>> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Hi everybody. Welcome back to Barcelona. We're here at the Fira Live, theCUBE's ongoing coverage of day two of MWC 23. Back in 2021 was my first Mobile World Congress. And you know what? It was actually quite an experience because there was nobody there. I talked to my friend, who's now my co-host, Chris Lewis about what to expect. He said, Dave, I don't think a lot of people are going to be there, but Danielle Royston is here and she's the CEO of Totoge. And that year when Erickson tapped out of its space she took out 60,000 square feet and built out Cloud City. If it weren't for Cloud City, there would've been no Mobile World Congress in June and July of 2021. DR is back. Great to see you. Thanks for coming on. >> It's great to see you. >> Chris. Awesome to see you. >> Yeah, Chris. Yep. >> Good to be back. Yep. >> You guys remember the narrative back then. There was this lady running around this crazy lady that I met at at Google Cloud next saying >> Yeah. Yeah. >> the cloud's going to take over Telco. And everybody's like, well, this lady's nuts. The cloud's been leaning in, you know? >> Yeah. >> So what do you think, I mean, what's changed since since you first caused all those ripples? >> I mean, I have to say that I think that I caused a lot of change in the industry. I was talking to leaders over at AWS yesterday and they were like, we've never seen someone push like you have and change so much in a short period of time. And Telco moves slow. It's known for that. And they're like, you are pushing buttons and you're getting people to change and thank you and keep going. And so it's been great. It's awesome. >> Yeah. I mean, it was interesting, Chris, we heard on the keynotes we had Microsoft, Satya came in, Thomas Curian came in. There was no AWS. And now I asked CMO of GSMA about that. She goes, hey, we got a great relationship with it, AWS. >> Danielle: Yeah. >> But why do you think they weren't here? >> Well, they, I mean, they are here. >> Mean, not here. Why do you think they weren't profiled? >> They weren't on the keynote stage. >> But, you know, at AWS, a lot of the times they want to be the main thing. They want to be the main part of the show. They don't like sharing the limelight. I think they just didn't want be on the stage with the Google CLoud guys and the these other guys, what they're doing they're building out, they're doing so much stuff. As Danielle said, with Telcos change in the ecosystem which is what's happening with cloud. Cloud's making the Telcos think about what the next move is, how they fit in with the way other people do business. Right? So Telcos never used to have to listen to anybody. They only listened to themselves and they dictated the way things were done. They're very successful and made a lot of money but they're now having to open up they're having to leverage the cloud they're having to leverage the services that (indistinct words) and people out provide and they're changing the way they work. >> So, okay in 2021, we talked a lot about the cloud as a potential disruptor, and your whole premise was, look you got to lean into the cloud, or you're screwed. >> Danielle: Yeah. >> But the flip side of that is, if they lean into the cloud too much, they might be screwed. >> Danielle: Yeah. >> So what's that equilibrium? Have they been able to find it? Are you working with just the disruptors or how's that? >> No I think they're finding it right. So my talk at MWC 21 was all about the cloud is a double-edged sword, right? There's two sides to it, and you definitely need to proceed through it with caution, but also I don't know that you have a choice, right? I mean, the multicloud, you know is there another industry that spends more on CapEx than Telco? >> No. >> Right. The hyperscalers are doing it right. They spend, you know, easily approaching over a $100 billion in CapEx that rivals this industry. And so when you have a player like that an industry driving, you know and investing so much Telco, you're always complaining how everyone's riding your coattails. This is the opportunity to write someone else's coattails. So jump on, right? I think you don't have a choice especially if other Telco competitors are using hyperscalers and you don't, they're going to be left behind. >> So you advise these companies all the time, but >> I mean, the issue is they're all they're all using all the hyperscalers, right? So they're the multi, the multiple relationships. And as Danielle said, the multi-layer of relationship they're using the hyperscalers to change their own internal operational environments to become more IT-centric to move to that software centric Telco. And they're also then with the hyperscalers going to market in different ways sometimes with them, sometimes competing with them. What what it means from an analyst point of view is you're suddenly changing the dynamic of a market where we used to have nicely well defined markets previously. Now they're, everyone's in it together, you know, it's great. And, and it's making people change the way they think about services. What I, what I really hope it changes more than anything else is the way the customers at the end of the, at the end of the supply, the value chain think this is what we can get hold of this stuff. Now we can go into the network through the cloud and we can get those APIs. We can draw on the mechanisms we need to to run our personal lives, to run our business lives. And frankly, society as a whole. It's really exciting. >> Then your premise is basically you were saying they should ride on the top over the top of the cloud vendor. >> Yeah. Right? >> No. Okay. But don't they lose the, all the data if they do that? >> I don't know. I mean, I think the hyperscalers are not going to take their data, right? I mean, that would be a really really bad business move if Google Cloud and Azure and and AWS start to take over that, that data. >> But they can't take it. >> They can't. >> From regulate, from sovereignty and regulation. >> They can't because of regulation, but also just like business, right? If they started taking their data and like no enterprises would use them. So I think, I think the data is safe. I think you, obviously every country is different. You got to understand the different rules and regulations for data privacy and, and how you keep it. But I think as we look at the long term, right and we always talk about 10 and 20 years there's going to be a hyperscaler region in every country right? And there will be a way for every Telco to use it. I think their data will be safe. And I think it just, you're going to be able to stand on on the shoulders of someone else for once and use the building blocks of software that these guys provide to make better experiences for subscribers. >> You guys got to explain this to me because when I say data I'm not talking about, you know, personal information. I'm talking about all the telemetry, you know, all the all the, you know the plumbing. >> Danielle: Yeah. >> Data, which is- >> It will increasingly be shared because you need to share it in order to deliver the services in the streamline efficient way that needs to be deliver. >> Did I hear the CEO of Ericsson Wright where basically he said, we're going to charge developers for access to that data through APIs. >> What the Ericsson have done, obviously with the Vage acquisition is they want to get into APIs. So the idea is you're exposing features, quality policy on demand type features for example, or even pulling we still use that a lot of SMS, right? So pulling those out using those APIs. So it will be charged in some way. Whether- >> Man: Like Twitter's charging me for APIs, now I API calls, you >> Know what it is? I think it's Twilio. >> Man: Oh, okay. >> Right. >> Man: No, no, that's sure. >> There's no reason why telcos couldn't provide a Twilio like service itself. >> It's a horizontal play though right? >> Danielle: Correct because developers need to be charged by the API. >> But doesn't there need to be an industry standard to do that as- >> Well. I think that's what they just announced. >> Industry standard. >> Danielle: I think they just announced that. Yeah. Right now I haven't looked at that API set, right? >> There's like eight of them. >> There's eight of them. Twilio has, it's a start you got to start somewhere Dave. (crosstalk) >> And there's all, the TM forum is all the other standard >> Right? Eight is better than zero- >> Right? >> Haven't got