment. And, you know, a lot of times when we onboard people into our platform, they'll achieve cost savings because maybe they weren't on the cloud. Maybe they weren't completely optimized there. And, um, you know, we want to make sure that they get a good operational experience and that's how we felt lock customers in, right. We don't lock them in with code. We make sure that they have a positive experience that we take a lot of that operational stuff off their hands. And so there's just a good natural alignment between what we want to provide that customer and what they ultimately want to consume. Uh, you know, that, that alignment I think is, is uniquely high within our business. >>Well, so how, how have things changed just in the last several years? Obviously, I mean, you know, the, the pandemic has, has affected everything in, in one way or another, but, but in terms of things that live at the data layer being important, um, I mean, just in the last three or four years, the talk of various messaging interfaces and databases has shifted to a degree. Um, what do you see on the horizon? What's, what's, what's, what's getting buzz that maybe didn't get buzz a year ago. What, what, what are you looking for as well? If you're out looking for people with skill sets right now, what are those skill sets you're hiring to? >>I don't hire engineers, right. I run the go to market organization. I hire marketers, salespeople, consultants, but, uh, so it's probably different. I'm maybe not the best person to ask from an engineering standpoint, but, uh, your question about the data layer, um, and how, you know, that's evolving trends that we see it's becoming increasingly strategic. You know, every, there's a couple of buzzwords out there that, you know, for years now, people have been talking about, um, modernization, digital transformation, stuff like that, but, you know, there's, there's a lot to it like digital, you know, every business kind of needs to become a digital business. And as that happens, the amount of data that's produced is, is just as mushrooming, right. You know, the amount of data on the planet doubles about every two years. And so for a lot of applications for a lot of enterprise mission-critical applications, data is the most expensive layer of the application. >>You know, much more expensive than delivering a front end, much more expensive than delivering a military when you just, when you factor in storage, um, uh, just the kind of moving data in and out, you know, data transfer fees, the cost of engineering resources that it's, it's incredibly expensive. So data layers are becoming strategic because organizations are looking at it and realizing, you know, the amount that they're spending on this is eye-popping. And so that's why it's becoming strategic. It's on the radar, just due to the, uh, the size of bills that organizations are looking at. Um, and we could drive those bills down. You know, our value proposition is really simpler. It's a better, faster, cheaper, and we eliminate the license fees. We can, you know, we are operational experts, so we can get people architected in the cloud more efficiently, and probably about a third of the time we save our customers cloud fees. Um, so it's, you know, it's a pretty simple model that some of those things that are strategically more, or are there, sorry, traditionally more tactical or becoming strategic, just because of the scope and scale of them. >>We, uh, we're having this conversation as part of the AWS startup showcase, which basically means that AWS said, Hey, Silicon angle, have your cube guys go talk to these people because we think they're cool. So, um, so why, why, why do they think you're cool? Are you a wholly owned subsidiary of AWS? Did you, did you and your family, uh, uh, exceed the 300 order, uh, Amazon threshold last year? Y what's your relationship with Amazon? >>I bought an elf on the shelf from, I don't know, I don't know why. Um, you know, we're, we're growing fast and we're, we're growing north of 50% last year in 21 and closer to 60%. Um, you know, we certainly, I think, uh, when our customers sign up for our services, you know, Amazon gets more workloads. That's, that's probably a positive thing for Amazon. Um, we're certainly not, you know, there's much, much, much bigger vendors and partners than us that they have, but, uh, but you know, they're, I think they're aware that there's, there's some, some of the smaller vendors like us will grow up to be, you know, the, you know, the bigger vendors of tomorrow. Um, but they've kind of, they've been a great partner. You know, we, we support multiple, we do support multiple clouds, and Amazon's cool with that. You know, we support GCP, we support Azure and kind of give our customers the choice of what clouds they want to run on. Uh, most of our customers do run an Amazon that seems to be sort of a defacto standard, but, um, they haven't been a great partner, >>But, but AWS, it's not a dependency. Uh, if you're, if you're working within the cluster, it doesn't mean that you must be in AWS. >>Nope. We can support customers. Uh, that's a great question. So we can support customers and multiple clouds, and we even support them on prem, right? If they, if organizations that have their own data center, we actually have an on-premise managed service offering. And if that's not a fit, we even have, um, we can offer support contracts, like if they want to do it themselves and do a lot of the heavy lifting and just need sort of a red phone for emergency situations. Uh, we offer 24 by 7, 365 support with 20 minutes service levels for urgent issues. >>So your chief revenue officer, that means that you write the code that runs operations in your system. I'm not smiling, but I'm at, but I'm, but I am actually joking. So that's what the dry sense of humor. Uh, but, but, but seriously, let's talk about the business end of this, right? We have, uh, we have a lot of folks who, uh, who tuned into the queue because of the technology aspect of it, but let's talk about your, your growth trajectory over time. Um, uh, this isn't a drill down. I'm not asking for your, your pipeline, Steve, but, uh, but, but, you know, give us an idea of what that trajectory has looked like. Um, what's going on. >>Yeah. I mean the most recent year, you know, we're, we're getting, uh, to be, um, I, I don't know what I'm permitted to share expect, but I, you know, we've, we've had a lot of growth, you know, if we've won a couple, a couple of hundred percent, our revenue has in the amount of time that I've been here, which is three years, and we're the point now, or pretty good size. Uh, and that gives us, uh, it's cool. It's exciting. You know, we're, we're noticing in the market is people who traded two years ago. People, no one knew who we were. And now we're beginning to talk to some partners, some resellers, some customers, and they will say things like, oh yeah, we've heard of you. We didn't know what you did, but we've heard of you. And, you know, that's, that's fun. That's a great place to be. Uh, you know, it becomes a little bit self-sustaining at that point. And, um, we, you know, we are about to launch, I, it's not a secret because this isn't public preview. So I think >>Was there, I noticed the pause where you're like, can I say this or not? Go ahead and say, go ahead and say, >>Really we, uh, I was trying to think, wait, am I revealing anything here? I shouldn't. But, uh, we did just go public preview, uh, probably a month ago with a project called Aiden's, uh, cadence workflow. Uh, you can actually, um, go to the Instaclustr website and look up cadence. Um, it's run their homepage, or you can, if you want to go to the open source project itself, you can go to cadence, workflow.io. Uh, this is a project that's trending pretty highly on Google. It's got a lot of important movers in the technology business that are using it and having a lot of success with it. Uh, and we're going to be first to market globally with a SAS offering for cadence, port flop. And, um, it's an incredibly exciting project. And it's exciting for us to specifically, because it's a little different, right? It's not, it's a middle tier project that is targeted at developers to increase developer productivity and developer velocity. >>Um, you joked about my being a CRO writing code, but I actually used to be a coder long time ago. I was not very good at it, but what I did enough of it to remember that a lot of what I did as a coder was right. Plumbing code, you know, rather than writing that code that makes the business application function a huge amount of my time as a developer was spent writing, you know, just the plumbing code to make things work and to make it secure and to make a transactional and just all that, you know, kind of nitty gritty code that you gotta do in a nutshell, cadence makes writing that code way easier. So especially for distributed applications that have workflow like capabilities requirements, uh, it's a massive productivity and PR increaser. So it's cool. Exciting for us is now we can, rather than just target data operators, we can actually target developers and engage, not just at the data layer, but kind of at that middle tier as well, and begin to, uh, identify and, um, uh, synergies between the different services that we have and, and our customers will obviously benefit from that. >>So that's a big part of our growth strategy. >>Yeah. So more, more on from a business perspective and a go to market perspective. Um, what is your, what is your go to market strategy or, uh, do you have, do you have a channel strategy? Are you working with partners? >>He is pretty nascent. You know, our go to market strategy for the most part has been, you know, we, uh, pay the Google gods and, and lots of people come to our website and say, they want to talk to us. You know, we talked to them and we get them signed up with, uh, uh, on our, our, our SAS platform or with a support contract or with our consulting team. Um, we also do outbound, you know, we do, we have an inside sales team that does outbound prospecting and we have, um, and we also have some self-service. We have some, some self service customers as well that just, you know, anyone can go to our website, swipe a credit card, sign up for one of our SAS offering and begin, literally get fired up in minutes and PR and using the platform. Uh, so, you know, it's a bit of a mix of high touch, low touch, I think are, you know, we have tons of big logos. >>We know lots and lots of our customers are household name, really big organizations solving big problems. And, um, that's kind of where the bulk of our businesses. And so I think we've been a little more focused there and go to market than we have sort of a know startup selling to startups and the people that just from super developer focused, wanting low touch. So, but I think we need to do better at that part of the market. And we are investing some resources there so that, you know, we're not so lopsided at the high end of the market. We want kind of a, more of a balanced approach because, you know, some of those, some of those, um, younger companies are going to grow up to be big massively successful companies. We've had that, you know, door dash is a tough class, has been a customer of ours for years, and they were not nearly, you know, we, there were a prepayment, there were custom bars, pre pandemic, and we all know what happened to them, uh, during the pandemic. And so, you know, we know there's other door dashes out there. >>Yeah. Yeah. Uh, uh, final question, geography, uh, you guys global. I, uh, I know you're in north America, but, um, what, what, what does that look like for you? Where are you at? >>We're super global. So, you know, in my go-to-market organization, we have sellers in, um, uh, AsiaPac and Europe, you know, multiple in Asia, multiple in Europe, uh, you know, lots of lots in the, in the states, uh, same with marketing, uh, same with engineering, same with our tech ops delivery team. We have most of them, uh, in Australia, which is where we were founded. Uh, but we also have a pretty good sized team, uh, out of Boston and, um, kind of a nascent team, uh, in India as well, to help to tell it, to help them out. So yeah, very much global and, um, you know, getting close to 300 employees, um, you know, when I started, I think we're about 85 to 90, >>That's it, that's an exciting growth trajectory. And, uh, I'm just going to assume, because it just feels awesome to assume it that since you're on a boat and since you were founded in Australia, that that's how you go back and forth to, uh, to visit the most. >>Yeah. Yeah. It takes a while. It takes a while. >>So with that, Steve, I want to say a smooth sailing and, uh, and, uh, thanks for joining us here on the cube. I'm Dave Nicholson. Uh, this has been part of the AWS startup showcase my conversation with Steve Francis of Instaclustr again. Thanks Steve. Stay tuned. >>Thanks very much to you, >>Your source for hybrid tech coverage.

>>Hey, we're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, it's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great >>To see you take it away. >>All right, guys. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing and flute and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution, things got interesting, right? You started to see automation, but that automation was done essentially programmed your robot to do something and did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different, right? >>Cause now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue, there we'll issue that, but it's important. Not for technology's sake, right? It's important because it actually drives very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, uh, companies and manufacturers moving to improve while its quality prompts still accounts for 20% of sales, right? So every fifth of what you meant are manufactured from a revenue perspective, do back quality issues that are costing you a lot planned downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of new spaces, we're not doing it just merely to implement technology. We're doing it to move these from members, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life with what like, right, but this is actually the business. The cloud area is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I say, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things are taking about time, but this, the ability to take these real-time actions or, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into an enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you could start to think about, you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we can put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one history sets data, you can build out those machine learning models. >>I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. Once you understand that you can actually then build out the smiles, you could deploy the models after the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, but schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. >>So, >>You know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is for SIA for ECA is the, um, is the, was, is the, um, the, uh, a supplier associated with Pooja central line out of France. They are huge, right? This is a multinational automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, um, they connected 2000 machines, right. Um, and they once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor the data firms coming in, you know, monitor the process. >>That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, fibrations pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision, wilding inspection. So let's take pictures of parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections beer. And so they both have those machine learning models. So they took that data. All this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case, a great example of how you can start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you wanted to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turn in the morning sessions and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're gonna, they're gonna hit? >>You know, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right. So, and it's unsafe, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. >>Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world for a long time, the silos, um, uh, you know, the silos, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid theme and you've kind of got this world, that's going toward an equilibrium. You've got the OT side, you know, pretty hardcore engineers. And we know, we know it. Uh, a lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space. And when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to it earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims, kick kickoff. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots by about warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning where simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start something with monitoring, get a lot of value, start, then bring together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases there's value to be had throughout. I >>Remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question that it kind of, um, goes back to one of the things I alluded earlier, we've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they've built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Patera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of industry 4.0, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to lead this discussion on the technology advances. I'd love to talk tech here, uh, are the key technology enablers, and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space, sorry, manufacturing in >>A factory space. Yeah. I knew what you meant in know in the manufacturing space. There's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can w we're finally being able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got back way capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, very much more quickly. Yep. We got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, for everybody who joined us. Thanks. Thanks for joining.