plenty. >> I mean for an industry that didn't really understand APIs as a feature, as a product as a service, right? For Mats Granryd, the deputy general of GSMA to stand on the keynote stage and say we partnered and we're unveiling, right. Pay by the use APIs. I was for it. I was like, that is insane. >> I liked his keynote actually, because I thought he was going to talk about how many attendees and how much economic benefiting >> Danielle: We're super diverse. >> He said, I would usually talk about that and you know greening in the network by what you did talk about a little bit. But, but that's, that surprised me. >> Yeah. >> But I've seen in the enterprise this is not my space as, you know, you guys don't live this but I've seen Oracle try to get developers. IBM had to pay $35 billion trying to get for Red Hat to get developers, right? EMC used to have a thing called EMC code, failed. >> I mean they got to do something, right? So 4G they didn't really make the business case the ROI on the investment in the network. Here we are with 5G, same discussion is having where's the use case? How are we going to monetize and make the ROI on this massive investment? And now they're starting to talk about 6G. Same fricking problem is going to happen again. And so I think they need to start experimenting with new ideas. I don't know if it's going to work. I don't know if this new a API network gateway theme that Mats talked about yesterday will work. But they need to start unbundling that unlimited plan. They need to start charging people who are using the network more, more money. Those who are using it less, less. They need to figure this out. This is a crisis for them. >> Yeah our own CEO, I mean she basically said, Hey, I'm for net neutrality, but I want to be able to charge the people that are using it more and more >> To make a return on, on a capital. >> I mean it costs billions of dollars to build these networks, right? And they're valuable. We use them and we talked about this in Cloud City 21, right? The ability to start building better metaverses. And I know that's a buzzword and everyone hates it, but it's true. Like we're working from home. We need- there's got to be a better experience in Zoom in 2D, right? And you need a great network for that metaverse to be awesome. >> You do. But Danielle, you don't need cellular for doing that, do you? So the fixed network is as important. >> Sure. >> And we're at mobile worlds. But actually what we beginning to hear and Crystal Bren did say this exactly, it's about the comp the access is sort of irrelevant. Fixed is better because it's more the cost the return on investment is better from fiber. Mobile we're going to change every so many years because we're a new generation. But we need to get the mechanism in place to deliver that. I actually don't agree that we should everyone should pay differently for what they use. It's a universal service. We need it as individuals. We need to make it sustainable for every user. Let's just not go for the biggest user. It's not, it's not the way to build it. It won't work if you do that you'll crash the system if you do that. And, and the other thing which I disagree on it's not about standing on the shoulders and benefiting from what- It's about cooperating across all levels. The hyperscalers want to work with the telcos as much as the telcos want to work with the hyperscalers. There's a lot of synergy there. There's a lot of ways they can work together. It's not one or the other. >> But I think you're saying let the cloud guys do the heavy lifting and I'm - >> Yeah. >> Not at all. >> And so you don't think so because I feel like the telcos are really good at pipes. They've always been good at pipes. They're engineers. >> Danielle: Yeah. >> Are they hanging on to the to the connectivity or should they let that go and well and go toward the developer. >> I mean AWS had two announcements on the 21st a week before MWC. And one was that telco network builder. This is literally being able to deploy a network capability at AWS with keystrokes. >> As a managed service. >> Danielle: Correct. >> Yeah. >> And so I don't know how the telco world I felt the shock waves, right? I was like, whoa, that seems really big. Because they're taking something that previously was like bread and butter. This is what differentiates each telco and now they've standardized it and made it super easy so anyone can do it. Now do I think the five nines of super crazy hardcore network criteria will be built on AWS this way? Probably not, but no >> It's not, it's not end twin. So you can't, no. >> Right. But private networks could be built with this pretty easily, right? And so telcos that don't have as much funding, right. Smaller, more experiments. I think it's going to change the way we think about building networks in telcos >> And those smaller telcos I think are going to be more developer friendly. >> Danielle: Yeah. >> They're going to have business models that invite those developers in. And that's, it's the disruption's going to come from the ISVs and the workloads that are on top of that. >> Well certainly what Dish is trying to do, right? Dish is trying to build a- they launched it reinvent a developer experience. >> Dave: Yeah. >> Right. Built around their network and you know, again I don't know, they were not part of this group that designed these eight APIs but I'm sure they're looking with great intent on what does this mean for them. They'll probably adopt them because they want people to consume the network as APIs. That's their whole thing that Mark Roanne is trying to do. >> Okay, and then they're doing open ran. But is it- they're not really cons- They're not as concerned as Rakuten with the reliability and is that the right play? >> In this discussion? Open RAN is not an issue. It really is irrelevant. It's relevant for the longer term future of the industry by dis aggregating and being able to share, especially ran sharing, for example, in the short term in rural environments. But we'll see some of that happening and it will change, but it will also influence the way the other, the existing ran providers build their services and offer their value. Look you got to remember in the relationship between the equipment providers and the telcos are very dramatically. Whether it's Ericson, NOKIA, Samsung, Huawei, whoever. So those relations really, and the managed services element to that depends on what skills people have in-house within the telco and what service they're trying to deliver. So there's never one size fits all in this industry. >> You're very balanced in your analysis and I appreciate that. >> I try to be. >> But I am not. (chuckles) >> So when Dr went off, this is my question. When Dr went off a couple years ago on the cloud's going to take over the world, you were skeptical. You gave a approach. Have you? >> I still am. >> Have you moderated your thoughts on that or- >> I believe the telecom industry is is a very strong industry. It's my industry of course I love it. But the relationship it is developing much different relationships with the ecosystem players around it. You mentioned developers, you mentioned the cloud players the equipment guys are changing there's so many moving parts to build the telco of the future that every country needs a very strong telco environment to be able to support the site as a whole. People individuals so- >> Well I think two years ago we were talking about should they or shouldn't they, and now it's an inevitability. >> I don't think we were Danielle. >> All using the hyperscalers. >> We were always going to need to transform the telcos from the conservative environments in which they developed. And they've had control of everything in order to reduce if they get no extra revenue at all, reducing the cost they've got to go on a cloud migration path to do that. >> Amenable. >> Has it been harder than you thought? >> It's been easier than I thought. >> You think it's gone faster than >> It's gone way faster than I thought. I mean pushing on this flywheel I thought for sure it would take five to 10 years it is moving. I mean the maths comp thing the AWS announcements last week they're putting in hyperscalers in Saudi Arabia which is probably one of the most sort of data private places in the world. It's happening really fast. >> What Azure's doing? >> I feel like I can't even go to sleep. Because I got to keep up with it. It's crazy. >> Guys. >> This is awesome. >> So awesome having you back on. >> Yeah. >> Chris, thanks for co-hosting. Appreciate you stay here. >> Yep. >> Danielle, amazing. We'll see you. >> See you soon. >> A lot of action here. We're going to come out >> Great. >> Check out your venue. >> Yeah the Togi buses that are outside. >> The big buses. You got a great setup there. We're going to see you on Wednesday. Thanks again. >> Awesome. Thanks. >> All right. Keep it right there. We'll be back to wrap up day two from MWC 23 on theCUBE. (upbeat music)

(upbeat music) >> Welcome back to theCUBE. We're covering Red Hat Summit 2022. We're going to wrap up now, Dave Vellante, Paul Gillin. We want to introduce you to Stephanie Chan, who's our new correspondent. Stephanie, one of your first events, your very first CUBE event. So welcome. >> Thank you. >> Up from NYC. Smaller event, but intimate. You got a chance to meet some folks last night at some of the after parties. What are your overall impressions? What'd you learn this week? >> So this has been my first in-person event in over two years. And even though, like you said, is on the smaller scale, roughly around 1000 attendees, versus it's usual eight to 10,000 attendees. There's so much energy, and excitement, and openness in these events and sessions. Even before and after the sessions people have been mingling and socializing and hanging out. So, I think a lot of people appreciate these in-person events and are really excited to be here. >> Cool. So, you also sat in some of the keynotes, right? Pretty technical, right? Which is kind of new to sort of your genre, right? I mean, I know you got a financial background but, so what'd you think of the keynotes? What'd you think of the format, the theater in the round? Any impressions of that? >> So, I think there's three things that are really consistent in these Red Hat Summit keynotes. There's always a history lesson. There's always, you know, emphasis in the culture of openness. And, there's also inspirational stories about how people utilize open source. And I found a lot of those examples really compelling and interesting. For instance, people use open source in (indistinct), and even in space. So I really enjoyed, you know, learning about all these different people and stories. What about you guys? What do you think were the big takeaways and the best stories that came out of the keynotes? >> Paul, want to start? >> Clearly the Red Hat Enterprise Linux 9 is a major rollout. They do that only about every three years. So that's a big deal to this audience. I think what they did in the area of security, with rolling out sigstore, which is a major new, I think an important new project that was sort of incubated at Red Hat. And they're trying to put in to create an open source ecosystem around that now. And the alliances. I'm usually not that much on partnerships, but the Accenture and the Microsoft partnerships do seem to be significant to the company. And, finally, the GM partnership which I think was maybe kind of the bombshell that they sort of rushed in at the last minute. But I think has the biggest potential impact on Red Hat and its partner ecosystem that is really going to anchor their edge architecture going forward. So I didn't see it so much on the product front, but the sense of Red Hat spreading its wings, and partnering with more companies, and seeing its itself as really the center of an ecosystem indicates that they are, you know, they're in a very solid position in their business. >> Yeah, and also like the pandemic has really forced us into this new normal, right? So customer demand is changing. There has been the shift to remote. There's always going to be a new normal according to Paul, and open source carries us through that. So how do you guys think Red Hat has helped its portfolio through this new normal and the shift? >> I mean, when you think of Red Hat, you think of Linux. I mean, that's where it all started. You think OpenShift which is the application development platforms. Linux is the OS. OpenShift is the application development platform for Kubernetes. And then of course, Ansible is the automation framework. And I agree with you, ecosystem is really the other piece of this. So, I mean, I think you take those three pieces and extend that into the open source community. There's a lot of innovation that's going around each of those, but ecosystems are the key. We heard from Stefanie Chiras, that fundamental, I mean, you can't do this without those gap fillers and those partnerships. And then another thing that's notable here is, you know, this was, I mean, IBM was just another brand, right? I mean, if anything it was probably a sub-brand, I mean, you didn't hear much about IBM. You certainly had no IBM presence, even though they're right across the street running Think. No Arvind present, no keynote from Arvind, no, you know, Big Blue washing. And so, I think that's a testament to Arvind himself. We heard that from Paul Cormier, he said, hey, this guy's been great, he's left us alone. And he's allowed us to continue innovating. It's good news. IBM has not polluted Red Hat. >> Yes, I think that the Red Hat was, I said at the opening, I think Red Hat is kind of the tail wagging the dog right now. And their position seems very solid in the market. Clearly the market has come to them in terms of their evangelism of open source. They've remained true to their business model. And I think that gives them credibility that, you know, a lot of other open source companies have lacked. They have stuck with the plan for over 20 years now and have really not changed it, and it's paying off. I think they're emerging as a company that you can trust to do business with. >> Now I want to throw in something else here. I thought the conversation with IDC analyst, Jim Mercer, was interesting when he said that they surveyed customers and they wanted to get the security from their platform vendor, versus having to buy these bespoke tools. And it makes a lot of sense to me. I don't think that's going to happen, right? Because you're going to have an identity specialist. You're going to have an endpoint specialist. You're going to have a threat detection specialist. And they're going to be best of breed, you know, Red Hat's never going to be all of those things. What they can do is partner with those companies through APIs, through open source integrations, they can add them in as part of the ecosystem and maybe be the steward of that. Maybe that's the answer. They're never going to be the best at all those different security disciplines. There's no way in the world, Red Hat, that's going to happen. But they could be the integration point. And that would be, that would be a simplifying layer to the equation. >> And I think it's smart. You know, they're not pretending to be an identity in access management or an anti-malware company, or even a zero trust company. They are sticking to their knitting, which is operating system and developers. Evangelizing DevSecOps, which is a good thing. And, that's what they're going to do. You know, you have to admire this company. It has never gotten outside of its swim lane. I think it's understood well really what it wants to be good at. And, you know, in the software business knowing what not to do is more important than knowing what to do. Is companies that fail are usually the ones that get overextended, this company has never overextended itself. >> What else do you want to know? >> And a term that kept popping up was multicloud, or otherwise known as metacloud. We know what the cloud is, but- >> Oh, supercloud, metacloud. >> Supercloud, yeah, here we go. We know what the cloud is but, what does metacloud mean to you guys? And why has it been so popular in these conversations? >> I'm going to boot this to Dave, because he's the expert on this. >> Well, expert or not, but I mean, again, we've coined this term supercloud. And the idea behind the supercloud or what Ashesh called metacloud, I like his name, cause it allows Web 3.0 to come into the equation. But the idea is that instead of building on each individual cloud and have compatibility with that cloud, you build a layer across clouds. So you do the hard work as a platform supplier to hide the underlying primitives and APIs from the end customer, or the end developer, they can then add value on top of that. And that abstraction layer spans on-prem, clouds, across clouds, ultimately out to the edge. And it's new, a new value layer that builds on top of the hyperscale infrastructure, or existing data center infrastructure, or