>>Welcome to our industry. Drill-downs from manufacturing. I'm here with Michael Gerber, who is the managing director for automotive and manufacturing solutions at cloud era. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data connected trucks are fundamental to optimizing fleet performance costs and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're gonna come back and have a little conversation about what we just heard. Michael, great to see you over to you. >>Oh, thank you, Dave. And I appreciate having this conversation today. Hey, um, you know, this is actually an area connected trucks. You know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because, you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022. Um, and the reasons, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made truck makers providing benefits to fleet operators. And if you look at the F the top fleet operator, uh, the top benefits that fleet operators expect, you see this in the graph over here. >>Now almost 80% of them expect improved productivity, things like improved routing rates. So route efficiencies and improve customer service decrease in fuel consumption, but better technology. This isn't technology for technology sake, these connected trucks are coming onto the marketplace because Hey, it can provide for Mendez value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable this right, um, trucks are becoming connected because at the end of the day, um, we want to be able to provide fleet deficiencies through connected truck, um, analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle analytics machine learning life cycle, and to do that, you need to do a few different things, right? >>You start off of course, with connected trucks in the field. And, you know, you can have many of these trucks cause typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first you need to be able to do is connect to those products, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you conducted the, um, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you the ability to take this real-time action is actually the result of your machine learning license. Let me explain to you what I mean by that. >>So we have this trucks, we start to collect data from it right at the end of the day. Well we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected collected from those trucks. And you want to augment that with your dealership, say service information. Now you have, you know, you have sensor data and there was salting repair orders. You're now equipped to do things like predict one day maintenance will work correctly for all the data sets that you need to be able to do that. >>So what do you do here? Like I said, you adjusted your storage, you're enriching it with data, right? You're processing that data. You're aligning say the sensor data to that transactional system data from your, uh, from your, your pair maintenance systems, you know, you're bringing it together so that you can do two things you can do. First of all, you could do self-service BI on that date, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor right values and the need, for example, for, for a dealership repair, or as you could start to correlate, which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time action. >>I mentioned earlier as that data that then comes through in real-time, you're running it against that model, and you can take some real time actions. This is what we are, this, this, this, this analytics and machine learning model, um, machine learning life cycle is exactly what Cloudera enables this end-to-end ability to ingest, um, stroke, you know, store it, um, put a query, lay over it, um, machine learning models, and then run those machine learning models. Real-time now that's what we, that's what we do as a business. Now when such customer, and I just wanted to give you one example, um, a customer that we have worked with to provide these types of results is Navistar and Navistar was kind of an early, early adopter of connected truck analytics. And they provided these capabilities to their fleet operators, right? And they started off, uh, by, um, by, you know, connecting 475,000 trucks to up to well over a million now. >>And you know, the point here is with that, they were centralizing data from their telematics service providers, from their trucks, from telematics service providers. They're bringing in things like weather data and all those types of things. Um, and what they started to do was to build out machine learning models, aimed at predictive maintenance. And what's really interesting is that you see that Navistar, um, made tremendous strides in reducing the need or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring and service it before it broke down so that you could do that in a much more cost-effective manner. And if you see the benefits, right, they, they reduced maintenance costs 3 cents a mile, um, from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. >>So this was a tremendous success for Navistar. And we're seeing this across many of our, um, um, you know, um, uh, truck manufacturers. We were working with many of the truck OEMs and they are all working to achieve, um, you know, very, very similar types of, um, benefits to their customers. So just a little bit about Navistar. Um, now we're gonna turn to Q and a, Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our lives or to our website, what you see up, uh, up on the screen, there's the URLs cloudera.com for slash solutions for slash manufacturing. And you'll see a whole slew of, um, um, lateral and information, uh, in much more detail in terms of how we connect, um, trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >>Thank you. Uh, Michael, that's a great example. You've got, I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that lifecycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into. >>Yeah, that's really, that's a great question. They, you know, cause you know, everybody always thinks about machine learning. Like this is the first thing you go, well, actually it's not right for the first thing you really want to be able to go around. Many of our customers are doing slow. Let's simply connect our trucks or our vehicles or whatever our IOT asset is. And then you can do very simple things like just performance monitoring of the, of the piece of equipment in the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how has the, how has the driver performing? Is there a lot of idle time spent, um, you know, what's, what's route efficiencies looking like, you know, by connecting the vehicles, right? You get insights, as I said into the truck and into the driver and that's not machine learning. >>Right. But that, that, that monitoring piece is really, really important. The first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, uh, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like, uh, predictive maintenance happening, um, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making, but let's not, let's not minimize the value of good old fashioned monitoring that site to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >>You know, it's interesting. I'm, I'm envisioning when you talked about the monitoring, I'm envisioning a, you see the bumper sticker, you know, how am I driving this all the time? If somebody ever probably causes when they get cut off it's snow and you know, many people might think, oh, it's about big brother, but it's not. I mean, that's yeah. Okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization, I mean, that's, that's bottom line business value. So, so that's, I love those, uh, those examples. Um, I wonder, I mean, one of the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into, uh, you know, the blind spots they're, they're going to, they're going to get hit with, >>There's a few different things, right? So first of all, a lot of times your it folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? That's very specialized hardware in the car and things like that. And protocols that's number one, that that's the classic, it OT kind of conundrum that, um, you know, uh, many of our customers struggle with, but then more fundamentally is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. And as you move towards, um, more commercial solutions, you had what I call the silo, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor, you get the idea, you know, one of the things that we really think that we need with that, that needs to be brought to the table is first of all, having an end to end data management platform, that's kind of integrated, it's all tested together. >>You have the data lineage across the entire stack, but then also importantly, to be realistic, we have to be able to integrate to, um, industry kind of best practices as well in terms of, um, solution components in the car, how the hardware and all those types things. So I think there's, you know, it's just stepping back for a second. I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of art, um, offerings. Um, our job as a software maker is to make that easier and connect those dots. So customers don't have to do it all on all on their own. >>And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about, you know, new types of hardware coming in, you guys are optimizing for that. We see the it and the OT worlds blending together, no question. And then that end to end management piece, you know, this is different from your right, from it, normally everything's controlled or the data center, and this is a metadata, you know, rethinking kind of how you manage metadata. Um, so in the spirit of, of what we talked about earlier today, uh, uh, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >>Yeah, I'm really glad you're asking that because we actually embarked on a product on a project called project fusion, which really was about integrating with, you know, when you look at that connected vehicle life cycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Cloudera is Peter piece of this was ingesting data and all the things I talked about being storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners and the partners that we did with, um, integrate with or NXP NXP provides the service oriented gateways in the car. So that's a hardware in the car when river provides an in-car operating system, that's Linux, right? >>That's hardened and tested. We then ran ours, our, uh, Apache magnify, which is part of flood era data flow in the vehicle, right on that operating system. On that hardware, we pump the data over into the cloud where we did them, all the data analytics and machine learning and, and builds out these very specialized models. And then we used a company called Arabic equity. Once we both those models to do, you know, they specialize in automotive over the air updates, right? So they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, um, you know, uh, ecosystem, if you will, of leaders in this space, what we wanted to do is make sure that our, there was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working closely with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some further acceleration in the machine learning side of things. So, uh, yeah, you know, one of the things I always say about this types of use cases, it does take a village. And what we've really tried to do is build out that, that, uh, an ecosystem that provides that village so that we can speed that analytics and machine learning, um, lifecycle just as fast as it can be. This >>Is again another great example of, of data intensive workloads. It's not your, it's not your grandfather's ERP. That's running on, you know, traditional, you know, systems it's, these are really purpose-built, maybe they're customizable for certain edge use cases. They're low cost, low, low power. They can't be bloated, uh, ended you're right. It does take an ecosystem. You've got to have, you know, API APIs that connect and, and that's that, that takes a lot of work and a lot of thoughts. So that, that leads me to the technologies that are sort of underpinning this we've talked we've we talked a lot in the cube about semiconductor technology, and now that's changing and the advancements we're seeing there, what do you see as the, some of the key technical technology areas that are advancing this connected vehicle machine learning? >>You know, it's interesting, I'm seeing it in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about that used to be kind of a, a dumb gateway. That was really all it was doing was pushing data up and down and provided isolation, um, as a gateway down to the, uh, down from the lower level subsistence. So it was really security and just basic, um, you know, basic communication that gateway now is becoming what they call a service oriented gate. So it can run. It's not that it's bad desk. It's got memories that always, so now you could run serious compute in the car, right? So now all of these things like running machine learning, inference models, you have a lot more power in the corner at the same time. >>5g is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now you now you've got credible compute both at the edge in the vehicle and on the cloud. Right. And, um, you know, and then on the, you know, on the cloud, you've got partners like Nvidia who are accelerating, it's still further through better GPU based compute. So I mean the whole stack, if you look at it, that that machine learning life cycle we talked about, no, David seems like there's improvements and EV every step along the way, we're starting to see technology, um, optimum optimization, um, just pervasive throughout the cycle. >>And then real quick, it's not a quick topic, but you mentioned security. If it was seeing a whole new security model emerge, there is no perimeter anymore in this use case like this is there. >>No there isn't. And one of the things that we're, you know, remember where the data management platform platform and the thing we have to provide is provide end-to-end link, you know, end end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into from the beginning of when that data is ingested through, when it's stored through, when it's kind of processed and people are doing machine learning, we provide, we will provide that lineage so that, um, you know, that security and governance is a short throughout the, throughout the data learning life cycle, it >>Federated across in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it, >>Dave. Thank you. And thank you. Thanks for the audience for listening in today. Yes. Thank you for watching. >>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime. And look, when you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces loss opportunities. Michael. Great to see you >>Take it away. All right. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right. And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, massive assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done, essentially programmed a robot to do something. It did the same thing over and over and over irrespective about it, of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfast. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adaptive right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives and very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, of, uh, companies, um, and manufacturers moving to improve while its quality promise still accounted to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. >>Plant downtime, cost companies, $50 billion a year. So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just merely to implement technology. We're doing it to move these from drivers, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle, what like, right, because this is actually the business that cloud era is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI, this, this analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors have connected over the internet. So suddenly we can collect all this data from your, um, ma manufacturing plants. What do we want to be able to do? >>You know, we want to be able to collect it. We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking the time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You've got, you're going to ingest that data. >>You're going to store it. You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. >>But as I mentioned, you, and what's really important here is the fact that once you've stored long histories that say that you can build out those machine learning models I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need, a correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for Maples. Once you understand that you can actually then build out those models for deploy the models out the edge, where they will then work in that inference mode that we talked about, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that PR that predicted the need for maintenance? If so, let's take real-time action, right? >>Let's schedule a work order or an equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connecting connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is bought for Russia, for SIA, for ACA is the, um, is the, was, is the, um, the, uh, a supplier associated with Peugeot central line out of France. They are huge, right? This is a multi-national automotive parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. >>Um, and then once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor data firms coming in, you know, monitor the process. That was the first step, right. Uh, and, you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models or compute. And what they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad Bali outcome. Then you teach the machine to make that decision on its own. >>So now, now the machine, the camera is doing the inspections. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you can start with monitoring, moved to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing, a lot more detail, and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the cost, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit, >>You know, there's, there's, there, there's a few of the, but I think, you know, one of the ones, uh, w one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant, are running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietorial pro protocols. That information can be very, very difficult to get to. Right. So, and it's, it's a much more unstructured than from your OT. So th the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world. And for a long time, the silos, um, uh, the silos a, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge, >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So, Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right. And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, but just talking about simple monitoring next level down, and we're seeing is something we would call quality event forensic analysis. >>And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims kick up. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots. What about warranty issues? What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning, we're simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole slew of machine learning, use dates, you know, and that ranges from things like Wally or say yield optimization. >>We start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. And you're certain start to say, which, um, you know, which on a sensor values or factors drove good or bad yield outcomes, and you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with, with monitoring, get a lot of value, start then bringing together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases, there's this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and the new player would come in and he'd be perfectly white uniform, and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so I question it relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. And it kind of goes back to one of the things I alluded to alluded upon earlier. We've had some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of, um, industry for porno, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and, and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to li lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry, manufacturing. Yeah. >>Yeah. I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can, we've finally been able to get to the OT data, right? That's that's number one, you know, numb number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, the super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed a book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to, to your equipment. All of those things are making this, um, there's, you know, the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, uh, very much more quickly. Yeah, we got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined us. Thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great to see you, >>Dave. All right, guys. Thank you so much. So I'll tell you, we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done essentially programmed a robot to do something. It did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfasts. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives very important business outcomes. First of all, falling, right? If you look at the cost of quality, even despite decades of, of, uh, companies and manufacturers moving to improve while its quality prompts still account to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. Plant downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just narrowly to implement technology. We're doing it to move these from adverse, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle with what like, right. But so this is actually the business that cloud areas is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics, life something, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking that time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right? And that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you bring these datasets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one histories that say that you can build out those machine learning models I talked to you about earlier. >>So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And then you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. And once you understand that you can actually then build out those models, you deploy the models out to the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, right? Let's schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that a piece of equipment fails and allows us to be very, very proactive. >>So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different, um, manufacturers around the world. I want to just highlight one of them. Cause I thought it's really interesting. This company is bought for Russia. And for SIA for ACA is the, um, is the, is the, um, the, uh, a supplier associated with out of France. They are huge, right? This is a multi-national automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. Um, I mean at once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? >>To be able to just monitor the data from coming in, you know, monitor the process. That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things, just start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections for you. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go, then you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of where the data is, you've got to be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit? >>No, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES system, Freightos your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right? So, and it's uncertain, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own. And for a long time, the silos, the silos, a bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. This is Chris now is getting, you know, instrumented and captured. Uh, and so you've got that, that cultural challenge and, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a great, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about this, a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards the internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I'm got warranty plans in the, in the field, right? So I'm starting to see warranty claims kicked off on them. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots I've got, I've got warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of a car. So, and that, again, also not machine learning is simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day so that you could take corrective actions, but then you get into a whole slew of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with monitoring, get a lot of value, start, then bring together more diverse datasets to do things like connect the.analytics then all and all the way then to, to, to the more advanced machine learning use cases this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. I kind of, um, goes back to one of the things I alluded a little bit about earlier. We've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to get to practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to implement those types of, um, industry 4.0, uh, analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, uh, barrier that we've always had and, and bring together those data sets that really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to Lee lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry. Manufacturing in >>Factor space. Yeah, I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and it had become ubiquitous that number one, we can w we're finally been able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, um, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book, bullet, uh, GP, you know, GPU level, machine learning, all that, those models, and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processes are getting much smarter, uh, very much more quickly. >>Yep. We've got a lot of data and we have way lower costs, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined. Uh, thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Cloud computing has been the single most transformative force in IT over the last decade. As we enter the 2020s, we believe that cloud will become the underpinning of a ubiquitous, intelligent and autonomous resource that will disrupt the operational stacks of virtually every company in every industry. Welcome to this week's special edition of Wikibon's CUBE Insights Powered by ETR. In this breaking analysis, and as part of theCUBE365's coverage of AWS re:Invent 2020, we're going to put forth our scenario for the next decade of cloud evolution. We'll also drill into the most recent data on AWS from ETR's October 2020 survey of more than 1,400 CIOs and IT professionals. So let's get right into it and take a look at how we see the cloud of yesterday, today and tomorrow. This graphic shows our view of the critical inflection points that catalyze the cloud adoption. In the middle of the 2000s, the IT industry was recovering from the shock of the dot-com bubble and of course 9/11. CIOs, they were still licking their wounds from the narrative, does IT even matter? AWS launched its Simple Storage Service and later EC2 with a little fanfare in 2006, but developers at startups and small businesses, they noticed that overnight AWS turned the data center into an API. Analysts like myself who saw the writing on the wall and CEO after CEO, they poo-pooed Amazon's entrance into their territory and they promised a cloud strategy that would allow them to easily defend their respective turfs. We'd seen the industry in denial before, and this was no different. The financial crisis was a boon for the cloud. CFOs saw a way to conserve cash, shift CAPEX to OPEX and avoid getting locked in to long-term capital depreciation schedules or constrictive leases. We also saw shadow IT take hold, and then bleed in to the 2010s in a big way. This of course created problems for organizations rightly concerned about security and rogue tech projects. CIOs were asked to come in and clean up the crime scene, and in doing so, realized the inevitable, i.e., that they could transform their IT operational models, shift infrastructure management to more strategic initiatives, and drop money to the bottom lines of their businesses. The 2010s saw an era of rapid innovation and a level of data explosion that we'd not seen before. AWS led the charge with a torrent pace of innovation via frequent rollouts or frequent feature rollouts. Virtually every industry, including the all-important public sector, got into the act. Again, led by AWS with the Seminole, a CIA deal. Google got in the game early, but they never really took the enterprise business seriously until 2015 when it hired Diane Green. But Microsoft saw the opportunity and leaned in heavily and made remarkable strides in the second half of the decade, leveraging its massive software stake. The 2010s also saw the rapid adoption of containers and an exit from the long AI winter, which along with the data explosion, created new workloads that began to go mainstream. Now, during this decade, we saw hybrid investments begin to take shape and show some promise. As the ecosystem realized broadly that it had to play in the AWS sandbox or it would lose customers. And we also saw the emergence of edge and IoT use cases like for example, AWS Ground Station, those emerge. Okay, so that's a quick history of cloud from our vantage point. The question is, what's coming next? What should we expect over the next decade? Whereas the last 10 years was largely about shifting the heavy burden of IT infrastructure management to the cloud, in the coming decade, we see the emergence of a true digital revolution. And most people agree that COVID has accelerated this shift by at least two to three years. We see all industries as ripe for disruption as they create a 360 degree view across their operational stacks. Meaning, for example, sales, marketing, customer service, logistics, etc., they're unified such that the customer experience is also unified. We see data flows coming together as well, where domain-specific knowledge workers are first party citizens in the data pipeline, i.e. not subservient to hyper-specialized technology experts. No industry is safe from this disruption. And the pandemic has given us a glimpse of what this is going to look like. Healthcare is going increasingly remote and becoming personalized. Machines are making more accurate diagnoses than humans, in some cases. Manufacturing, we'll see new levels of automation. Digital cash, blockchain and new payment systems will challenge traditional banking norms. Retail has been completely disrupted in the last nine months, as has education. And we're seeing the rise of Tesla as a possible harbinger to a day where owning and driving your own vehicle could become the exception rather than the norm. Farming, insurance, on and on and on. Virtually every industry will be transformed as this intelligent, responsive, autonomous, hyper-distributed system provides services that are ubiquitous and largely invisible. How's that for some buzzwords? But I'm here to tell you, it's coming. Now, a lot of questions remain. First, you may even ask, is this cloud that you're talking about? And I can understand why some people would ask that question. And I would say this, the definition of cloud is expanding. Cloud has defined the consumption model for technology. You're seeing cloud-like pricing models moving on-prem with initiatives like HPE's GreenLake and now Dell's APEX. SaaS pricing is evolving. You're seeing companies like Snowflake and Datadog challenging traditional SaaS models with a true cloud consumption pricing option. Not option, that's the way they price. And this, we think, is going to become the norm. Now, as hybrid cloud emerges and pushes to the edge, the cloud becomes this what we call, again, hyper-distributed system with a deployment and programming model that becomes much more uniform and ubiquitous. So maybe this s-curve that we've drawn here needs an adjacent s-curve with a steeper vertical. This decade, jumping s-curves, if you will, into this new era. And perhaps the nomenclature evolves, but we believe that cloud will still be the underpinning of whatever we call this future platform. We also point out on this chart, that public policy is going to evolve to address the privacy and concentrated industry power concerns that will vary by region and geography. So we don't expect the big tech lash to abate in the coming years. And finally, we definitely see alternative hardware and software models emerging, as witnessed by Nvidia and Arm and DPA's from companies like Fungible, and AWS and others designing their own silicon for specific workloads to control their costs and reduce their reliance on Intel. So the bottom line is that we see programming models evolving from infrastructure as code to programmable digital businesses, where ecosystems power the next wave of data creation, data sharing and innovation. Okay, let's bring it back to the current state and take a look at how we see the market for cloud today. This chart shows a just-released update of our IaaS and PaaS revenue for the big three cloud players, AWS, Azure, and Google. And you can see we've estimated Q4 revenues for each player and the full year, 2020. Now please remember our normal caveats on this data. AWS reports clean numbers, whereas Azure and GCP are estimates based on the little tidbits and breadcrumbs each company tosses our way. And we add in our own surveys and our own information from theCUBE Network. Now the following points are worth noting. First, while AWS's growth is lower than the other two, note what happens with the laws of large numbers? Yes, growth slows down, but the absolute dollars are substantial. Let me give an example. For AWS, Azure and Google, in Q4 2020 versus Q4 '19, we project annual quarter over quarter growth rate of 25% for AWS, 46% for Azure and 58% for Google Cloud Platform. So meaningfully lower growth rates for AWS compared to the other two. Yet AWS's revenue in absolute terms grows sequentially, 11.6 billion versus 12.4 billion. Whereas the others are flat to down sequentially. Azure and GCP, they'll have to come in with substantially higher annual growth to increase revenue from Q3 to Q4, that sequential increase that AWS can achieve with lower growth rates year to year, because it's so large. Now, having said that, on an annual basis, you can see both Azure and GCP are showing impressive growth in both percentage and absolute terms. AWS is going to add more than $10 billion to its revenue this year, with Azure growing nearly 9 billion or adding nearly 9 billion, and GCP adding just over 3 billion. So there's no denying that Azure is making ground as we've been reporting. GCP still has a long way to go. Thirdly, we also want to point out that these three companies alone now account for nearly $80 billion in infrastructure services annually. And the IaaS and PaaS business for these three companies combined is growing at around 40% per year. So much for repatriation. Now, let's take a deeper look at AWS specifically and bring in some of the ETR survey data. This wheel chart that we're showing here really shows you the granularity of how ETR calculates net score or spending momentum. Now each quarter ETR, they go get responses from thousands of CIOs and IT buyers, and they ask them, are you spending more or less than a particular platform or vendor? Net score is derived by taking adoption plus increase and subtracting out decrease plus replacing. So subtracting the reds from the greens. Now remember, AWS is a $45 billion company, and it has a net score of 51%. So despite its exposure to virtually every industry, including hospitality and airlines and other hard hit sectors, far more customers are spending more with AWS than are spending less. Now let's take a look inside of the AWS portfolio and really try to understand where that spending goes. This chart shows the net score across the AWS portfolio for three survey dates going back to last October, that's the gray. The summer is the blue. And October 2020, the most recent survey, is the yellow. Now remember, net score is an indicator of spending velocity and despite the deceleration, as shown in the yellow bars, these are very elevated net scores for AWS. Only Chime video conferencing is showing notable weakness in the AWS data set from the ETR survey, with an anemic 7% net score. But every other sector has elevated spending scores. Let's start with Lambda on the left-hand side. You can see that Lambda has a 65% net score. Now for context, very few companies have net scores that high. Snowflake and Kubernetes spend are two examples with higher net scores. But this is rarefied air for AWS Lambda, i.e. functions. Similarly, you can see AI, containers, cloud, cloud overall and analytics all with over 50% net scores. Now, while database is still elevated with a 46% net score, it has come down from its highs of late. And perhaps that's because AWS has so many options in database and its own portfolio and its ecosystem, and the survey maybe doesn't have enough granularity there, but in this competition, so I don't really know, but that's something that we're watching. But overall, there's a very strong portfolio from a spending momentum standpoint. Now what we want to do, let's flip the view and look at defections off of the AWS platform. Okay, look at this chart. We find this mind-boggling. The chart shows the same portfolio view, but isolates on the bright red portion of that wheel that I showed you earlier, the replacements. And basically you're seeing very few defections show up for AWS in the ETR survey. Again, only Chime is the sore spot. But everywhere else in the portfolio, we're seeing low single digit replacements. That's very, very impressive. Now, one more data chart. And then I want to go to some direct customer feedback, and then we'll wrap. Now we've shown this chart before. It plots net score or spending velocity on the vertical axis and market share, which measures pervasiveness in the dataset on the horizontal axis. And in the table portion in the upper-right corner, you can see the actual numbers that drive the plotting position. And you can see the data confirms what we know. This is a two-horse race right now between AWS and Microsoft. Google, they're kind of hanging out with the on-prem crowd vying for relevance at the data center. We've talked extensively about how we would like to see Google evolve its business and rely less on appropriating our data to serve ads and focus more on cloud. There's so much opportunity there. But nonetheless, you can see the so-called hybrid zone emerging. Hybrid is becoming real. Customers want hybrid and AWS is going to have to learn how to support hybrid deployments with offerings like outposts and others. But the data doesn't lie. The foundation has been set for the 2020s and AWS is extremely well-positioned to maintain its leadership, in our view. Now, the last chart we'll show takes some verbatim comments from customers that sum up the situation. These quotes were pulled from several ETR event roundtables that occurred in 2020. The first one talks to the cloud compute bill. It spikes and sometimes can be unpredictable. The second comment is from a CIO at IT/Telco. Let me paraphrase what he or she is saying. AWS is leading the pack and is number one. And this individual believes that AWS will continue to be number one by a wide margin. The third quote is from a CTO at an S&P 500 organization who talks to the cloud independence of the architecture that they're setting up and the strategy that they're pursuing. The central concern of this person is the software engineering pipeline, the cICB pipeline. The strategy is to clearly go multicloud, avoid getting locked in and ensuring that developers can be productive and independent of the cloud platform. Essentially separating the underlying infrastructure from the software development process. All right, let's wrap. So we talked about how the cloud will evolve to become an even more hyper-distributed system that can sense, act and serve, and provides sets of intelligence services on which digital businesses will be constructed and transformed. We expect AWS to continue to lead in this build-out with its heritage of delivering innovations and features at a torrid pace. We believe that ecosystems will become the main spring of innovation in the coming decade. And we feel that AWS has to embrace not only hybrid, but cross-cloud services. And it has to be careful not to push its ecosystem partners to competitors. It has to walk a fine line between competing and nurturing its ecosystem. To date, its success has been key to that balance as AWS has been able to, for the most part, call the shots. However, we shall see if competition and public policy attenuate its dominant position in this regard. What will be fascinating to watch is how AWS behaves, given its famed customer obsession and how it decodes the customer's needs. As Steve Jobs famously said, "Some people say, give the customers what they want. "That's not my approach. "Our job is to figure out "what they're going to want before they do." I think Henry Ford once asked, "If I'd ask customers what they wanted, "they would've told me a faster horse." Okay, that's it for now. It was great having you for this special report from theCUBE Insights Powered by ETR. Keep it right there for more great content on theCUBE from re:Invent 2020 virtual. (cheerful music)

>> Presenter: From around the globe, it's theCUBE, with coverage of KubeCon and CloudNativeCon North America 2020 Virtual. Brought to you by Red Hat, the Cloud Native Computing Foundation and ecosystem partners. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're coming to you from our Palo Alto Studios with our ongoing coverage of KubeCon + CloudNativeCon 2020, the digital version. It would have been the North American version but obviously everything is digital. So we're excited, we've been coming back here for years and we've got a founder of CNCF and also a practitioner, really great opportunity to get some insight from someone who's out in the field and putting this stuff into work. So we're joined in this next segment by Ken Owens. He is the Vice President of Software Development Engineering for MasterCard, and he's a founding member of the CNCF, The Cloud Native Computing Foundation. Ken, great to see you. >> Yeah, great. Thank you for having me, I have, I've enjoyed theCUBE over the years and I'm glad to be a part of it again. >> Yeah, so we're, we're psyched to have you on, and I think it's the first time I've got to talk to you. I think you might've been on in LA a couple of years ago, or I was kind of drifting around that show. I don't think I was a it was on the set that day, but before we jump into kind of what's going on now, you were a founding member of CNCF. So let's take a step back and kind of share your perspective as to kind of where we are now from where this all began and kind of this whole movement around Cloud Native. Certainly it's a good place to be. >> Yeah, yeah definitely. It's been a great ride. In our industry, we go through these sort of timeframes every decade or so, where something big kind of comes along and you get involved in and you participate in it. And it gets to be a lot of fun and it either dies or it evolves into something else, right? And with CloudNativeCon Cloud Native itself, this concept of just how difficult it was to really move with the type of agility and the type of speed that developers in the enterprise really need to move at. It was just, it was hard to get there with just traditional infrastructure, traditional ways of doing configurations of doing management of infrastructure and it really needed something different and something to kind of help, it was called orchestration of course but at the time we didn't know it was called orchestration right. We knew we needed things like service mesh, but they weren't called service meshes then. There were more like control planes. And how do you, how do you custom create all of these different pieces? And the great thing about the CNCF is that we, when we started it, we had very simple foundational principles we wanted to follow right. One was, we wanted to have end users involved. A lot of foundations as become very vendor-driven and very vendor-centric. And you kind of lose your, your core base of the practitioners as you call us right? The guys who actually need to solve problems they're trying to make a living solving problems for the industry, not just for selling products, right? And so it was important that we get those end users involved and that, and that's probably the biggest changes. It's a great technology body. We had great technologists, great engineers and the foundation but we also have a huge over 150 end users that have engaged and been very involved and contributing to the end users things of the community, contributing to the foundation now. And it's been awesome to see that come to fruition over the last three years. >> Yeah, it certainly part of the magic of open source, that's been so, so transformative. And we've seen that obviously with servers and Linux and what what that did, but we've been talking a lot lately too about kind of the anniversary of the of the Agile Manifesto and kind of the Agile Movement and really changing the prioritization around change and really making change a first class citizen as opposed to kind of a nightmare I don't want to deal with and really building systems and ways of doing things that adopt that. I want to just to pull up the Cloud Native definition 'cause I think it's interesting. We talk about Cloud Native a lot and you guys actually wrote some words down and I think it's worth reading them that Cloud Native Technologies empower organizations to build and run scalable applications in dynamic environments. Dynamic environments is such a key piece to this puzzle because it used to be, this is your infrastructure person, you've got to build something that fits into this. Now with an app-centric world has completely flipped over and the application developer doesn't have to worry about the environment anymore, right? It's spin it up and make it available to me when I need it. A really different way of thinking about things than kind of this static world. >> Definitely and then that was the big missing piece for all those years was how do you get to this dynamic environment, right, that embraces change and embraces risk to some extent. Not risk like you heard in the past with risk avoidance is so important to have, right. It's really more, how do you embrace risk and fail earlier in the process, learn earlier in the process so that when you get to production you're not failing, you're not having to worry about failure because you cut as much as you could in the earlier phases of your development life cycle. And that's been set, like you said that dynamic piece has just been such the difference. I think in why it's been taken off. >> Yeah. >> And industry this last five years now that we've been around. >> Yeah, for sure. So then the next one well, I'm just going to go through them 'cause there's three main tenants of this thing. These techniques and techniques enabled loosely coupled systems that allow engineers to make high impact changes frequently and predictably with minimum toil. I mean, those are, those are really hard challenges in a classic waterfall way with PRDs and MRDs and everything locked down in a big, giant Gantt chart that fills half of the half the office to actually be able to have loosely coupled systems. Again a really interesting concept versus hardwired, connected systems. Now you're talking about APIs and systems all connecting. Really different way to think about development and how do you build applications. >> Yeah and the interesting thing there is the very first definition we came up with five plus years ago was containers, containerized workloads, right? And being technologist, everyone focused on those words containers and containerized and then everything had to be a container, right? And to your point, that isn't what we're trying to do, right? We're trying to create services that are just big enough to support whatever is needed for that service to support and be able to scale those up and down independently of other dependent systems that may have different requirements associated with what they have to do, right. And it was more about that keeping those highly efficient type of patterns in mind of spinning up and spinning down things that don't have impact or cause impact to other larger components around them was really the key not containers or containerized. >> Right. >> Obviously that's one of the patterns you could follow to create those types of services and those patterns, but there is nothing that guarantees it has to be a container that can do that. Lots of BMS today and lots of Bare Metal Servers can have a similar function. They're just not going to be as dynamic as you may want them to be in other environments. >> Right and then the third tenant, three of three is fostering sustainable ecosystem of open source vendor neutral projects, democratizing state-of-the-art patterns to make these innovations accessible for everyone. So just the whole idea of democratization of technology, democratization of data, democratization of tools, to do something with the data to find the insight democratization of the authority to execute on those decisions once you get going on that, I mean the open source and kind of this democratization to enable a broad distribution of power to more than just mahogany row, huge fundamental shift in the way people think about things. And really even still today, as everyone's trying to move their organizations to be more data-centric in the way they operate, it is really all about the democratization and getting that information and the tools and the ability to do something with it to as broad a group of people as you can. And that's even before we talk about open source development and the power of again, as you said, bringing in this really active community who want to contribute. It's a really interesting way that open source works. It's such a fun thing to watch, and I'm not a developer from the outside, but to see people get excited about helping other people. I think that's probably the secret to the whole thing that really taps into. >> Yeah, it is. And open source, there were discussions about open source for 20 plus years trying to get more into open source contributing to open source in an enterprise mindset, right? And it could never really take off 'cause it's not really the foundation or the platforms or the capabilities needed to do that. And now to your point, open source was really the underlying engine that is making all of this possible. Without open source and some of those early days of trying to get more open source and understanding of open source in the enterprise, I think we'd still be trying to get adoption but open source had just gotten to that point where everyone wanted to do more with open source. The CNCF comes along and said, here's the set of democratized, we're not going to have kingmakers in this organization. We're going to have a lot of open solutions, a lot of good options for companies to look at, and we're not going to lock you in to anything. 