emerging edge infrastructure. And the reason why that is important is because it's so damn complicated, number one. Number two, every company's becoming a software company, a technology company. They're bringing their services through digital transformation to their customers. And you've got to have a cloud to do that. You're not going to build your own data center. That's like Charles Wang says, not Charles Wang. (Paul laughing) Charles Phillips. We were just talking about CA. Charles Phillips. Friends don't let friends build data centers. So that supercloud concept, or what Ashesh calls metacloud, is this new layer that's going to be powered by ecosystems and platform companies. And I think it's real. I think it's- >> And OpenShift, OpenShift is a great, you know, key card for them or leverage for them because it is perhaps the best known Kubernetes platform. And you can see here they're really doubling down on adding features to OpenShift, security features, scalability. And they see it as potentially this metacloud, this supercloud abstraction layer. >> And what we said is, in order to have a supercloud you got to have a superpaz layer and OpenShift is that superpaz layer. >> So you had conversations with a lot of people within the past two days. Some people include companies, from Verizon, Intel, Accenture. Which conversation stood out to you the most? >> Which, I'm sorry. >> Which conversation stood out to you the most? (Paul sighs) >> The conversation with Stu Miniman was pretty interesting because we talked about culture. And really, he has a lot of credibility in that area because he's not a Red Hat. You know, he hasn't been a Red Hat forever, he's fairly new to the company. And got a sense from him that the culture there really is what they say it is. It's a culture of openness and that's, you know, that's as important as technology for a company's success. >> I mean, this was really good content. I mean, there were a lot, I mean Stefanie's awesome. Stefanie Chiras, we're talking about the ecosystem. Chris Wright, you know, digging into some of the CTO stuff. Ashesh, who coined metacloud, I love that. The whole in vehicle operating system conversation was great. The security discussion that we just had. You know, the conversations with Accenture were super thoughtful. Of course, Paul Cormier was a highlight. I think that one's going to be a well viewed interview, for sure. And, you know, I think that the customer conversations are great. Red Hat did a really good job of carrying the keynote conversations, which were abbreviated this year, to theCUBE. >> Right. >> I give 'em a lot of kudos for that. And because, theCUBE, it allows us to double click, go deeper, peel the onion a little bit, you know, all the buzz words, and cliches. But it's true. You get to clarify some of the things you heard, which were, you know, the keynotes were, were scripted, but tight. And so we had some good follow up questions. I thought it was super useful. I know I'm leaving somebody out, but- >> We're also able to interview representatives from Intel and Nvidia, which at a software conference you don't typically do. I mean, there's the assimilation, the combination of hardware and software. It's very clear that, and this came out in the keynote, that Red Hat sees hardware as matter. It matters. It's important again. And it's going to be a source of innovation in the future. That came through clearly. >> Yeah. The hardware matters theme, you know, the old days you would have an operating system and the hardware were intrinsically linked. MVS in the mainframe, VAX, VMS in the digital mini computers. DG had its own operating system. Wang had his own operating system. Prime with Prime OS. You remember these days? >> Oh my God. >> Right? (Paul laughs) And then of course Microsoft. >> And then x86, everything got abstracted. >> Right. >> Everything became x86 and now it's all atomizing again. >> Although WinTel, right? I mean, MS-DOS and Windows were intrinsically linked for many, many years with Intel x86. And it wasn't until, you know, well, and then, you know, Sun Solaris, but it wasn't until Linux kind of blew that apart. And the internet is built on the lamp stack. And of course, Linux is the fundamental foundation for Red Hat. So my point is, that the operating system and the hardware have always been very closely tied together. Whether it's security, or IO, or registries and memory management, everything controlled by the OS are very close to the hardware. And so that's why I think you've got an affinity in Red Hat to hardware. >> But Linux is breaking that bond, don't you think? >> Yes, but it still has to understand the underlying hardware. >> Right. >> You heard today, how taking advantage of Nvidia, and the AI capabilities. You're seeing that with ARM, you're seeing that with Intel. How you can optimize the operating system to take advantage of new generations of CPU, and NPU, and CPU, and PU, XPU, you know, across the board. >> Yep. >> Well, I really enjoyed this conference and it really stressed how important open source is to a lot of different industries. >> Great. Well, thanks for coming on. Paul, thank you. Great co-hosting with you. And thank you. >> Always, Dave. >> For watching theCUBE. We'll be on the road, next week we're at KubeCon in Valencia, Spain. We're at VeeamON. We got a ton of stuff going on. Check out thecube.net. Check out siliconangle.com for all the news. Wikibon.com. We publish there weekly, our breaking analysis series. Thanks for watching everybody. Dave Vellante, for Paul Gillin, and Stephanie Chan. Thanks to the crew. Shout out, Andrew, Alex, Sonya. Amazing job, Sonya. Steven, thanks you guys for coming out here. Mark, good job corresponding. Go to SiliconANGLE, Mark's written some great stuff. And thank you for watching. We'll see you next time. (calm music)

(upbeat music) >> We're back. You're watching theCUBE's coverage of Red Hat Summit 2022 here in the Seaport in Boston. I'm Dave Vellante with my co-host, Paul Gillin. Justin Boitano is here. He's the Vice President of Enterprise and Edge Computing at NVIDIA. Maybe you've heard of him. And Tushar Katarki who's the Director of Product Management at Red Hat. Gentlemen, welcome to theCUBE, good to see you. >> Thank you. >> Great to be here, thanks >> Justin, you are a keynote this morning. You got interviewed and shared your thoughts on AI. You encourage people to got to think bigger on AI. I know it's kind of self-serving but why? Why should we think bigger? >> When you think of AI, I mean, it's a monumental change. It's going to affect every industry. And so when we think of AI, you step back, you're challenging companies to build intelligence and AI factories, and factories that can produce intelligence. And so it, you know, forces you to rethink how you build data centers, how you build applications. It's a very data centric process where you're bringing in, you know, an exponential amount of data. You have to label that data. You got to train a model. You got to test the model to make sure that it's accurate and delivers business value. Then you push it into production, it's going to generate more data, and you kind of work through that cycle over and over and over. So, you know, just as Red Hat talks about, you know, CI/CD of applications, we're talking about CI/CD of the AI model itself, right? So it becomes a continuous improvement of AI models in production which is a big, big business transformation. >> Yeah, Chris Wright was talking about basically take your typical application development, you know, pipeline, and life cycle, and apply that type of thinking to AI. I was saying those two worlds have to come together. Actually, you know, the application stack and the data stack including AI need to come together. What's the role of Red Hat? What's your sort of posture on AI? Where do you fit with OpenShift? >> Yeah, so we're really excited about AI. I mean, a lot of our customers obviously are looking to take that data and make meaning out of it using AI is definitely a big important tool. And OpenShift, and our approach to Open Hybrid Cloud really forms a successful platform to base all your AI journey on with the partners such as NVIDIA whom we are working very closely with. And so the idea really is as Justin was saying, you know, the end to end, when you think about life of a model, you've got data, you mine that data, you create models, you deploy it into production. That whole thing, what we call CI/CD, as he was saying DevOps, DevSecOps, and the hybrid cloud that Red Hat has been talking about, although with OpenShift as the center forms a good basis for that. >> So somebody said the other day, I'm going to ask you, is INVIDIA a hardware company or a software company? >> We are a company that people know for our hardware but, you know, predominantly now we're a software company. And that's what we were on stage talking about. I mean, ultimately, a lot of these customers know that they've got to embark on this journey to apply AI, to transform their business with it. It's such a big competitive advantage going into, you know, the next decade. And so the faster they get ahead of it, the more they're going to win, right? But some of them, they're just not really sure how to get going. And so a lot of this is we want to lower the barrier to entry. We built this program, we call it Launchpad to basically make it so they get instant access to the servers, the AI servers, with OpenShift, with the MLOps tooling, with example applications. And then we walk them through examples like how do you build a chatbot? How do you build a vision system for quality control? How do you build a price recommendation model? And they can do hands on labs and walk out of, you know, Launchpad with all the software they need, I'll say the blueprint for building their application. They've got a way to have the software and containers supported in production, and they know the blueprint for the infrastructure and operating that a scale with OpenShift. So more and more, you know, to come back to your question is we're focused on the software layers and making that easy to help, you know, either enterprises build their apps or work with our ecosystem and developers to buy, you know, solutions off the shelf. >> On the harbor side though, I mean, clearly NVIDIA has prospered on the backs of GPUs, as the engines of AI development. Is that how it's going to be for the foreseeable future? Will GPUs continue to be core to building and training AI models or do you see something more specific to AI workloads? >> Yeah, I mean, it's a good question. So I think for the next decade, well, plus, I mean not forever, we're going to always monetize hardware. It's a big, you know, market opportunity. I mean, Jensen talks about a $100 billion, you know, market opportunity for NVIDIA just on hardware. It's probably another a $100 billion opportunity on the software. So the reality is we're getting going on the software side, so it's still kind of early days, but that's, you know, a big area of growth for us in the future and we're making big investments in that area. On the hardware side, and in the data center, you know, the reality is since Moore's law has ended, acceleration is really the thing that's going to advance all data centers. So I think in the future, every server will have GPUs, every server will have DPUs, and we can talk a bit about what DPUs are. And so there's really kind of three primary processors that have to be there to form the foundation of the enterprise data center in the future. >> Did you bring up an interesting point about DPUs and MPUs, and sort of the variations of GPUs that are coming about? Do you see those different PU types continuing to proliferate? >> Oh, absolutely. I mean, we've done a bunch of work with Red Hat, and we've got a, I'll say a beta of OpenShift 4.10 that now supports DPUs as the, I'll call it the control plane like software defined networking offload in the data center. So it takes all the software defined networking off of CPUs. When everybody talks about, I'll call it software defined, you know, networking and core data centers, you can think of that as just a CPU tax up to this point. So what's nice is it's all moving over to DPU to, you know, offload and isolate it from the x86 cores. It increases security of data center. It improves the throughput of your data center. And so, yeah, DPUs, we see everybody copying that model. And, you know to give credit where credit is due, I think, you know, companies like AWS, you know, they bought Annapurna, they turned it into Nitro which is the foundation of their data centers. And everybody wants the, I'll call it democratized version of that to run their data centers. And so every financial institution and bank around the world sees the value of this technology, but running in their data centers. >> Hey, everybody needs a Nitro. I've written about it. It's Annapurna acquisition, 350 million. I mean, peanuts in the grand scheme of things. It's interesting, you said Moore's law is dead. You know, we have that conversation all the time. Pat Gelsinger promised that Moore's law is alive and well. But the interesting thing is when you look at the numbers, that's, you know, Moore's law, we all know it, doubling of the transistor densities every 18 to 24 months. Let's say that, that promise that he made is true. What I think the industry maybe doesn't appreciate, I'm sure you do, being in NVIDIA, when you combine what you were just saying, the CPU, the GPU, Paul, the MPU, accelerators, all the XPUs, you're talking about, I mean, look at Apple with the M1, I mean 6X in 15 months versus doubling every 18 to 24. The A15 is probably averaging over the last five years, a 110% performance improvement each year versus the historical Moore's law which is 40%. It's probably down to the low 30s now. So it's a completely different world that we're entering now. And the new applications are going to be developed on these capabilities. It's just not your general purpose market anymore. From an application development standpoint, what does that mean to the world? >> Yeah, I mean, yeah, it is a great point. I mean, from an application, I mean first of all, I mean, just talk about AI. I mean, they are all very compute intensive. They're data intensive. And I mean to move data focus so much in to compute and crunch those numbers. I mean, I'd say you need all the PUs that you mentioned in the world. And also there are other concerns that will augment that, right? Like we want to, you know, security is so important so we want to secure everything. Cryptography is going to take off to new levels, you know, that we are talking about, for example, in the case of DPUs, we are talking about, you know, can that be used to offload your encryption and firewalling, and so on and so forth. So I think there are a lot of opportunities even from an application point of view to take of this capacity. So I'd say we've never run out of the need for PUs if you will. >> So is OpenShift the layer that's going to simplify all that for the developer. >> That's right. You know, so one of the things that we worked with NVIDIA, and in fact was we developed this concept of an operator for GPUs, but you can use that pattern for any of the PUs. And so the idea really is that, how do you, yeah-- (all giggle) >> That's a new term. >> Yeah, it's a new term. (all giggle) >> XPUs. >> XPUs, yeah. And so that pattern becomes very easy for GPUs or any other such accelerators to be easily added as a capacity. And for the Kubernetes scaler to understand that there is that capacity so that an application which says that I want to run on a GPU then it becomes very easy for it to run on that GPU. And so that's the abstraction to your point about how we are making that happen. >> And to add to this. So the operator model, it's this, you know, open source model that does the orchestration. So Kubernetes will say, oh, there's a GPU in that node, let me run the operator, and it installs our entire run time. And our run time now, you know, it's got a MIG configuration utility. It's got the driver. It's got, you know, telemetry and metering of the actual GPU and the workload, you know, along with a bunch of other components, right? They get installed in that Kubernetes cluster. So instead of somebody trying to chase down all the little pieces and parts, it just happens automatically in seconds. We've extended the operator model to DPUs and networking cards as well, and we have all of those in the operator hub. So for somebody that's running OpenShift in their data centers, it's really simple to, you know, turn on Node Feature Discovery, you point to the operators. And when you see new accelerated nodes, the entire run time is automatically installed for you. So it really makes, you know, GPUs and our networking, our advanced networking capabilities really first class citizens in the data center. >> So you can kind of connect the dots and see how NVIDIA and the Red Hat partnership