'Cause that's another piece of that open source model, right. Open source still can lock you in, right. But if you have open choices within open source, there's less, lock-in potential and locking isn't really a horrible thing. It's just one of those tenants you don't want to be tied too tightly to any one solution or one hope, open source even program because that could 'cause issues of that minimal toil we talked about, right. If you have a lot of dependencies and a lot of, I always joked about OpenStack but if I have to email two guys, if I find an issue in OpenStack about security that's not really a great security model that I can tell my customers I have your security covered, right? So, you want to get away from emails and having to ask for help, if you see a big security issue you want to just address it right then and fix it fast. >> Right, right. So much to unpack there. And for those that don't follow you, you've done a ton of presentations. You've got a ton of great content out of the internet with deep technical dives, into some of this stuff and the operational challenges in your philosophies but good keeping it kind of high level here. 'Cause one of the themes that comes up over and over in some of the other stuff I saw from you is really about asking the right questions. And we hear this time and time again, that the way to get the right answer first you got to frame the question right. And you talk quite extensively about asking the why and asking the how. I wonder if you can unpack that a little bit as to why those two questions are so important and how do you ask them in a way that doesn't piss everybody off or scare them away when you're at a big company like MasterCard that has a lot of personal information, you're in the finance industry, you got ton of regulation but still you're asking how and you're asking why. >> Yeah, definitely. And those, those are two questions that I keep coming back to in the industry because they are, they're not asked enough in my opinion. I think they, for the reasons you brought up those there's too much pushback or there's, you don't want to be viewed as someone who's being difficult, right? And there maybe other reasons why you don't want to ask that but I like to ask the why first because it, you kind of have to understand what's the problem you're trying to solve. And it kind of goes back to my engineering background, I think right. I love to solve problems and one of my early days and you might have heard this on one of my, my interviews, right. But in my early days, I was trying to fix a problem that I was on an advanced engineering team. And I was tier four support in a large Telco. And for months we had this issue with one of our large oil based companies and no one could solve it. And I was on call the night that they called in. And I asked the guy a simple question, tell me which lights you see on this DHUC issue? Which is a piece of equipment that sits between a ATM network and a regular Sonnet network. So we're watching, I'm asking them as kind of find out where in this path, there's a problem. And the guy tells me where there's no lights on. And I'm like well, plug in the power and let me know when it boots up and then let's try another test. And that was the problem. So my, the cleaning crew would come through and unplugged it. And so I learned early on in my crew that if you don't ask those simple questions, you just assume that everything's working almost nine times out of 10, it's the simple, easy solution to a problem. You're just too busy thinking of all the complex things that could go wrong and trying to solve all the hard problems first. And so I really try to help people think about, ask the why questions, ask, why is this important? Why do we need to do this now? Why, what would happen if we don't do this? If we did it this other way, what's the downside of doing it this other way? Really think through your options, 'cause it may take you 20, 30 minutes to kind of do a good analysis of a problem, but then your solution you're not going to spend weeks trying to troubleshoot when it doesn't work because you put the time upfront to think about it. So that's sort of the main reason why I like to ask the why and the how, because it forces you to think outside of your normal, my job is to take this cog and put it over here and fix this, right. And you don't want to be in that, that mode when you're solving complex problems because you overlook or you miss the simple things. >> Right. So you don't like the 'cause we've always done it that way? (both laughing) >> I do not. And I hear that a lot everywhere I've been in the industry and anywhere, any company you have those, this is the way we've always done it. >> Yeah, yeah. Just like the way we've always traveled, right. And the way we've always been educated and the way we've always consumed entertainment. It's like really? I wanted to (indistinct) >> I have learned though that there's a good, I like to understand the reason behind why we've always done it that way. So I do always ask that question. >> Right. >> I don't turn around on someone and get mad at them and you say, Oh, we can't we have to do it differently. I don't have the mindset of let's throw that out the window because I realized that over time something happened. It's like when I had younger kids, I always laugh because they put these warnings on those whatever they call them at the kids stand up in them. >> Right, the little, the little (indistinct) >> Don't put them on top of the stairs right. These stupid little statements are written on there. And I always thought I was dumb. And if somebody told me, well that's because somebody put their kid near the pool and they drown. >> Right, right. >> You have to kind of point out the obvious to people and so, >> Yeah. >> I don't think it's that dangerous of a situation and in the work environment, but hopefully we're not making the same mistakes that have been prevented by not allowing just the, not because we've done it this way before modeled it to go forward. >> Right, right now we have a rule around here too. There's a reason we have every rules is because somebody blew it at some point in time. That's why we have the rule that I want to shift gears a little bit and talk about automation, right? 'Cause automation is such a big and important piece of this whole story especially as these systems scale, scale, scale. And we know that people are prone to errors. I mean, I had seen that story about the cleaner accidentally unplugging things. We all know that people fat fingers, copy and paste is not used as universally as it should be. But I wonder if you could share, how important automation is. And I know you've talked a lot about how people should think about automate automation and prioritizing automation and helping use automation to both make people more productive but also to prioritize what the people should be working on as well as lowering the error rate on stuff that they probably shouldn't be doing anyway. >> Exactly, yeah automation to me is, as you've heard me say before is it's something that is probably almost as big of a key tenet as open source should be, right? It's one of those foundational things that it really helps you to get rid of some of that churn and some of the toil that you run into in a production environment where you're trying to always figure out what went wrong and why did this system not work on this point in time and this day and this deployment, and it's almost to your point always a fat finger, someone deleted an IP address from the IPAM system. There's all kinds of errors that you can people can tell you about that have happened. But to the root of your question is automation needs to be thought about from three different primary areas in my view, in my experience. The first one is the infrastructure as code, software defined infrastructure, right. So the networking teams and the storage teams and the security teams are probably the furthest behind in adopting automation in in their jobs, right. And their jobs are probably the most critical pieces of the infrastructure, right? And so those are, those are pieces that I really highly encouraged them to think about how can they automate those areas. The second piece is I think is equally as important as the infrastructure piece is the application side. When I first joined multiple enterprises in the past, the test coverage is in the low 10's to 20%, right. And your test coverage is a direct correlation to how well your application is going to behave and production in terms of failures, right? So if you have low test coverage, you're going to have high failure rates. It's sort of over over all types of industries every study has shown that, right. So getting your test coverage up and testing the right things not just testing to have test coverage right. >> But actually. >> Right, right. >> Thinking through your user stories and acceptance criteria and having good test is really, really important. So you have those two bookends, right. And in between, I think it's important that you look at how you connect to these services, these distributed systems we talked about in the opening right. If you fully automate your infrastructure and fully automate your application development and delivery, that's great. But if in the middle you have this gooey middle that doesn't really connect well doesn't really have the automation in place to ensure that your certificates are there that your security is in place. That middle piece can become really a problem from a security and from a availability issue. And so those those are the two pieces that I say really focus on is that gooey middle and then that infrastructure piece is really the two keys. >> Right, right. You've got another group of words that you use a lot. I want you to give us a little bit more color behind it. And that's talking to people to tell them that they need to spend more time on investigation. They need to do more experimentation. And then and the one that really popped out to me was it was retro to retrospective to not necessarily a postmortem which I thought is interesting. You say retrospective versus the postmortem, because this is an ongoing process for continuous improvement. And then finally, what seems drop dead dumb obvious is to iterate and deliver. But I wonder if you can share a little bit more color on how important it is to experiment and to investigate and to have those retrospectives. >> Yeah definitely. And then it kind of goes back to that culture we want to create in a Cloud Native world, right. We want to be open to thinking about how we can solve problems better, how we can have each iteration we want, to look at, how do we have a less toil, have less issues. How do we improve the, I liked kind of delight in your experience, how do you make your developers and your customers specific, but specifically how do you make your customers so happy with your service? And when you think about those sort of areas, right. You want to spend some portion of your time dedicated to how do I look at and investigate better ways of doing things or more improvements around the way my customer experience is being delivered. Asking your customers questions, right. You'd be surprised how how many customers don't ever get asked for their opinion on how something works, right. And they want to be asked, they'd love to give you feedback. It doesn't necessarily mean you're going to go do it that next iteration, right? The old adage I like to use is if Henry Ford had listened to his customers he would have tried to breed a faster horse, right? And so you have to kind of think about what you want to try to deliver as a product and as an organization but at the same time, that input is important. And I think, I say carve it out, because if you don't, we're so busy today and there's so much going on in our lives. If you don't dedicate and carve out some of that time and protect that time, you will never get to that, right. It's always a, I'll get to that next year. Maybe our next iteration I'll try, right. And so it's important to really hold that time as sacred and spend time every week, every couple of weeks, whatever it works out in the schedule, but actually put that in your calendar and block out that time and use it to really look at what's possible, what's relevant, what kind of improvements you can have. I think those are really the key the key takeaways I can have from that piece of it. And then, the last one you asked about, which I think is so important, is the retrospective, right. Always trying to get better and better at what you do is, is an engineer's goal, right? We never liked to fail. We never liked to do something twice, right? We don't want to, we want to learn the first time we make a mistake and not make it over and over again. So that those retrospectives and improving on what you're doing iteratively. And to the point you brought up and I like to bring this up a lot, 'cause I've been part not at MasterCard, but at other companies parts of companies that would talk a great game come up with great stories, say here's our plan. And then when we get ready to go to deliver it, we go and we reinvestigate the plan and see if there's a better plan. And then we get to a point where we're ready to go execute. And then we go back and start all over again, right. And you've got to deliver iteratively, if you don't, you're the point I like to always make is you're never going to be ready, right. It's like, when are you ready to have kids? You never ready to have kids, right. You just have to go and you'll learn as you go. You know so. >> Right, right, I love that. Well again, Ken, you have so much great stuff out there for technical people that want to dive in deep? So I encourage them just to do a simple YouTube or excuse me, YouTube search or Google search but I want to give you the last word. One word, I'm going to check the transcript when this thing is over that you've used probably more than any other word while we've been talking for the last few minutes is toil. And I think it's really interesting that it brings up and really highlights your empathy towards what you're trying to help developers avoid and what you're trying to help teams avoid so that they can be more productive. You keep saying, avoid the toil, get out of the toil, get out of this kind of crap that inhibits people from getting their job done and being creative and being inventive and being innovative. Where does that come from? And I just love that you keep reinforce it and just kind of your final perspective as we wrap on 2020 and another year of CNCF and clearly containers and Kubernetes and Cloud Native is continues to be on fire and on a tear. I just wonder if you can share a little bit of your perspective as a founding member as we kind of come to the end of 2020. >> Yeah definitely. Thanks again for having me. It's been a great, great discussion. I am a developer by background, by trade today, I still develop. I still contribute to open source and I've had this mantra pretty much my entire career that you have to get into the weeds and understand what everyone's experiencing in order to figure out how to solve the problems, right. You can't be in an ivory tower and look down and say, Oh, there's a problem, I'm going to go fix that. It just doesn't work that way. And most problems you try to solve in that model will be problems that no other team has really experienced. And there not going to be help, they're not going to be thankful that you solved the problem they don't have, right? They want you to solve a problem that they have. And so I think that that's sort of a key for the reason why I spent so much time talking about that as I live it every day. I understand it. I talk with my development community and with a broader community of developers at MasterCard and understand the pains that they're going through and try to help them every day with coming up with ways to help make their lives a lot easier. So it's important to me and to to all organizations out there and in all of the, in the world. So, CNCF its been great. It's still growing. I'm always looking for end users. I'd love to talk to you. Well, you can reach out to, to the CNCF if you'd like to learn more, our website has information on how to get connected to the end user community. We community within the CNCF that is not, it's a private community. So you don't have to worry about your information being shared. If you don't want people to know you belong to the community, you don't have to list that information. If you want to list it, you're welcome to list it. There's no expectations on you to contribute to open source, but we do encourage you to contribute, and are here to support that end user community any way we can. So thanks again for having us and looking forward to, to a great show in North America. >> All right well, thank you, Ken, for sharing your information sharing the insight, sharing the knowledge really appreciate it and great to catch up. All right. He's Ken, I'm Jeff. You're watching theCUBE with our ongoing coverage of KubeCon + CloudNativeCon 2020 North America Digital. Thanks for watching. We'll see you next time. (gentle music)