are sort of aiming at the enterprise. I mean, NVIDIA, obviously, they got the AI piece. I always thought maybe 25% of the compute cycles in the data center were wasted doing storage offloads or networking offload, security. I think Jensen says it's 30%, probably a better number than I have. But so now you're seeing a lot of new innovation in new hardware devices that are attacking that with alternative processors. And then my question is, what about the edge? Is that a blue field out at the edge? What does that look like to NVIDIA and where does OpenShift play? >> Yeah, so when we talk about the edge, we always going to start talking about like which edge are we talking about 'cause it's everything outside the core data center. I mean, some of the trends that we see with regard to the edges is, you know, when you get to the far edge, it's single nodes. You don't have the guards, gates, and guns protection of the data center. So you start having to worry about physical security of the hardware. So you can imagine there's really stringent requirements on protecting the intellectual property of the AI model itself. You spend millions of dollars to build it. If I push that out to an edge data center, how do I make sure that that's fully protected? And that's the area that we just announced a new processor that we call Hopper H100. It supports confidential computing so that you can basically ensure that model is always encrypted in system memory across the bus, of the PCI bus to the GPU, and it's run in a confidential way on the GPU. So you're protecting your data which is your model plus the data flowing through it, you know, in transit, wallet stored, and then in use. So that really adds to that edge security model. >> I wanted to ask you about the cloud, correct me if I'm wrong. But it seems to me that that AI workloads have been slower than most to make their way to the cloud. There are a lot of concerns about data transfer capacity and even cost. Do you see that? First of all, do you agree with that? And secondly, is that going to change in the short-term? >> Yeah, so I think there's different classes of problems. So we'll take, there's some companies where their data's generated in the cloud and we see a ton of, I'll say, adoption of AI by cloud service providers, right? Recommendation engines, translation engines, conversational AI services, that all the clouds are building. That's all, you know, our processors. There's also problems that enterprises have where now I'm trying to take some of these automation capabilities but I'm trying to create an intelligent factory where I want to, you know, merge kind of AI with the physical world. And that really has to run at the edge 'cause there's too much data being generated by cameras to bring that all the way back into the cloud. So, you know, I think we're seeing mass adoption in the cloud today. I think at the edge a lot of businesses are trying to understand how do I deploy that reliably and securely and scale it. So I do think, you know, there's different problems that are going to run in different places, and ultimately we want to help anybody apply AI where the business is generating the data. >> So obviously very memory intensive applications as well. We've seen you, NVIDIA, architecturally kind of move away from the traditional, you know, x86 approach, take better advantage of memories where obviously you have relationships with Arm. So you've got a very diverse set of capabilities. And then all these other components that come into use, to just be a kind of x86 centric world. And now it's all these other supporting components to support these new applications and it's... How should we think about the future? >> Yeah, I mean, it's very exciting for sure, right? Like, you know, the future, the data is out there at the edge, the data can be in the data center. And so we are trying to weave a hybrid cloud footprint that spans that. I mean, you heard Paul come here, talk about it. But, you know, we've talked about it for some time now. And so the paradigm really that is, that be it an application, and when I say application, it could be even an AI model as a service. It can think about that as an application. How does an application span that entire paradigm from the core to the edge and beyond is where the future is. And, of course, there's a lot of technical challenges, you know, for us to get there. And I think partnerships like this are going to help us and our customers to get there. So the world is very exciting. You know, I'm very bullish on how this will play out, right? >> Justin, we'll give you the last word, closing thoughts. >> Well, you know, I think a lot of this is like I said, it's how do we reduce the complexity for enterprises to get started which is why Launchpad is so fundamental. It gives, you know, access to the entire stack instantly with like hands on curated labs for both IT and data scientists. So they can, again, walk out with the blueprints they need to set this up and, you know, start on a successful AI journey. >> Just a position, is Launchpad more of a Sandbox, more of a school, or more of an actual development environment. >> Yeah, think of it as it's, again, it's really for trial, like hands on labs to help people learn all the foundational skills they need to like build an AI practice and get it into production. And again, it's like, you don't need to go champion to your executive team that you need access to expensive infrastructure and, you know, and bring in Red Hat to set up OpenShift. Everything's there for you so you can instantly get started. Do kind of a pilot project and then use that to explain to your executive team everything that you need to then go do to get this into production and drive business value for the company. >> All right, great stuff, guys. Thanks so much for coming to theCUBE. >> Yeah, thanks. >> Thank you for having us. >> All right, thank you for watching. Keep it right there, Dave Vellante and Paul Gillin. We'll be back right after this short break at the Red Hat Summit 2022. (upbeat music)

(bright upbeat music) >> We're back at the Red Hat Summit at the Seaport in Boston, theCUBE's coverage. This is day two. Dave Vellante and Paul Gillin. Chris Wright is here, the chief technology officer at Red Hat. Chris, welcome back to theCUBE. Good to see you. >> Yeah, likewise. Thanks for having me. >> You're very welcome. So, you were saying today in your keynote. We got a lot of ground to cover here, Chris. You were saying that, you know, software, Andreessen's software is eating the world. Software ate the world, is what you said. And now we have to think about AI. AI is eating the world. What does that mean? What's the implication for customers and developers? >> Well, a lot of implications. I mean, to start with, just acknowledging that software isn't this future dream. It is the reality of how businesses run today. It's an important part of understanding what you need to invest in to make yourself successful, essentially, as a software company, where all companies are building technology to differentiate themselves. Take that, all that discipline, everything we've learned in that context, bring in AI. So, we have a whole new set of skills to learn, tools to create and discipline processes to build around delivering data-driven value into the company, just the way we've built software value into companies. >> I'm going to cut right to the chase because I would say data is eating software. Data and AI, to me, are like, you know, kissing cousins. So here's what I want to ask you as a technologist. So we have the application development stack, if you will. And it's separate from the data and analytics stack. All we talk about is injecting AI into applications, making them data-driven. You just used that term. But they're totally two totally separate stacks, organizationally and technically. Are those worlds coming together? Do they have to come together in order for the AI vision to be real? >> Absolutely, so, totally agree with you on the data piece. It's inextricably linked to AI and analytics and all of the, kind of, machine learning that goes on in creating intelligence for applications. The application connection to a machine learning model is fundamental. So, you got to think about not just the software developer or the data scientist, but also there's a line of business in there that's saying, "Here's the business outcomes I'm looking for." It's that trifecta that has to come together to make advancements