>> Voiceover: Live! From Anahiem, California, it's theCUBE. Covering Nutanix.next 2019 Brought to you by Nutanix. >> Welcome back everyone to theCUBE's live coverage of Nutanix.next, here in Anaheim California, I'm your host, Rebecca Knight along with my co-host John Furrier. We're joined by Sunil Potti, he is the chief product and development officer here at Nutanix. Thank you so much for coming on the show. >> Glad to be here. >> So we are talking about the era of invisible infrastructure and this morning on the main stage there was many many different announcements, new products and adjustments, augmentations to products. Can you walk our viewers a little bit, walk our viewers through a little bit what you were talking about today? >> Yeah, I mean (inaudible) so in fact, our vision really hasn't materially changed over the last few years. In fact, my team always teases me that all I do is essentially change the timeline but the same slideshow is up. But you know, something about vision being consistent and we sort of have broken that up into two major phases, the first phase is essentially to move cloud from being a destination to being an experience. What do I mean by that? Essentially, everyone knows about cloud as being something served by Amazon, or Google, or (inaudible) and ultimately, our belief has been that if we do an honest job of what Amazon or Google provided natively But bring cloud to the customers rather than having the customers go to a destination, Then they can essentially get maybe 60 or 70 percent of that experience but maybe at a tenth of the price or a tenth of the time. And most human beings as you guys know, is that once you get 60 or 70 percent, You're happy and you move on other things. And that's really the first act of this company is to sort of bring cloud to the customers. And in doing so, in my opinion solves one of clouds biggest, you know, perennial issues, which is migration. Because that's essentially what lift and shift, gets in the way, that I've gotta change something that I've invested 20 years in and I've gotta lift and shift it. And if something comes to you, that gap is dramatically reduced, right? And sure, we don't do everything that public clouds do but, like I said, if you can do an honest job of that 60 % then it turns out that most customers now adopt Nutanix looking at public cloud as more of a tailwind instead of a headwind because the more they taste amazon outside the more they want amazon inside. And so, so, that's really the first act of the company. A series of products that allow us to build out a full blown IA stack but also a bunch of services such as desktops, databases, all the usual services. So it's all about increasing the layers of abstraction to the user so they can do one take operations. So, that's the first act. And the second act which is much more a longer term bet for the next decade or so is that if the first act was about bringing cloud to you to replatform the data center, customers are also going to redesign their apps and when they redesign their apps Do you want to do it on an operating system that locks you only into one public cloud? Or do you want to do it in something that can moves across clouds? And that's our second act of the company. And there's a lot of details there. >> John Furrier: So hyper-convergence was a great concept and proved it out, great customer base, core business is humming along, solid, but the growth is gonna come from essentials which is the enterprise in multiple clouds. So I get that. As you guys look and build those products and you're the chief product officer, you have the keys to the kingdom, it's all on you. >> It's in my guide to work out. >> So you're a team. But this is a big pressure, this is the opportunity. As you think about a software company as you guys are shifting from being hardware to software things start to be different so as you start thinking about the act two the convergence of clouds. That really is a key part of it, what you did for the data center, HCI, >> Yeah, totally. >> You're doing HCI for the cloud. >> Yeah, like what does that actually mean? >> So explain that concept. >> No, it's a great question. So, and some of this, obviously, we are struggling through ourselves. But we are not afraid of making mistakes in this transition as you've seen other the last year, we've gone from being in the plans company to a software that runs on third party to being a subscription company, to now running on clouds. All within a span of 12 months, while building a business, right? And sometimes it works, sometimes we pick up ourselves and learn from mistakes and go but to your point I think, we're not afraid to become an app on somebody else's operating system. Just like Microsoft said "Look I'm gonna release office, "on Mac or Ipad before I even do it on Windows," that kind of thinking has to permeate and pretty much, in my opinion, every technology will end up going forward. A good example of that is look, if somebody wants to consume their applications that they built on Nutanix on premise but their idea was look they don't wanna be in the data center business tomorrow without changing the apps they should be able to take that entire infrastructure and applications and consume it inside Amazon's fabric because they provide a bunch of other services as well as data centers. So, a recent announcement of Nutanix in AWS not on AWS for a reason is an example of us becoming an app on somebody else's operating system. That's an example of us transforming further away from being an infrastructure only or an appliance only company. >> What does this mean for your customers and your partners because you guys have taken an open strategy with partnering, the HPE announcements, very successfully off the tee, in the middle of the fair way as we say, looking good. That seems to be the trend, others taking a different approach, you know that is, owning it all. >> Yeah yeah, in fact I would say that look, in some way, internally we joke about ourselves, as we have to prove the... You know, we always used to think about ourselves as a smart phone for the enterprise, consumerizing the data center. But we had to prove that model by owning the full stack like Apple did, but over a period of time, to democratization happens, by distribution. And so in some ways, we have to become more of an android like company while retaining the best practices of the delight and the security of an apple device. So that's the easiest analogy where, We're trying to work with partners like Dell, Lenovo, and now increasingly, Hitachi, Fujitsu, Inspur, Intel, everybody is signed up, just because everybody now knows that the customers want an experience. And now the lastest relationship with HP takes it to the next level now where we want to bring essentailly super micro like appliance goodness one click from away upgrades, support, everything. But with a HPE backed platform, that both companies can benefit from. >> You know, one of the big complaints from customers, I hear, on theCUBE, and also privately is there's so many tools, and management software, I've got management plane for this, I got this over here, >> For sure... >> So there's kinda this toolshed mentality of, you know, a new hire, learn this tool for that software, people don't want another tool, they don't want another platform. So, how do you see that, how do you address that with going forward, this act two, as you continue to build the products what's the strategy and what's the value proposition for customers? >> I mean, think it's no different than I think how we sort of launched the company in the first place which is there's no way you can say we'll simplify your life without removing parts. That was the original Steve Jobs thing, right? The true way to simplify is to remove parts, right? And essentially that's what hyper-convergence has done, it just we're doing this not just for infrastructure but for clouds because when you use Nutanix you throw away old computer, you throw away old storage, you throw away old (inaudible) I mean, that's the only way to converge your experience down to one tool. You can't stitch together ten tools into this magical fabric, I mean it doesn't work that way. But that's hard, because not every customer is ready to do that, every partner is ready to do that they've got their own little incumbencies. But that's the journey we're on, it's a right of passage for us, we have to earn it the old fashioned way and we've done reasonably well so far. >> So you mentioned Steve Jobs, he also said, when he was alive, in an interview, on the lost interviews on Netflix, I watched that recently. He said, also software gives you the opportunity to move the needle on efficiencies, and change the game, much more significantly then managing a process improvement which can give you maybe 30% yield. He's saying you can go 60s, 80% changeover with software. This is part of your strategy, how do you guys see Nutanix in the future, with the software lead or approach, changing the game for IT? >> I think clearly, software is fundamental, I mean the whole point of us, our product was I think, we have some folks on the platform group that help make sure that the software runs because software has to run somewhere, by the way. It doesn't run in air, it runs on hardware. So let's not under emphasize hardware for that reason, but, most of our IP has been in software. But I would say that the real thing for us that has kept us going is design of software which is essentially also, when you go back to the Apple thing, because a lot of software renders out that too. It's how you design it, starting with why, rather than just going to the how, is how we see ourselves differentiating what we deliver to our customers over the next 5 years. >> Rebecca Knight: I want to ask you about innovation and your process because here you are, you're the Chief product officer at this very creative company, I wanna know, what sparks you're creativity, where do you get your ideas? Of course you're gonna say, "I talk to customers, "and I find out their problems", but where do you go for inspiration? >> Yeah, I think it's an age old problem I'll give you my personal answer, I don't think it's representative of everyone in the company obviously. And that's one of the good things with Nutanix each of us have their own point of view and things, right? We have this term of "let chaos reign and then reign in chaos". Right? To some extent. That has been done well at other companies like Google, and so forth. So, I've always believed in a couple of vectors for inspiration. The most obvious one is to listen to others. More than talk. Whether it's listening to customers, listening to partners, listening to other employees with other ideas and have a curated way to do that because if you only listen to customers you build faster horses not carts, as Henry Ford said, okay? So that's the what I would call a generic theme and you'd think that it's easy to do so, but it's very hard to truly listen from signal from the noise by the way. So there's an art there that one has to get better at. But the DNA has to be there to listen that's the first thing I would say. The second thing which I think is maybe deeper, and that's probably more in the... The first one applies to maybe 1% The second one, probably applies to .001% which is having intuition of what's right. And this ability, people call it, I don't know, big words like vision and so forth the ability to see around corners and anticipate, you know, my old manager, a guy that I respect a lot, Mark Templeton who was the CEO for Citrix, used to always ask this question "Do you know why Michelin has three stars? "The first star is for food, obviously, "there has to be good food. "The second star is for service. "The third star, not many people know why it's for" According to him, and I haven't really checked it yet, I haven't really eaten in too many Michelin three star restaurants, is anticipation. And product strategy is a little bit like that, right? So to me, that's where Nutanix really trumps the competition. Is that second dimension of intuition. More so than even, listening to customers. >> It's seeing around those corners, and knowing which way the winds are blowing. >> Totally. >> One of the other things that we're talking about a lot about, here on theCUBE, particularly at this conference, is the importance of culture. Nutanix...we had Dheeraj on this morning talking about the sort of playful nature that he tries to bring to the company, and that really has filtered down, how would you describe the Nutanix culture and how do you maintain the culture? >> So I think, we... I'll tell you personally, the journey that I was on, that there were a couple of things that I brought to the table, a couple things that I learned myself, as well as what I could see, a couple things that you'll see in a company that has been built by founders, in my opinion, I'm not a founder, or entrepreneur myself, but I've seen them in action now, is they bring one dimension that I've not seen in big company leaders, which is continuous learning. Because that's the only way they can stay in the company when it goes from 0 to ninety, right? And the folks that continuously learn, stay. If they don't, they leave and we get professional leaders. So, continuous learning, if it can be applied, to the generic company becomes an amplifying effect now. People can learn how to grow, look around the corners, they can learn things, that otherwise they aren't born with, in my opinion. So I think that's one unique dimension that Nutanix I think, inculcates in a lot of people, is this continuous learning. The other dimension, which I think, everybody knows about Nutanix being this humble, hungry, honest, with heart, you know those four words sort of capture the, a sense of, the playful, authenticity. But I think we're not afraid to be wrong. And, we're not afraid to make fun of ourselves. We're not afraid to be, I guess, ourselves, right? And that, I think is easy to say, but very hard to do. >> John Furrier: You learn through your mistakes as they say, learn through failure. So, you mention intuition. What does your intuition tell you about the current ecosystem as the market starts to really accelerate with multi cloud on premise private cloud, which by the way, good intuition, of course we keep on, at the first private cloud reports dominion and team, they got that right. The waves are coming and they look different. There's gonna be more integration we think. What does your intuition tell you about these next couple waves that are gonna come in to the landscape of the tech industry? >> Yeah, I mean I think, since I do want to come back on theCUBE again and again, and have something left over, I will say one thing though, is I think the gain in multi cloud is going to move up the stack, okay? That's where the next set of cloud wars are going to be fought. Is whose going to provide not just a great database as a service, but a great database itself. Because, Oracle's time's up, as far as I'm concerned, right? And you're going to see that with many traditional software stacks, some of them are Sass stacks that have been around for 20 years, by the way. Some of the largest Sass companies have been around for 20 years. It's time for a reboot for most of those companies. >> How about the Edge? What does the intuition tell you on the Edge? Certainly very relevant, you've got power, you've got connectivity expanding, Wifi 6 around the corner, we've seen that. 5g, okay, I buy it. But as it really starts to figure itself out, it's just another note on the network. What's your intuition tell you? >> Yeah, I mean, this is one area that I'm not too deep in, I've got other guys in my team who know a lot more, but, my intuition tells me, the more things change, the more they'll remain the same, in that area, right? So don't be surprised if they just end up being another smart phone. You know, its got an operating system, it runs apps, it's centrally controlled, talks to services in the back end, I see no reason why the Edge should be any different, if that make sense. >> John Furrier: Yeah, exactly. Then data, big part of it. Big part of your strategy, the data piece, >> Of course, of course, yeah. I mean I think data being a core competency of any company is going to stand out, I think in the next 5, 10 years. >> John Furrier: Awesome. What's going on at the show? What's been your hottest conversation in the hallways, talking to customers, partners, employees, what's some of the trending conversation? >> I don't know, this conversations pretty interesting! (laughs) >> Of course! >> Rebecca Knight: We agree! (Laughs) >> My intuition is telling me this is a good conversation! Hope it comes out good! >> Keep using that word man. >> I love it! >> Anyway, always great to be with you guys. >> Sunil, thank you so much for returning to theCUBE. >> Anytime. >> I'm Rebecca Knight, for John Furrier, we will have much more from Nutanix.next coming up in just a little bit. Stay with us. (upbeat music)

>> Narrator: Live from Anaheim, California, it's The CUBE covering Nutanix .NEXT 2019. Brought to you by Nutanix. >> Cameraman: Izzy! >> Welcome back, everyone, to The CUBES's live coverage of Nutanix .NEXT here in Anaheim, California. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We have two guests for this segment, we have Phil Davis, he is the president of Hybrid IT Hewlett Packard Entrerprise. Thanks so much for coming on The CUBE, Phil? >> Great to be here. >> And we have Chris Kaddaras, he is the SVP America's Nutanix. Thank you so much, Chris. >> Right, thanks for having me. >> So, two weeks, this partnership between Nutanix and HPE, two weeks old, newly announced. Chris, I wanna ask you, explain to our viewers a little bit about it and how it came about. What is the partnership? >> Sure, now I think the way the partnership came about was really around customer and partner demand, right? The marketplace was really looking for two great companies to get together and provide a solution for what they wanted to kind of cure their problems. The two components of the partnership effectively is, one component is the Nutanix sales teams are gonna be selling their Nutanix solutions and appliances with a great HPE computing infrastructure involved in that appliance. So, that's the first big group part, and I'll let Phil talk about the second part of the relationship. >> Yeah, and the second part is really around how do we enable a consumption model for our customers? I mean, if you think about what's going on with the public cloud, customers wanna be able to scale up or scale down and kind of pay as they go. And so, HPE has been leading with an offering we call Green Lake. It's a couple-billion-dollar business growing over 50% a year, so it kind of shows you the interest in it, and we also, therefore, offer the Nutanix solution on our infrastructure and then wrap that with a consumption model service that allows customers that flexibility. So, those are the two elements of the partnership. >> So, you're selling Nutanix with your Green Lake. >> Embedded in the Green Lake offering, that's correct. >> And Nutanix has selling Compute with their sales worth. >> Phil: Exactly right. >> Chris: Yeah, so with our DX solution, yeah with HPE Compute. >> Got it. Now, you guys have indirect and direct sales, both sides, channel play, is it a channel partnership or both, can you just explain the go-to market? >> Yeah, and I think that what you'll see is there's just a lot of alignment, a lot of synergy. Both companies are very, very channel friendly. I mean, HPE's a 75 plus year old company and our very first sale as a company went through the channel, right? So, our whole DNA is wired towards the channel. Over 70% of our business goes through the channel. So, what we've really made sure is that we make this very, very easy for the channel to consume and also, be paid and compensated on. So, it flows through all the standard HPE channel compensation and programs that we have in play. So, absolutely, very friendly for the channel. >> Yeah, and I think this will work really well for both channel communities that we have. We have a lot of Nutanix channel partners that have not been, for whatever reason, have not been selling HPE and now, they have a perfect opportunity to sell HPE Compute platforms with our DX appliance. We also have a lot of great channel partners who want a better consumption model where customers are looking to flex up and down. We have not been able to provide that for Nutanix software solutions. So, to adopt Green Lake for some of these partners will be a fantastic offering for their customers. >> Maybe just a dove-tail on that comment, one of the things we've worked really hard in the last year is to make Green Lake more channel friendly. Channel reps tend to get paid as the margin comes in. So, if you spread that out over time, they don't make the same money. So, we've changed the rebate 17% up front for the channel partners, we've simplified the offering, we made it quicker, so we're doing a lot to make Green Lake much easier for our channel partners and a lot of excitement about being able to offer Nutanix with Green Lake as well. >> What's the timing on the channel rollout? Is it rolling out now? Is it instantly growing out? Is there timing on-- >> Phil: Instantly. >> Instantly? >> So, we've already briefed the channel, we are making it available, we're providing all the quotes, we have a ton of material available online through our online portals and tools for the channel partners, we have FAQs, we have marketing materials, we have, actually, letters already built up for the channel. So, it's now. >> So, I gotta ask the hard question here because I think one of the things I see that's really awesome is the channel's gonna love this because Nutanix has a channel generated opportunity. Their challenge in that opportunity is when they do a POC, they usually win the business. That's kind of a direct sales model that's favored Nutanix for their success. This is gonna bring a lot of mojo to the channel bringing HPE and Nutanix together for this unique solution. I'm sure the reaction's been positive. Are they seeing an up-step in more POCs and more action with customers? >> Phil: You wanna take that? >> Yeah, we're seeing a lot, actually. So, I was just there actually reviewing my team yesterday. We have a list of now starting to get towards 100 customers that we think we can align with together, right? And multiple go to markets. We have Green Lake opportunities, we have DX opportunities, which is Nutanix on HPE. We also have a lot of opportunities around Nutanix software only on HPE Compute that a lot of customers wanna consume as well in a different way. So, we're seeing that really start to scale. We haven't done the first POC of DX because it hasn't released to the market yet, right? We are doing POCs on software only on HPE servers, but the DX solution will be releasing in the next few months. So Phil, I know the HPE channel pretty well and they love services, wrapping services around an offering. Can you talk about how this impacts from the services side because I gotta be looking at my chops if I'm a dealer partner because I can bring this new solution in and I can wrap cloud-like capabilities around it. >> Yeah, and you look at a lot of our partners, the hardware-only business is getting pressure. And so, a lot of our partners are doing exactly what you just described. They're trying to move more and more into services. And you're right, there's a whole sweep of services the partners can wrap around this. Everything from advisory, upfront, because all of these workloads run on some sort of legacy environment. So, when they do bring in a hyperconverged, they need to move the workloads. So partners can help with that, supporting maintenance, implementation, all the way through to kind of day-to-day break fix. So, there's a range on services. Obviously, HPE has a pretty big services capability. We make those available through our channel partner as well, so if they wanna sell to HPE services they can do that, or if they wanna deliver 'em themselves, they can do that as well. >> I wanna ask you about the customers. You made this point on main stage that you, sort of, likened back to the Henry Ford quote where you can have any color, as long as it's black and the current marketplace was anything you want as long as it's in my stack, and this is how we're gonna do it. So, giving them more choice, more flexibility, what are you hearing so far? What was the problem in terms of their workload and why things were stiffeled or stunted, and now what do you hope this is going to do? >> Well, as I mentioned on main stage, everybody wants to make it easy to get on to their stack and really, really hard to move off of their stack, right? Whether you're a public cloud company, you want all your microservices, you want all the data trapped there, so it's not easy to move and some of our joint competitors are actually trying to lock you into the complete top-down stack. So, the feedback, so far, from customers and partners has been very, very, very positive because one of the things, I've been in the industry 29 years. One of the things that I can tell you is no one company is gonna out-innovate the entire industry. And so, what customers want is to be able to pick and choose the solutions that best meet their needs. And that's really what this partnership, I think, really embodies is the ability to give customers choice at multiple levels within that stack. Choice in the public cloud, choice on prem, choice of hypervisors, and that's really resonating. >> Yeah, and that's really Nutanix's design point, right? Is around choice, right? Choice at every level of a stack that you can have. And this provides us with the biggest choice in the marketplace at this point and time that was missing from our portfolio. The other piece that you mentioned that I'd like to point out is that the thing that a lot of people haven't been talking about is the services component. You know, Nutanix is a great company, we've grown a lot. But one place that we haven't grown to an extent is in the services side. We have a small services organization that really helps our customers, but we really need a services organization that can help our customers transform. And help our customers through a transformation of their underlying infrastructure and reduce the risk of change. And this HPE relationship will help us do that as well. >> And the other thing, too, that's interesting with Cloud and you guys are in the middle of demodernizing the data center, HPE's been there forever in the data center, is the private cloud has shown that the data center's still relevant. However, if you start going cloud-based stuff, integration's huge. So integrating, not just packaging our solutions, customers need to integrate all this stuff. This has been a key part of Nutanix and HPE. How do you guys see this going forward from an integration standpoint? Because on the product side, it's gotta integrate, and then in the customer environment you mentioned the consumption piece. Can you guys just expand on what that means? >> Sure. Yeah, we saw Dheeraj's presentation this morning, right? And Sunil's, our entire design point is how do we make everything invisible, right? How do we make those integration points invisible? Now, we all know that there's a traditional architecture you need to migrate from to take advantage of some of these things. And that's where the risk is, how do you get from A to B into these environments? As I mentioned, we do have a services organization that helps there, but we could use, now we have one of the largest partners in the industry that could help us do that. I think that's a key component. We will always try to innovate being Nutanix, we will always try to innovate in software, right? Let's try to figure out how we can make this so much easier, move it up the stack to make sure this is the easiest thing to migrate and have choice for customers. >> Yeah, and I think, maybe, just to add to that, if you think about it from a customer view in, right? A lot of customers moved a lot of things very quickly to the public cloud and the public cloud will continue to grow fast, but they're also learning some things. It's not quite as cheap as they thought it was gonna be, like twice as expensive. Moving data around is very expensive. The public cloud is charging you to get your own data back out. Data sovereignty matters a lot more than it used to with things like GDPR in Europe. More and more of the data's getting created at the edge. It's not in the cloud or the data center. And so, what we're seeing is customers are now thinking about things as you mentioned, we're kind of hybrid, and they're talking about the right mix. What's the right mix of public? What's the right mix of private? Where should the data live? And that's a tough story and that's a tough journey for them to go on, so they want help up front with the advisory services, they want help in being able to architect that, implement it, and then, in many cases, even kind of run that. And with nearly 25,000 services professionals around the globe, we have a unique footprint to help customers along that journey. >> It's an interesting deal, it's very, I think, gonna be pretty big. So, congratulations. >> Phil: Thank you. >> It was great having you both on The Cube, Phil and Chris. >> Thank you very much, thanks. >> Thanks for having us. >> I'm Rebecca Knight for John Furrier, we will have so much more from Nutanix .NEXT here in Anaheim, California, so stay with us. (electronic dance music)

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

>> Live from San Francisco, it's theCUBE, covering DockerCon '18, brought to you by Docker and it's ecosystem partners. >> Welcome back to theCUBE, we are live at DockerCon 2018 in San Francisco on a spectacular day. I am Lisa Martin with my with my co-host for the day, John Troyer, and we're very pleased to welcome back to theCUBE a distinguished CUBE alumni and Docker veteran, Steve Johnston, Chief Product Officer at Docker. Welcome back. >> Thank you, thank you very much. That's Scott Johnston but that's okay. >> What did I say? Steve? >> Steve. That's okay. >> Oh, I gave you a new name. >> You know, I get that all the time. >> I'm sorry, Scott. >> That's alright. >> This event, between five and six thousand people. >> Yes. >> You were saying in your general session in keynote this morning, that this is the fifth DockerCon. You started a few years ago with just 300 people and when I was walking out of the keynote this morning, I took a photograph, incredible. People as far as the eye can see. It was literally standing room only. >> It's crazy, right? And you think about four years ago, June 2014 when we did our very first DockerCon, here in San Francisco, 300 people, right? And we've gone from 300 to over 5,000 in that time, grown the community, grown the products, grown the partnerships and it's just, it's very humbling, honestly, to be part of something that's literally industry changing. >> You gave some great numbers during your keynote. You talked about 500 customers using Docker Enterprise Edition. >> Yes. >> Some big names. >> Yes. >> MET Life, Visa, PayPal, McKesson, who was on stage and that was a really interesting. McKesson is what, 183 years old? >> Healthcare company, yeah. >> Talking about data, life and death type of data. >> Right. >> Their transformation working with Docker and containers was really pretty impressive. >> It's exciting that companies get their hands on the technology and they start maybe on a small project or a small team but very quickly they see the potential impact of the solution and very quickly, it's almost infectious inside the organization and more and more teams want to jump on, understand how they can use it to help with their applications, their business to get impact in their operations and it just spreads, spreads like wildflower. That was really the story that McKesson was sharing, just how quickly they were seeing the adoption throughout their org. >> I thought that was really interesting and they did point it out on stage, how that developer adoption did help them go to the next level. >> Yes. >> And kind of transform their whole pipeline. >> Yes. >> Now Scott, you've been here the whole line of time and that through line has been, for Docker, that developer experience. >> That's exactly right. >> Now, as Product Lead here, you've got the Docker Desktop side and the Docker EE side and it's clear, there were some great announcements about desktop here, previews today but how do you balance the enterprise side with the developer centric desktop side and that developer experience idea? >> No, it's a great question, John. I'd reshape it almost to say, it's a continuous platform from developer experience to the operation side and you have to stand back and kind of see it as one and less about trading off one versus the other and how do you create an experience that carries all the way through. So a lot of Gareth's demonstration and the Lily Mason play, was showing how you can create apps in Docker very easily as a developer but those same artifacts that they put their apps in to carry all the way through into production, all the way through into operations. So it's about providing a consistent user experience, consistent set of artifacts that can be used by all the different personas that are building software so that they can be successful moving these Docker applications through the entire application development life cycle. Does that make sense? >> It does, thank you. I'd love to get your perspective, when you're talking with enterprises who might have some trepidation about the container journey, they probably know they have to do it to stay agile and competitive. I think in the press release, I believe it was you, that was quoted saying, "An estimated 85% of enterprise organizations are in a multi Cloud world." >> That's right. >> In a multi Cloud strategy. >> That's right. >> So when you're talking with customers, what's that executive conversation like? C level to C level, what are some of the main concerns that you hear and how influential are the developers in that C suite saying, "Hey guys, we've got to go this direction"? >> No, that's right. That's a great question, Lisa and what we hear again, and again, and again, is a realization going on in the C suite, that having software capabilities is strategic to their business, right? That was not always the case, as much as a decade ago, as recently as a decade ago, inside kind of big manufacturing businesses or big verticals that weren't kind of tech first, IT was a back office, right? It was not front and center but now they're seeing the disruption that software can have in other verticals and they're saying, "Wait a minute, we need to make software capabilities a core capability in our business." And who starts that whole cycle? It's the developers, right? If the developers can integrate with the lines of business, understand their objectives, understand how software can help them achieve those objectives, that's where it kicks off the whole process of, "Okay, we're going to build competitive applications. We then need an operations team to manage and deploy those applications to help us deploy them in a competitive way by taking them to the Cloud." So developers are absolutely pivotal in that conversation and core to helping these very large, Fortune 500, hundred year old companies, transform into new, agile, software driven businesses. >> Modernizing enterprise apps has been a theme >> Yes. >> also at Docker for a few years now. >> Yup. >> Up on stage Microsoft demonstrating the results of a multiyear partnership >> That's right. >> between Microsoft and Docker both with Docker integrating well with Windows server as well as, you talked about, Kubernetes now. >> That's right. >> Can you talk a little bit about what the implications of this are? The demo on stage, of course, was a very old enterprise app written in dot net, with just a few clicks, up and running in the Cloud on Kubernetes no less. >> That's right. >> Managed by Docker, that's actually very cool. You want to talk a little bit about, again, your conversations? >> Absolutely. >> Is this all about Cloud native or how much of your conversations are also supporting enterprise apps? >> Tying back to Lisa's question, so how do we help these organizations get started on their transformation? So they realize they need to transform, where do you start? Well guess what? 90% of their IT budget right now is going into these legacy applications and these legacy infrastructures, so if you start there and it can help modernize what they already have and bring it to modern platforms like Docker and Kubernetes, modern platforms like Window Server 2016, it's a modern operating system, modern platforms like Clouds, that's where you can create a lot of value out of existing application assets, reduce your costs, make these apps agile, even though they're thirteen years old and it's a way for the organization to start to get comfortable with the technology, to adopt it in a surface area that's very well known, to see results very, very quickly and then they gain the confidence to then spread it further into new applications, to spread it further into IOT, to spread it further into big data. But you've got to start it somewhere, right? So the MTA, Modernized Traditional Apps, is a very practical, pragmatic but also high, very quick, return way to get started. >> Oh, go ahead. >> Well I just, the other big announcement involving Kubernetes was managing Kubernetes in the Cloud and I wanted to make sure we hit that. >> That's right, that's right. >> Because I think if people aren't paying attention, they're just going to hear multi Cloud and they're going to go on and say, "Well everybody does multi Cloud, Docker's no different, Docker's just kind of catching up." Actually, this tech preview, I think, is a step forward. I think it's something- >> Thank you. >> I haven't actually seen in practice, so I'm kind of curious, again, how you as an engineering leader make those trade offs. Kind of talk a little bit about what you did and how deciding, "Well there's multi Cloud but the devil's in the details." You actually have integrated now with the native Kubernetes in these three Clouds, EKS, AKS and GKE. >> GKE, no that's right. No, it's a great question, John. The wonderful and fascinating but double edged sword of technology is that the race is always moving the abstraction up, right? You're always moving the abstraction up and you're always having to stay ahead and find where you can create real value for your customers. There was two factors that were going on, that you saw us kind of lean in to that and realize there's an opportunity here. One is, the Cloud providers are doing a wonderful job investing in Kubernetes and making it a manage service on their platforms, great. Now, let's take advantage of that because that's a horizontal infrastructure piece. At parallel we were seeing customers want to take advantage of these different Clouds but getting frustrated that every time they went to a different Cloud they were setting up another stack of process and tooling and automation and management and they're like, "Wait a minute. This is going to slow us down if we have to maintain these stacks." So we leaned in to that and said, "Okay, great. Let's take advantage of commoditized infrastructure, hosting Kubernetes. Let's also then take advantage of our ability to ingest and onboard them into Docker Enterprise Edition, and provide a consistent experience user based APIs, so that the enterprise doesn't get tied into these individual silos of tools, processes and stacks." Really, it's the combination of those two that you see a product opportunity emerged that we leaned heavily into and you saw the fruits of this morning. >> I saw a stat on the docker.com website that said that customers migrating to EE containers can reduce total cost by around 50%? >> Yes. >> That's a significant number. >> It's huge, right? You're reducing your cost of maintaining a ten year old app by 50% and you've made it Cloud portable, and you've made it more secure by putting it in the Docker container than outside and so it's like, "Why wouldn't you invest in that?" It shows a way to get comfortable with the technology, free up some cashflow that then you can pour back into additional innovation, so it's really a wonderful formula. That again, is why we start a lot of customers with their legacy applications because it has these types of benefits that gets them going in other parts of their business. >> And as you mentioned, 90% of an enterprise IT budget is spent keeping the lights on. >> That's right. >> Which means 10% for innovation and as we've talked about before, John, it's the aggressively innovating organizations that are the winners. >> That's exactly right and we're giving them tools, we're giving them a road map even, on how they can become an aggressively innovating organization. >> What about the visibility, in terms of, you know, an organization that's got eight different IT platforms, on prem, public Cloud, hybrid- >> Right. >> What are you doing with respect to being able to deliver visibility across containers and multiple clusters? >> That's right. Well that's a big part of today's announcement, was being able ... Every time we ingest one of these clusters, whether it's on prem, whether it's in the Cloud, whether it's a hosted Kubernetes cluster, that gives us that visibility of now we can manage applications across that, we can aggregate the logging, aggregate the monitoring. You can see, are your apps up, down, are they running out of resources? Do you need to load balance them to another cluster? So it's very much aligned with the vision that we shared on stage, which is fully federated management of the applications across clusters which includes visibility and all the tools necessary for that. >> Scott, I wanted to ask about culture and engineering culture >> Thank you. >> The DockerCon here is very, I think we called it humane in our intro, right? There's childcare on site, there's spoustivities, there's other places to take care of the people who are here and give them a great experience and a lot of training, of course, and things like that. But internally, engineering, there's a war for talent. Docker is very small compared to the Googles of the world but yet you have a very ambitious agenda. The theme of choice today, CLI versus GUI, Kubernetes versus Swarm, Lennox and Windows, not versus, Lennox and Windows, you know and, and, and, and now all these different Clouds and on prem. That's very ambitious and each "and" there takes engineering resources, so I'm kind of curious how the engineering team is growing, how you want to build the culture internally and how you use that to attract the right people? >> Well it certainly helps to be the start up that kicked off this entire movement, right? So a lot of credit to Solomon Hykes, our founder, and the original crew that ... Docker was a Skunkworks project in the previous version of our company and they had the vision to bring it forward and bring it to the world in an opensource model which at the time was a brand new language, go language. That was a catalyst that really got the company off and running in 2013/2014. We're staying true to that in that there's still a very strong opensource culture in the company and that attracts a lot of talent, as well as continuing to balance enterprise features and innovation and you see a combination of that on stage. You're also going to see a wonderful combination of that on the show floor, both from our own employees but also from the community. And I think that's the third dimension, John, which is being humble and call it "aware" that innovation doesn't just come from inside our four walls but that we give our engineers license to bring things in from the outside that add value to their projects. The Kubernetes is a great example of that, right? Our team saw the need for orchestration, we had our own IP in the form of Swarm, but they saw the capabilities of Kubernetes is very complimentary to that, or some customers were preferring to deploy that. So, no ifs, ands or buts, let's take advantage of that innovation, bring it inside the four walls and go. So, it's that kind of flexibility and awareness to attract great engineers who want to work on cutting edge, industry building technologies but also who are aware enough of, there's exciting things happening outside with the community and partnering with that community to bring those into the platform as well. >> So Scott, you guys are doing a lot of collaboration internally, but you're also doing a lot of collaboration with customers. How influential are customers to the development of Docker technologies? >> At ground zero, literally and we have at DockerCon, we call it a customer advisor group, where the customers who have been with us, who have deployed with us in production, we have them. And it's a very select group, it's about twelve to sixteen, and they tell us straight talk in terms of where it's working, where we need to improve. They give us feedback on the road map and so that happens every DockerCon, so that's once every six months. But then we actually have targets inside engineering and product management to be out in the field on a regular basis to make sure we're continuing to get that customer feedback. Innovation's a tricky balance, right? Because you want to be out in front and go where folks aren't asking you to, but you know there's opportunity, at the same time here, where they are today, and make sure you're not getting too far ahead. It's the old joke, Henry Ford, where if he's just listened to his customers, he would have made faster horses but instead he was listening to their problems, their real problems which was transportation and his genius, or his innovation, was to give them the Model T, right? We're trying to balance that ourselves inside Docker. Listen to customers but also know where the innovation, where the technology can take you to give you new solutions, hopefully many of which you saw on stage today. >> We did, well Scott, thanks so much for stopping by theCUBE again and sharing some of the exciting announcements that Docker has made and what you're doing to innovate internally and for the external enterprise community. We appreciate your time. >> Thank you, Lisa. Thank you, John. >> We want to thank you for watching theCUBE. Again, Lisa Martin with John Troyer, live in San Francisco at DockerCon 2018. Stick around, John and I will be right back with our next guest. (upbeat techno music)

>> Narrator: Live from Austin, Texas, it's the Cube, covering South by Southwest 2017. Brought to you by Intel. (electronic music) Now, here's John Furrier. >> Hello, and welcome to a special broadcast of Silicon Angles, the Cube. This is our flagship program. We go out to the events and extract the signal from the noise. We're here for a special broadcast, kicking off South by Southwest. This show is the center of the entertainment/media universe and we are here in the Intel AI Lounge, the hashtag Intel AI, and of course, hashtag The Cube, hashtag South by Southwest, and, again, South by Southwest, I call it the Burning Man for the tech industry, the music industry. It is where all the creative, the talented, and the innovators, the bomb throwers, the disrupters, and also the innovators building the next generation technologies. We're going to have wall-to-wall coverage, all day interviews here, and our theme this week at South by Southwest, is really powered by Intel AI, and that is, AI for social good. We're going to be unpacking all the cutting edge technology that's taking us into the next generation. What's this world look like with AI? What's this world look like with autonomous vehicles? These are significant shifts that we've never seen in the computer industry before. We're going to be breaking them down. And here to kickoff day one of our Cube coverage is, my friend, Brian Fanzo. iSocialFanz, is the founder. Great guy, young guy-- younger than me but, you know, still in the front lines. Brian, welcome to our kickoff. >> Thanks for having me. I like to be here. First time on the show was 2013, VM world. So, we were inside VM world, 2013, and now outside the Intel Lounge at South by. Pretty exciting. >> So, it's high noon here. We got our sunglasses on. High noon in Texas. I'm wearing my Ray Bans, but you have your Snapchat spectacles on. What's going on? Do you like them? Give us the update. >> Yeah, I'm actually a new user of them. I'm one who likes to jump on new technology, embrace the FOMO. I kind of waited a little bit on the specs. I also wanted to have something cool to release them with. After I got them, I decided to keep them in wraps until South by Southwest, but it's kind of fun. It's interactive. They are definitely-- now that you can buy them online, I think they're going to be seen a little bit more frequent, but here at South by, just walking down the streets, people are still stopping and saying, "Hey, take a picture of me," and, "How does it work?" I've been impressed. The quality's been pretty good, and it's really easy to use. I think battery life has a long way to go but we'll see. I think battery life in everything mobile has a long way to go. >> Well, that leads to our whole theme here. We're going to have Robert Scoble on, good friend, he's been doing a lot in Virtual Reality and AR, benpar, and a lot of scientists from Intel. Really, folks, talking about this kind of movement. There's a shift going on, user behavior shifting. You're seeing actually entrepreneurship, young companies coming out and changing the world, and not changing the world to go public and some of those vanity things around money, but really around social change, and that's our theme. You have been really prolific over the past couple years, this year in particular, going out, pounding the pavement. You've been at a zillion events. We see each other all the time. Of course, we do over a hundred events last year. You see a lot of stuff. What's the pattern that you're seeing out right now? In this new world order, there's certainly a couple key trends, and the big ones are autonomous vehicles, smart cities. Median entertainment's changing. The home, Alexa, Google Home, automation, but a paradigm shift is happening. What is your take on this? >> I think it comes down to, a lot of it, I think we've all realized we want an experience. Experience is extremely important. But what does an experience mean? And how do you make an experience stand out? I think that's one of the bigger problems today, is, with so much noise, so many things that are out there, I think a lot of people-- the idea of social good, people want to know that what they're working with, what they're working on, has a greater purpose. And I think, today's world, you're connected with no limitations, no silos, and not only being connected at all times, but how can you be connected at the right time and reach the right audience. I think technology like AI and some of the things-- especially cognitive, the idea that machines are learning with us, so it's not just machines learning and leaving the humans behind, but it's humans teaching machines, machines teaching humans, and then moving forward together. I think that's some exciting change. And it's from TV entertainment to enterprise tech, to even the social media space where I do a lot of work in. >> We're here in the Intel AI Lounge. We're on 77 Rainey St, so come by if you're watching here in South by Southwest. Always on Twitter. The hashtag is Intel AI at the Cube, ping us. Brian, the whole theme is here at Intel, and at South by Southwest, is real progressive thinkers, Intel's tag line is, "Your amazing starts with