and really make change in the business. So, you know, some of the folks we had on stage today were talking about exactly that. Which is, how do you bring together those three different roles? And there's technology that can help bridge gaps. So, we look at what we call intelligent applications. Embed intelligence into the application. That means you surface a machine learning model with APIs to make it accessible into applications, so that developers can query a machine learning model. You need to do that with some discipline and rigor around, you know, what does it mean to develop this thing and life cycle it and integrate it into this bigger picture. >> So the technology is capable of coming together. You know, Amanda Purnell is coming on next. >> Oh, great. >> 'Cause she was talking about, you know, getting, you know, insights in the hands of nurses and they're not coders. >> That's right. >> But they need data. But I feel like it's, well, I feel very strongly that it's an organizational challenge, more so. I think you're confirming. It's not really a technical challenge. I can insert a column into the application development stack and bring TensorFlow in or AI or data, whatever it is. It's not a technical issue. Is that fair? >> Well, there are some technical challenges. So, for example, data scientists. Kind of a scarce kind of skillset within any business. So, how do you scale data scientists into the developer population? Which will be a large population within an organization. So, there's tools that we can use to bring those worlds together. So, you know, it's not just TensorFlow but it's the entire workflow and platform of how you share the data, the data training models and then just deploying models into a runtime production environment. That looks similar to software development processes but it's slightly different. So, that's where a common platform can help bridge the gaps between that developer world and the data science world. >> Where is Red Hat's position in this evolving AI stack? I mean, you're not into developing tool sets like TensorFlow, right? >> Yeah, that's right. If you think about a lot of what we do, it's aggregate content together, bring a distribution of tools, giving flexibility to the user. Whether that's a developer, a system administrator, or a data scientist. So our role here is, one, make sure we work with our hardware partners to create accelerated environments for AI. So, that's sort of an enablement thing. The other is bring together those disparate tools into a workflow and give a platform that enables data scientists to choose which, is it PyTorch, is it TensorFlow? What's the best tool for you? And assemble that tool into your workflow and then proceed training, doing inference, and, you know, tuning and lather, rinse, repeat. >> So, to make your platform then, as receptive as possible, right? You're not trying to pick winners in what languages to work with or what frameworks? >> Yeah, that's right. I mean, picking winners is difficult. The world changes so rapidly. So we make big bets on key areas and certainly TensorFlow would be a great example. A lot of community attraction there. But our goal isn't to say that's the one tool that everybody should use. It's just one of the many tools in your toolbox. >> There are risks of not pursuing this, from an organization's perspective. A customer, they kind of get complacent and, you know, they could get disrupted, but there's also an industry risk. If the industry can't deliver this capability, what are the implications if the industry doesn't step up? I believe the industry will, just 'cause it always does. But what about customer complacency? We certainly saw that a lot with digital transformation and COVID sort of forced us to march to digital. What should we be thinking about of the implications of not leaning in? >> Well, I think that the disruption piece is key because there's always that spectrum of businesses. Some are more leaning in, invested in the future. Some are more laggards and kind of wait and see. Those leaning in tend to be separating themselves, wheat from the chaff. So, that's an important way to look at it. Also, if you think about it, many data science experiments fail within businesses. I think part of that is not having the rigor and discipline around connecting, not just the tools and data scientists together, but also looking at what business outcomes are you trying to drive? If you don't bring those things together then it sort of can be too academic and the business doesn't see the value. And so there's also the question of transparency. How do you understand why is a model predicting you should take a certain action or do a certain thing? As an industry, I think we need to focus on bringing tools together, bringing data together, and building better transparency into how models work. >> There's also a lot of activity around governance right now, AI governance. Particularly removing bias from ML models. Is that something that you are guiding your customers on? Or, how important do you feel this is at this point of AI's development? >> It's really important. I mean, the challenge is finding it and understanding, you know, we bring data that maybe already carrying a bias into a training process and building a model around that. How do you understand what the bias is in that model? There's a lot of open questions there and academic research to try to understand how you can ferret out, you know, essentially biased data and make it less biased or unbiased. Our role is really just bringing the toolset together so that you have the ability to do that as a business. So, we're not necessarily building the next machine learning algorithm or models or ways of building transparency into models, as much as building the platform and bringing the tools together that can give you that for your own organization. >> So, it brings up the question of architectures. I've been sort of a casual or even active observer of data architectures over the last, whatever, 15 years. They've been really centralized. Our data teams are highly specialized. You mentioned data scientists, but there's data engineers and there's data analysts and very hyper specialized roles that don't really scale that well. So there seems to be a move, talk about edge. We're going to talk about edge. The ultimate edge, which is space, very cool. But data is distributed by its very nature. We have this tendency to try to force it into this, you know, monolithic system. And I know that's a pejorative, but for good reason. So I feel like there's this push in organizations to enable scale, to decentralize data architectures. Okay, great. And put data in the hands of those business owners that you talked about earlier. The domain experts that have business context. Two things, two problems that brings up, is you need infrastructure that's self-service, in that instance. And you need, to your point, automated and computational governance. Those are real challenges. What do you see in terms of the trends to decentralize data architectures? Is it even feasible that everybody wants a single version of the truth, centralized data team, right? And they seem to be at odds. >> Yeah, well I think we're coming from a history informed by centralization. That's what we understand. That's what we kind of gravitate towards, but the reality, as you put it, the world's just distributed. So, what we can do is look at federation. So, it's not necessarily centralization but create connections between data sources which requires some policy and governance. Like, who gets access to what? And also think about those domain experts maybe being the primary source of surfacing a model that you don't necessarily have to know how it was trained or what the internals are. You're using it more to query it as a, you know, the domain expert produces this model, you're in a different part of the organization just leveraging some work that somebody else has done. Which is how we build software, reusable components in software. So, you know, I think building that mindset into data and the whole process of creating value from data is going to be a really critical part of how we roll forward. >> So, there are two things in your keynote. One, that I was kind of in awe of. You wanted to be an astronaut when you were a kid. You know, I mean, I watched the moon landing and I was like, "I'm never going up into space." So, I'm in awe of that. >> Oh, I got the space helmet picture and all that. >> That's