Intel." You start to see, even Intel, which powered the PC revolution, servers, are starting to make chips not just for machines anymore, for the Cloud, for cars. If you just think about autonomous vehicles, for instance. You think about what that does for the younger generation coming in, the computing landscape isn't about a device anymore, it's about an integrated experience, and one of the things we've been talking about on the Cube, and we're going to talk about this year, is, my vision of counterculture. >> Right. >> Every single movement, if you go look at the 60s, the computer industry was impacted by the counterculture of the 60s. You look at the PC revolution with Steve Jobs in the 80s, that was a counterculture. We're starting to see a counterculture now around new amazing new things. >> Brian: Right. >> With software, machine learning, AI-- I mean, it's mind boggling. >> Brian: It is. >> So, what is this counterculture? Do you have any thoughts on it? Do you agree, do you have any thoughts on that? >> I like to say, when Henry Ford said, that if he would've asked then what they wanted, they would have said faster horses not cars. I think today's generation has a bigger megaphone, is not afraid to say what they want, and because now, we have all of the data, they're not afraid to share that data. We're being much more transparent, allowing people to be a little bit more authentic with what they're sharing. I think we now have the opportunity to really shape new technology based on more data than we've ever had, more understanding of our consumers than we've ever had, and I like to say the consumer's no longer dumb, therefore, we have to start really pushing the boundaries. I love the tagline with awesome in it, because I think we are now creating awesome experiences and connecting things, probably in ways we would have never imagined. >> Yeah, I mean, one of the things we've been unpacking on Silicon Angle on the Cube, is this notion of all these trends that we're watching. A couple things we can talk about-- Delete Uber campaign came out of nowhere. The company's reeling because of one blog post by a woman who worked there, accusing the CEO of having a misogynistic culture. Fake news during the election. Global communication, now network, with instant sharing. We start to see these points where the voices of the internet of people is now part and disrupting traditional sacred cows, whether it's government, play, academia, so you can almost see it if you look at it and zoom out, you can say, "Woah, a new set of amazing things are happening, good and bad." >> Yeah, for sure, and I think, also, in that same realm, where now, it's kind of this idea where-- I think for the longest time, technology was taking us further away from the human condition, and we were able to be fake online, throw up a website, and really distance ourselves from the consumer and the community. And I believe now, because people are seeing through that, and the idea where people are faking profiles, we're now coming full circle where live video and a lot of these other things are saying, "Hey, we want humans, we want-- and then we want to be able to connect and come together." And I love the idea that we don't need-- a movement doesn't require a resume, doesn't require you to live in the same location. You can come together around a shared purpose, a shared passion, leveraging technology, and you can do it anywhere in the world. Especially from a mobile perspective, it's exciting to see people being able to have their voice heard, no matter where they are in the world. >> I mean, they literally-- I hate to use the phrase democratization, but that is really what's happening here, and if you look at how politics is changing and media-- the gatekeepers used to be a few parts of the world, whether it's a group of guys or a group of media companies or whatever, they were the gatekeepers. That's now leveled. You have now a leveling of that where you have these voices. So, what's happening, in my mind, is this whole AI for social good is super interesting to me because, if you think about it, the younger generation that's coming online right now and growing up into adulthood or teens is post-9/11 generation. When you think about 9/11, what that meant for our world, and now you're seeing the whole terrorist thing, these are people who are digital natives. There's a sense of, I won't say philanthropy, but societal thinking. >> And I think a part of it is, I think everyone has always wanted the ability to make a bigger impact on the world, but they also, now, I believe-- chapter three of my upcoming book is actually the future of marketing as social good, because I believe people want to know that what they're investing their money, their time in, has a greater purpose than themselves, and I think, because they're able to be connected, and we're able to expose cultures-- I mean, my daughter says good night to Alexa when she goes to bed, as if it's a human, and she's like, "Well, I got to say good night to it." It's this idea where, we're able to share, connect, and communicate-- computers are as much a part of that as humans are online, and it's an exciting movement because I think it's going to highlight and amplify the good and we're going to start to be able to drown out the noise and the bad that, before, oftentimes had a larger microphone and now, we're able to kind of equalize that. >> This is what I like about what Intel's doing. If you think about AI for social good. First of all, Intel benefits, thanks to Intel for sponsoring the Cube here, appreciate that. Plug for Intel. But if you know what they're doing under the hood, Intel makes chips. Moore's law has been one of those things that, for the folks who don't know, look it up on Google, Moore's law. Doubling the power every x-number of months, that creates really good processing power. That powers your glasses. That powers your car. The car is now a data center. The car is now an internet device. A human might have implants, chips some day. So this notion of the power, the computing power and now software's creation an amazing thing, but if you look at what you just said, it has nothing to do with computers. >> Brian: Right. >> So, computers are enabling us to do things and be connected, but if you think about that next generation of impact, it's going to come from human beings. Human beings, part of communities. And I think, if you look at the community dynamic, which has always been kind of like, oh yeah, I'm part of a community, but now, that there's intercommunication, your glasses are doing a streaming a video, we're doing a live broadcast, Twitter's out there, people can talk all over the place. You have a self-forming governance, a network. >> Which is awesome, because now, it's connecting great people no matter where you're at, you're not limited by your resume or where you grew up, and I also think there's an element here where, if you look at collaboration-- I believe collaboration is this key for the future of innovation. I think it's the idea of chips coming together with hardware and software, working together, not only in the post-product stage, but also in the innovation stage. And also, R&D Teams working together to now make things faster and smaller and able to really push the envelope. Things like, in the glasses, having sound and video, and having it connected to my phone, and transmitting with very little human input, we're now able to get perspectives that we would have never imagined, especially from just a regular person walking the streets. >> One of the things I want to get your thoughts on, because you're in the front lines, and also, I look at you, and you're not a young guy, you're an adult, but you're part of a new generation. I was talking with some folks at Stanford just last week around algorithms, and it's kind of an AI conversation, and something popped up. There is actually an issue of gender bias in algorithms. Who would have ever thought? So, now, there's kind of like algorithms for algorithms. This is kind of this AI for social good where, we don't want to actually start bringing our biases into the algorithms, so we have to always be monitoring that. But that brings up the whole point of-- Okay, we're living in a world of first time opportunities and problems and challenges. In the old days in the tech, we knew what the processes were: automated accounting software, automate this, automate some IT department, with unknown technology. And the technology would come out, like Intel and others-- now, we have unknown processes and problems, and known technology developing faster. So, what that's going to require is the human involvement, the communities to be very agile. >> Without question. Not only embrace change, but you also have to look at communities now where, I don't believe we are doing things massively different as humans today than we were years ago, we just now have more transparency and more exposure and access to all of our lives, and I think, with that becomes, as technology exposes more of our vulnerabilities, we as humans have to start to realize that people are more vulnerable and no one's perfect, and things are migrating in a different pattern. Give me that collaboration because we have to be able to trust the algorithms, there has to be that transparency there, but we also want some version of our own privacy, but I kind of live in the space where I don't think of privacy anymore. I think of things as transparently sharing, engaging, and then, hopefully, technology amplifying that and giving us the controls. >> And that's why I like how the AI for social good that Intel's doing here at South by, because it's not just the tech, it's the humanization of it, and South by Southwest represents a global culture of tech, creative tech practitioners, tech visionaries, futurists, kind of all kind of coming together. So, give us the update so far. You've been on the streets. You've been seeing folks last night. I've been on the influencers list last night on Facebook, there's a special group there, all our friends are on there. What's the update so far at South by Southwest, what's the current vibe, how do you see it going this week, what are some of the themes you see popping out of the woodwork at South by Southwest? >> I think last year was interesting. This is my third year in a row at South by, and I present and talk on a bunch of different topics, but I think last year, it was a lot about what is VR, and VR was shiny and fancy, and the conversations now seem to be, what is VR doing, what's the content look like, and where is it going and how do I get there. That's an exciting conversation because, I think, instead of it being a shiny object, it's now VR and AR and AI, how do they intertwine into our lives. The idea of interactive-- South by Southwest Interactive, really what these tools and technology are, is connecting that interactive capabilities. It's interesting to see the different car brands here. You have Intel, you have Dell, you have IBM, but then you also have some of these other brands that are trying to push the, I'd say, the startup agenda. That's exciting, because I remember, I wasn't here for Twitter when you were here for Twitter, but Meerkat, two years ago, for me, was the darling live streaming app that launched here, and it died a year later, but I'm glad to see that innovation and the startup culture is now mixing, kind of hand-in-hand with the enterprise. >> Well, I'm going to see some of my old peeps from the Web 2.0 days, and a lot of people were like, "Oh, the Web 2.0 days didn't happen," just like the bubble burst and the internet bubble, and that burst, but it all happened. Everything that was put out there, pets online, everything online went online. Everything that was promoted in Web 2.0 is happening now, so I believe that you're seeing now the absolute operationalizing, the globalization of democratization. The technology has now come with software for that democratization and now, what's exciting is, with machine learning, data sets, and all the stuff happening with the cloud technology and 5G, it's going to get faster now. >> Which is exciting, because I think real time is a powerful element, but if you're able to get multiple senses of data, interact with machines, and ultimately push that forward at the right time, I think that collaboration of machine, human, and experience at the right time is where we start pushing new innovations. AR and VR, even some of this cognitive type learning, starts hitting to mainstream, which I'm excited about because, I think, we're getting to this culture now where we look at change and we're hopefully now embracing the opportunities rather than looking and saying what you do. I think, now we're realizing no one cares what the product is, we want to know how does it impact us and why should we care. >> Brian Fanzo, new generation, a millennial, making things happen out there, checking things out. Of course, iSocialFanz is his Twitter handle, check him out. Always great content, always out there, the canary in the coalmine, poking at the new stuff and analyzing it and sharing it, oversharing, as some people would say, but not in my book. Always great to have you on. Good to see you. Thanks for spending the time, taking off our AI Lounge. >> My pleasure. Happy South by Southwest. >> Alright, we'll be back with more Intel AI Lounge after this short break. Hashtag Intel AI. I'm John Furrier with the Cube. We'll be right back. (electronic music)

>> Narrator: Live from New York, it's theCUBE. Covering the IBM Machine Learning Launch Event. Brought to you by IBM. Now, here are your hosts, Dave Vellante and Stu Miniman. >> Welcome back to New York City, everybody this is theCUBE, we're here at the IBM Machine Learning Launch Event, Rob Thomas is here, he's the general manager of the IBM analytics group. Rob, good to see you again. >> Dave, great to see you, thanks for being here. >> Yeah it's our pleasure. So two years ago, IBM announced the Z platform, and the big theme was bringing analytics and transactions together. You guys are sort of extending that today, bringing machine learning. So the news just hit three minutes ago. >> Rob: Yep. >> Take us through what you announced. >> This is a big day for us. The announcement is we are going to bring machine learning to private Clouds, and my observation is this, you look at the world today, over 90% of the data in the world cannot be googled. Why is that? It's because it's behind corporate firewalls. And as we've worked with clients over the last few years, sometimes they don't want to move their most sensitive data to the public Cloud yet, and so what we've done is we've taken the machine learning from IBM Watson, we've extracted that, and we're enabling that on private Clouds, and we're telling clients you can get the power of machine learning across any type of data, whether it's data in a warehouse, a database, unstructured content, email, you name it we're bringing machine learning everywhere. To your point, we were thinking about, so where do we start? And we said, well, what is the world's most valuable data? It's the data on the mainframe. It's the transactional data that runs the retailers of the world, the banks of the world, insurance companies, airlines of the world, and so we said we're going to start there because we can show clients how they can use machine learning to unlock value in their most valuable data. >> And which, you say private Cloud, of course, we're talking about the original private Cloud, >> Rob: Yeah. >> Which is the mainframe, right? >> Rob: Exactly. >> And I presume that you'll extend that to other platforms over time is that right? >> Yeah, I mean, we're going to think about every place that data is managed behind a firewall, we want to enable machine learning as an ingredient. And so this is the first step, and we're going to be delivering every quarter starting next quarter, bringing it to other platforms, other repositories, because once clients get a taste of the idea of automating analytics with machine learning, what we call continuous intelligence, it changes the way they do analytics. And, so, demand will be off the charts here. >> So it's essentially Watson ML extracted and placed on Z, is that right? And describe how people are going to be using this and who's going to be using it. >> Sure, so Watson on the Cloud today is IBM's Cloud platform for artificial intelligence, cognitive computing, augmented intelligence. A component of that is machine learning. So we're bringing that as IBM machine learning which will run today on the mainframe, and then in the future, other platforms. Now let's talk about what it does. What it is, it's a single-place unified model management, so you can manage all your models from one place. And we've got really interesting technology that we pulled out of IBM research, called CADS, which stands for the Cognitive Assistance for Data Scientist. And the idea behind CADS is, you don't have to know which algorithm to choose, we're going to choose the algorithm for you. You build your model, we'll decide based on all the algorithms available on open-source what you built for yourself, what IBM's provided, what's the best way to run it, and our focus here is, it's about productivity of data science and data scientists. No company has as many data scientists as they want, and so we've got to make the ones they do have vastly more productive, and so with technology like CADS, we're helping them do their job more efficiently and better. >> Yeah, CADS, we've talked about this in theCUBE before, it's like an algorithm to choose an algorithm, and makes the best fit. >> Rob: Yeah. >> Okay. And you guys addressed some of the collaboration issues at your Watson data platform announcement last October, so talk about the personas who are asking you to give me access to mainframe data, and give me, to tooling that actually resides on this private Cloud. >> It's definitely a data science persona, but we see, I'd say, an emerging market where it's more the business analyst type that is saying I'd really like to get at that data, but I haven't been able to do that easily in the past. So giving them a single pane of glass if you will, with some light data science experience, where they can manage their models, using CADS to actually make it more productive. And then we have something called a feedback loop that's built into it, which is you build a model running on Z, as you get new data in, these are the largest transactional systems in the world so there's data coming in every second. As you get new data in, that model is constantly updating. The model is learning from the data that's coming in, and it's becoming smarter. That's the whole idea behind machine learning in the first place. And that's what we've been able to enable here. Now, you and I have talked through the years, Dave, about IBM's investment in Spark. This is one of the first, I would say, world-class applications of Spark. We announced Spark on the mainframe last year, what we're bringing with IBM machine learning is leveraging Spark as an execution engine on the mainframe, and so I see this as Spark is finally coming into the mainstream, when you talk about Spark accessing the world's greatest transactional data. >> Rob, I wonder if you can help our audience kind of squint through a compare and contrast, public Cloud versus what you're offering today, 'cause one thing, public Cloud adding new services, machine learning seemed like one of those areas that we would add, like IBM had done with a machine learning platform. Streaming, absolutely you hear mobile streaming applications absolutely happened in the public Cloud. Is cost similar in private Cloud? Can I get all the services? How will IBM and your customer base keep up with that pace of innovation that we've seen from IBM and others in the public Cloud on PRIM? >> Yeah, so, look, my view is it's not an either or. Because when you look at this valuable data, clients want to do some of it in public Cloud, they want to keep a lot of it in the system that they built on PRIMA. So our job is, how do we actually bridge that gap? So I see machine learning like we've talked about becoming much more of a hybrid capability over time because the data they want to move to the Cloud, they should do that. The economics are great. The data, doing it on private Cloud, actually the economics are tremendous as well. And so we're delivering an elastic infrastructure on private Cloud as well that can scale the public Cloud. So to me it's not either or, it's about what everybody wants as Cloud features. They want the elasticity, they want a creatable interface, they want the economics of Cloud, and our job is to deliver that in both places. Whether it's on the public Cloud, which we're doing, or on the private Cloud. >> Yeah, one of the thought exercises I've gone through is if you follow the data, and follow the applications, it's going to show you where customers are going to do things. If you look at IOT, if you look at healthcare, there's lots of uses that it's going to be on PRIMA it's going to be on the edge, I got to interview Walmart a couple of years ago at the IBM Ed show, and they leveraged Z globally to use their sales, their enablement, and obviously they're not going to use AWS as their platform. What's the trends, what do you hear form their customers, how much of the data, are there reasons why it needs to stay at the edge? It's not just compliance and governance, but it's just because that's where the data is and I think you were saying there's just so much data on the Z series itself compared to in other environments. >> Yeah, and it's not just the mainframe, right? Let's be honest, there's just massive amounts of data that still sits behind corporate firewalls. And while I believe the end destination is a lot of that will be on public Cloud, what do you do now? Because you can't wait until that future arrives. And so the place, the biggest change I've seen in the market in the last year is clients are building private Clouds. It's not traditional on-premise deployments, it's, they're building an elastic infrastructure behind their firewall, you see it a lot in heavily-regulated industries, so financial services where they're dealing with things like GDPR, any type of retailer who's dealing with things like PCI compliance. Heavy-regulated industries are saying, we want to move there, but we got challenges to solve right now. And so, our mission is, we want to make data simple and accessible, wherever it is, on private Cloud or public Cloud, and help clients on that journey. >> Okay, so carrying through on that, so you're now unlocking access to mainframe data, great, if I have, say, a retail example, and I've got some data science, I'm building some models, I'm accessing the mainframe data, if I have data that's elsewhere in the Cloud, how specifically with regard to this announcement will a practitioner execute on that? >> Yeah, so, one is you could decide one place that you want to land your data and have it be resonant, so you could do that. We have scenarios where clients are using data science experience on the Cloud, but they're actually leaving the data behind the firewalls. So we don't require them to move the data, so our model is one of flexibility in terms of how they want to manage their data assets. Which I think is unique in terms of IBM's approach to that. Others in the market say, if you want to use our tools, you have to move your data to our Cloud, some of them even say as you click through the terms, now we own your data, now we own your insights, that's not our approach. Our view is it's your data, if you want to run the applications in the Cloud, leave the data where it is, that's fine. If you want to move both to the Cloud, that's fine. If you wanted to leave both on private Cloud, that's fine. We have capabilities like Big SQL where we can actually federate data across public and private Clouds, so we're trying to provide choice and flexibility when it comes to this. >> And, Rob, in the context of this announcement, that would be, that example you gave, would be done through APIs that allow me access to that Cloud data is that right? >> Yeah, exactly, yes. >> Dave: Okay. >> So last year we announced something called Data Connect, which is basically, think of it as a bus between private and public Cloud. You can leverage Data Connect to seamlessly and easily move data. It's very high-speed, it uses our Aspera technology under the covers, so you can do that. >> Dave: A recent acquisition. >> Rob, IBM's been very active in open source engagement, in trying to help the industry sort out some of the challenges out there. Where do you see the state of the machine learning frameworks Google of course has TensorFlow, we've seen Amazon pushing at MXNet, is IBM supporting all of them, there certain horses that you have strong feelings for? What are your customers telling you? >> I believe in openness and choice. So with IBM machine learning you can choose your language, you can use Scala, you can use Java, you can use Python, more to come. You can choose your framework. We're starting with Spark ML because that's where we have our competency and that's where we see a lot of client desire. But I'm open to clients using other frameworks over time as well, so we'll start to bring that in. I think the IT industry always wants to kind of put people into a box. This is the model you should use. That's not our approach. Our approach is, you can use the language, you can use the framework that you want, and through things like IBM machine learning, we give you the ability to tap this data that is your most valuable data. >> Yeah, the box today has just become this mosaic and you have to provide access to all the pieces of that mosaic. One of the things that practitioners tell us is they struggle sometimes, and I wonder if you could weigh in on this, to invest either in improving the model or capturing more data and they have limited budget, and they said, okay. And I've had people tell me, no, you're way better off getting more data in, I've had people say, no no, now with machine learning we can advance the models. What are you seeing there, what are you advising customers in that regard? >> So, computes become relatively cheap, which is good. Data acquisitions become relatively cheap. So my view is, go full speed ahead on both of those. The value comes from the right algorithms and the right models. That's where the value is. And so I encourage clients, even think about maybe you separate your teams. And you have one that's focused on data acquisition and how you do that, and another team that's focused on model development, algorithm development. Because otherwise, if you give somebody both jobs, they both get done halfway, typically. And the value is from the right models, the right algorithms, so that's where we stress the focus. >> And models to date have been okay, but there's a lot of room for improvement. Like the two examples I like to use are retargeting, ad retargeting, which, as we all know as consumers is not great. You buy something and then you get targeted for another week. And then fraud detection, which is actually, for the last ten years, quite good, but there's still a lot of false positives. Where do you see IBM machine learning taking that practical use case in terms of improving those models? >> Yeah, so why are there false positives? The issue typically comes down to the quality of data, and the amount of data that you have that's why. Let me give an example. So one of the clients that's going to be talking at our event this afternoon is Argus who's focused on the healthcare space. >> Dave: Yeah, we're going to have him on here as well. >> Excellent, so Argus is basically, they collect data across payers, they're focused on healthcare, payers, providers, pharmacy benefit managers, and their whole mission is how do we cost-effectively serve different scenarios or different diseases, in this case diabetes, and how do we make sure we're getting the right care at the right time? So they've got all that data on the mainframe, they're constantly getting new data in, it could be about blood sugar levels, it could be about glucose, it could be about changes in blood pressure. Their models will get smarter over time because they built them with IBM machine learning so that what's cost-effective today may not be the most effective or cost-effective solution tomorrow. But we're giving them that continuous intelligence as data comes in to do that. That is the value of machine learning. I think sometimes people miss that point, they think it's just about making the data scientists' job easier, that productivity is part of it, but it's really about the voracity of the data and that you're constantly updating your models. >> And the patient outcome there, I read through some of the notes earlier, is if I can essentially opt in to allow the system to adjudicate the medication or the claim, and if I do so, I can get that instantaneously or in near real-time as opposed to have to wait weeks and phone calls and haggling. Is that right, did I get that right? >> That's right, and look, there's two dimensions. It's the cost of treatment, so you want to optimize that, and then it's the effectiveness. And which one's more important? Well, they're both actually critically important. And so what we're doing with Argus is building, helping them build models where they deploy this so that they're optimizing both of those. >> Right, and in the case, again, back to the personas, that would be, and you guys stressed this at your announcement last October, it's the data scientist, it's the data engineer, it's the, I guess even the application developer, right? Involved in that type of collaboration. >> My hope would be over time, when I talked about we view machine learning as an ingredient across everywhere that data is, is you want to embed machine learning into any applications that are built. And at that point you no longer need a data scientist per se, for that case, you can just have the app developer that's incorporating that. Whereas another tough challenge like the one we discussed, that's where you need data scientists. So think about, you need to divide and conquer the machine learning problem, where the data scientist can play, the business analyst can play, the app developers can play, the data engineers can play, and that's what we're enabling. >> And how does streaming fit in? We talked earlier about this sort of batch, interactive, and now you have this continuous sort of work load. How does streaming fit? >> So we use streaming in a few ways. One is very high-speed data ingest, it's a good way to get data into the Cloud. We also can do analytics on the fly. So a lot of our use case around streaming where we actually build analytical models into the streaming engine so that you're doing analytics on the fly. So I view that as, it's a different side of the same coin. It's kind of based on your use case, how fast you're ingesting data if you're, you know, sub-millisecond response times, you constantly have data coming in, you need something like a streaming engine to do that. >> And it's actually consolidating that data pipeline, is what you described which is big in terms of simplifying the complexity, this mosaic of a dupe, for example and that's a big value proposition of Spark. Alright, we'll give you the last word, you've got an audience outside waiting, big announcement today; final thoughts. >> You know, we talked about machine learning for a long time. I'll give you an analogy. So 1896, Charles Brady King is the first person to drive an automobile down the street in Detroit. It was 20 years later before Henry Ford actually turned it from a novelty into mass appeal. So it was like a 20-year incubation period where you could actually automate it, you could make it more cost-effective, you could make it simpler and easy. I feel like we're kind of in the same thing here where, the data era in my mind began around the turn of the century. Companies came onto the internet, started to collect a lot more data. It's taken us a while to get to the point where we could actually make this really easy and to do it at scale. And people have been wanting to do machine learning for years. It starts today. So we're excited about that. >> Yeah, and we saw the same thing with the steam engine, it was decades before it actually was perfected, and now the timeframe in our industry is compressed to years, sometimes months. >> Rob: Exactly. >> Alright, Rob, thanks very much for coming on theCUBE. Good luck with the announcement today. >> Thank you. >> Good to see you again. >> Thank you guys. >> Alright, keep it right there, everybody. We'll be right back with our next guest, we're live from the Waldorf Astoria, the IBM Machine Learning Launch Event. Be right back. [electronic music]

>> 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. >> I already We're back Dave along with Student of American Nelson and Macca Fear are back here after the day Each of them gave a detailed presentation today related to the book Gentlemen, welcome back to to see you >> Good to see you again I want to start with you >> on a question. That last question That and he got from a woman when you're >> starting with him on a question that was asked of him Yes. And you'LL see why when you find something you like. You dodged the question by the way. Fair for record Hanging out with you guys makes us smarter. Thank you. Hear it? So the question was >> around education She expressed real concern, particularly around education for younger people. I guess by the time they get to secondary education it's too late. You talked about in the book about the three r's we need to read. Obviously we need to write Teo be able to do arithmetic in our head. Sure. What's your take on that on that question. You >> know those basics, our table stakes. I mean, you have to be able to do that kind of stuff. But the real payoff comes from creativity doing something really new and original. The good news is that most people love being creative and original. You look at a kid playing, you know, whether it there two or three years old, that's all that you put some blocks in front of them. They start building, creating things, and our school system is, Andy was saying in his his talkers, questions was, is that many of the schools are almost explicitly designed to tamp that down to get people to conform, get them to all be consistent. Which is exactly what Henry Ford needed for his factories, you know, to work on the assembly line. But now that machines could do that repetitive, consistent kind of work, it's time to let creativity flourish again. And that's when you got to do on top of those basic skills. >> So I have one, and it's pretty clear that that that are Kramer education model. It's really hard for some kids to accept. They just want they want to run around. They want to go express themselves. They wantto poke a world. That's not what that grid full of desks is designed to do. >> We call that a d d. Now I follow. Yeah, I have one >> Montessori kid out of my foot. Really? He's by far the most creative most ano didactic. You're a Montessori Travel Marie, not the story. Have it right? Is that >> Look, I'm not educational research. I am Amon a story kid. I think she got it right. And she was able to demonstrate that she could take kids out of the slums of Bologna who were, at the time considered mentally defective. There's this notion that the reason the poor are poor because they were they were just mentally insufficient. And she could show their learning and their progress. So I completely agree with Eric. We need all of our students need to be able to Teo, accomplish the basics, to read, to write, to do basic math. What Montessori taught me is you can get there via this completely kind of hippie freeform route. And I'm really happy for that education talk. Talk about you and your students. >> Your brainstorm on things that people could do with computers. Can't. >> Yeah, a lot of money >> this and exercise that you do pretty regularly. What's that? How is >> that evolved? A little >> something. We do it more systematically, I almost always doing in at talking over where With Forum. It's a kind of dinner conversation out we can't get away from. So we're hearing a lot. And you know, there's a recurring patterns that emerged, and you heard some of them today around interpersonal skills around creativity. Still, coordination is still physical coordination. What some of these have in common is that their skills that we've evolved over literally, you know, hundreds of thousands or millions of years. And there are billions of neurons devoted to some of these skills. Coordination, vision, interpersonal skills and other skills like arithmetic is something that's really very recent, and we don't have a lot of neurons devoted to that. So it's not surprising the machines can pick up those more recent skills more than the Maurin eight ones. Now overtime, will machines be able to do more of those other skills? I suspect they probably will exactly how long it will take. That's the question for neuroscientists. The AI researchers >> made me make that country think about not just diagnosing a patient but getting them to comply with the treatment regimen. Take your medicine. Eat better. Stop smoking. We know the compliance rates for terrible for demonstrably good ideas. How do we improve them? Is in a technology solution a little bit. Is it an interpersonal solution? Absolutely. I think we need deeply empathetic, deeply capable people to help each other become healthier, become better people. Right Program might come from an algorithm, but that algorithm on the computer that spits it out is going to be lousy at getting most people to comply. Way need human beings for that. So when >> we talking technology space, we've been evangelizing that people need to get rid of what we call the undifferentiated having lifting. And I wonder if there's an opportunity in our personal life, you think about how much time we spend Well, you know, what are we doing for dinner when we're running the kids around? You know, how do I get dressed in the different things that have here their studies sometimes like waste so much brain power, trying to get rid of these things and there's opportunities. Welcome, Jetsons. Actually, no, they >> didn't have these problems that can help us with some of that. I think people should actually help us with over of it. You know, I actually I have a personal trainer and he's one of the last people that I would ever have exclude from my life because he's the guy who could actually help me lead a healthier life. And I play so much value on that. >> I like your metaphor of this is undifferentiated stuff, that really it's not the stuff that makes you great. It's just stuff you have to do. And I remember having a conversation with folks that s AP, and they said, you know, sure would like to brag about this, but we take away a lot of stuff that isn't what differentiates companies in the back office stuff. Getting your basic bookkeeping, accounting, supply chain stuff done and it's interesting. I think we could use the same thing for for personal lives. Let's get rid of that sort of underbrush of necessity stuff so we can focus on the things that are uniquely good at >> alright so way have to run out when I need garbage bags with toilet paper. Honestly, a drone should show up and drop that on my friends. >> So I wonder when I look at the self driving car that you've talked about, will we reach a point that not only do we trust computers in the car, it's cars to drive herself? But we've reached a point where we're just got nothing. Trust humans anymore because self driving cars there just so much safer and better than what we've got is that coming >> in the next twenty years? I personally think so, and the first time is deeply weird and unsettling. I think both of us were a little bit terrified the first time we drove in the Google Autonomous Car and the Google or driving it hit the button and took his hands off the controls. That was a weird moment. I liken it to when I was learning to scuba dive. Very first breath you take underwater is deeply unsettling because you're not supposed to be doing this. After a few breaths, it becomes background. >> But you know, I was I was driving to the airport to come here, and I look in the lanes left to me. There's a woman, you know, texting, and I'd be much you're terrifying if she wasn't driving. If the computer is doing because then we could be more, that's the right way to think about it. I think the time will come and it may not be that far away. We're the norm's shift exactly the other way around and be considered risky to have a human at the wheel and the safety. That thing that the insurance company will want is to have a machine there. You know, I think this is a temporary phase with Newt technology. We become frightened of them. When microwave ovens first came out, they were weird and wonderful. Not most of us think of them is really kind of boring and routine. Same thing is gonna happen with self driving to accidents. Well, that's the story is, that is, But none of them were. Of course, according to the story >> driving, what's clear is that they're safer than the human driver. As of today, they are only going to get safer. We're not evolving that quick, >> but you got the question. Is that self driving, car driven story? Dr. We laughed because we're live in Boston. But your answer was, Will drive started driving, driving, >> you know, eventually, you know, I think it's fair to say that there's a big difference. You know, the first nineteen, ninety five, ninety nine percent of driving is something that's a lot easier. That last one percent or one hundredth of one percent becomes much, much harder. And right now we've had There's a card just last week that drove across the United States, but there were half a dozen times when he had to have a human interviews and particularly unusual situations. And I think because of our norms and expectations, that won't be enough for a self driving car to be safer than humans will need it to be te next paper or something like maybe >> like the just example may be the ultimate combination is a combination of human and self driving car, >> Maybe situation after situation. I think that's going to be the case and I'LL go back to medical diagnosis. I would at least for the short to medium term, I would like to have a pair of human eyes over the treatment plan that the that being completely digital diagnostician spits out. Maybe over time it will be clear that there are no flaws in that. We could go totally digital, but we can combine the two. >> I think in most cases what anything is right, what you brought up. But you know the case of self driving cars in particular, and other situations where humans have to take over for a machine that's failing for someway like aircraft. When the autopilot is doing things right, it turns out that that transition could be very, very rocky and expecting a human to be on call to be able to quickly grasp what's going on in the middle of a crisis of a freak out that's not reasonable isn't necessarily the best time to be swishing over. So there's a there's a fuel. Human factors issued their of how you design it, not just to the human could take over, but you could make a kind of a seamless transition. And that's not easy. >> Okay, so maybe self driving cars, that doesn't happen. But back to the medical example. Maybe Watson will replace Dr Welby, but have not Dr Oz >> interaction or any nurse or somebody who actually gets me to comply again. But also, I do think that Dr Watson can and should take over for people in the developing world who only have access instead of First World medical care. They've got a smartphone. OK, we're going to be able to deliver absolute top shelf world class medical diagnostics to those people fairly quickly. Of course, we should >> do that and then combine it with a coach who gets people to take the prescription when they're supposed to do it, change their eating habits or communities or whatever else you hear your peers are all losing weight. >> Why aren't you? >> I wantto askyou something coming on. Time here has been gracious with your time and your talk. We're very out spoken about. A couple of things I would summarize. It is you lot must Bill Gates and Stephen Hawking. You're paranoid tens. There's no privacy in the Internet, so get over. >> I didn't say there's no privacy. I know working. I think it's important to be clear on this. I think privacy is really important. I do think it's right that we have, and we should have. What I don't want to do is have a bureaucrat defined my privacy rights for me and start telling >> companies what they can and can't do is a result. What >> I'd much prefer instead is to say, Look, if there are things that we know >> Cos we're doing that we do not approve >> of let's deal with that situation as opposed to trying to put the guard rails in place and fence off the different kinds of innovative, strict growth, right? >> I mean, there's two kinds of mistakes you could make. One is, you can let companies do things and you should have regulated them. The other is. You could regulate them preemptively when you really should have let them do things and both kinds of errors or possible. Our sense of looking at what's happening in Jinan is that we've thrived where we allow more permission, listen innovation. We allowed companies to do things and then go back and fix things rather than when we try and locked down the past in the existing processes, so are leaning. In most cases, not every case is to be a little more free, a little more open recognized that there will be mistakes. It's not gonna be that we're perfectly guaranteed is that there is a risk when you walk across the street but go back and fix things at that point rather than preemptively define exactly how things are gonna play. Let >> me give you an example. If Google were to say to me, Hey, Andy, unless you pay us x dollars per month, we're gonna show the world your last fifty Google searches. I would completely pay for that kind of blackmail, right? Certain your search history is incredibly personal reveals a lot about you. Google is not going to do that. It would just it would crater their own business. So trying to trying to fence that kind of stuff often advance makes a lot of sense to me. Then then then relying on this. This sounds a little bit weird, but a combination of for profit companies and people with three choice that that's a really good guarantor of our freedoms and our rights. So you >> guys have a pretty good thing going. It doesn't look like strangle each other anytime soon. But >> how do you How do you decide who >> does one treat by how you operate with reading the book? It's like, Okay, like I think that was Andy because he's talking about Erica. I think that was Erica's. He's talking, >> but I couldn't tell you. I think it's hard for you to reverse engineer because it gets so co mingled over time. And, you know, I gave the example the end of the talk about humans and machines working together synergistically. I think the same thing is true with Indian me out. You may disagree, but I find that we are smarter when we work together so much smarter. Then when we work individually, we go and bring some things on the blackboard. And I had these aha moments that I don't think I would've had just sitting by myself and do I should be that ah ha moment to Andy. To me, it's actually to this Borg of us working together >> and fundamentally, these air bumper sticker things to say. If after working with someone, you become convinced that they respect you and that you could trust them and like Erik says that you're better off together, that you would be individually, it's a complete no brainer to >> keep doing the work together. Well, we're really humbled to be here. You guys are great contact. Everything is free and available. We really believe in that sort of economics. And so thank you very much for having us here. >> Well, it's just a real pleasure. >> All right, Right there, buddy. We'LL be back to wrap up right after this is Q relied from London. My tea.

>> 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