awesome, really, you know, hat's off to you. The other one really pissed me off, which was that you're a better skier 'cause you got some device in your boot. >> Oh, it's amazing. >> And the reason it angered me is 'cause I feel like it's the mathematicians taking over baseball, you know. Now, you're saying, you're a better skier because of that. But those are two great edge examples and there's a billion of them, right? So, talk about your edge strategy. Kind of, your passion there, how you see that all evolving. >> Well, first of all, we see the edge as a fundamental part of the future of computing. So in that centralization, decentralization pendulum swing, we're definitely on the path towards distributed computing and that is edge and that's because of data. And also because of the compute capabilities that we have in hardware. Hardware gets more capable, lower power, can bring certain types of accelerators into the mix. And you really create this world where what's happening in a virtual context and what's happening in a physical context can come together through this distributed computing system. Our view is, that's hybrid. That's what we've been working on for years. Just the difference was maybe, originally it was focused on data center, cloud, multi-cloud and now we're just extending that view out to the edge and you need the same kind of consistency for development, for operations, in the edge that you do in that hybrid world. So that's really where we're placing our focus and then it gets into all the different use cases. And you know, really, that's the fun part. >> I'd like to shift gears a little bit 'cause another remarkable statistic you cited during your keynote was, it was a Forrester study that said 99% of all applications now have open source in them. What are the implications of that for those who are building applications? In terms of license compliance and more importantly, I think, confidence in the code that they're borrowing from open source projects. >> Well, I think, first and foremost, it says open source has won. We see that that was audited code bases which means there's mission critical code bases. We see that it's pervasive, it's absolutely everywhere. And that means developers are pulling dependencies into their applications based on all of the genius that's happening in open source communities. Which I think we should celebrate. Right after we're finished celebrating we got to look at what are the implications, right? And that shows up as, are there security vulnerabilities that become ubiquitous because we're using similar dependencies? What is your process for vetting code that you bring into your organization and push into production? You know that process for the code you author, what about your dependencies? And I think that's an important part of understanding and certainly there are some license implications. What are you required to do when you use that code? You've been given that code on a license from the open source community, are you compliant with that license? Some of those are reasonably well understood. Some of those are, you know, newer to the enterprise. So I think we have to look at this holistically and really help enterprises build safe application code that goes into production and runs their business. >> We saw Intel up in the keynotes today. We heard from Nvidia, both companies are coming on. We know you've done a lot of work with ARM over the years. I think Graviton was one of the announcements this week. So, love to see that. I want to run something by you as a technologist. The premise is, you know, we used to live in this CPU centric world. We marched to the cadence of Moore's Law and now we're seeing the combinatorial factors of CPU, GPU, NPU, accelerators and other supporting components. With IO and controllers and NICs all adding up. It seems like we're shifting from a processor centric world to a connect centric world on the hardware side. That first of all, do you buy that premise? And does hardware matter anymore with all the cloud? >> Hardware totally matters. I mean the cloud tried to convince us that hardware doesn't matter and it actually failed. And the reason I say that is because if you go to a cloud, you'll find 100s of different instance types that are all reflections of different types of assemblies of hardware. Faster IO, better storage, certain sizes of memory. All of that is a reflection of, applications need certain types of environments for acceleration, for performance, to do their job. Now I do think there's an element of, we're decomposing compute into all of these different sort of accelerators and the only way to bring that back together is connectivity through the network. But there's also SOCs when you get to the edge where you can integrate the entire system onto a pretty small device. I think the important part here is, we're leveraging hardware to do interesting work on behalf of applications that makes hardware exciting. And as an operating system geek, I couldn't be more thrilled, because that's what we do. We enable hardware, we get down into the bits and bytes and poke registers and bring things to life. There's a lot happening in the hardware world and applications can't always follow it directly. They need that level of indirection through a software abstraction and that's really what we're bringing to life here. >> We've seen now hardware specific AI, you know, AI chips and AI SOCs emerge. How do you make decisions about what you're going to support or do you try to support all of them? >> Well, we definitely have a breadth view of support and we're also just driven by customer demand. Where our customers are interested we work closely with our partners. We understand what their roadmaps are. We plan together ahead of time and we know where they're making investments and we work with our customers. What are the best chips that support their business needs and we focus there first but it ends up being a pretty broad list of hardware that we support. >> I could pick your brain for an hour. We didn't even get into super cloud, Chris. But, thanks so much for coming on theCUBE. It's great to have you. >> Absolutely, thanks for having me. >> All right. Thank you for watching. Keep it right there. Paul Gillin, Dave Vellante, theCUBE's live coverage of Red Hat Summit 2022 from Boston. We'll be right back. (mellow music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante. >> If it weren't for Pat Gelsinger, Intel's future would be a disaster. Even with his clear vision, fantastic leadership, deep technical and business acumen, and amazing positivity, the company's future is in serious jeopardy. It's the same story we've been telling for years. Volume is king in the semiconductor industry, and Intel no longer is the volume leader. Despite Intel's efforts to change that dynamic With several recent moves, including making another go at its Foundry business, the company is years away from reversing its lagging position relative to today's leading foundries and design shops. Intel's best chance to survive as a leader in our view, will come from a combination of a massive market, continued supply constraints, government money, and luck, perhaps in the form of a deal with apple in the midterm. Hello, and welcome to this week's "Wikibon CUBE Insights, Powered by ETR." In this "Breaking Analysis," we'll update you on our latest assessment of Intel's competitive position and unpack nuggets from the company's February investor conference. Let's go back in history a bit and review what we said in the early 2010s. If you've followed this program, you know that our David Floyer sounded the alarm for Intel as far back as 2012, the year after PC volumes peaked. Yes, they've ticked up a bit in the past couple of years but they pale in comparison to the volumes that the ARM ecosystem is producing. The world has changed from people entering data into machines, and now it's machines that are driving all the data. Data volumes in Web 1.0 were largely driven by keystrokes and clicks. Web 3.0 is going to be driven by machines entering data into sensors, cameras. Other edge devices are going to drive enormous data volumes and processing power to boot. Every windmill, every factory device, every consumer device, every car, will require processing at the edge to run AI, facial recognition, inference, and data intensive workloads. And the volume of this space compared to PCs and even the iPhone itself is about to be dwarfed with an explosion of devices. Intel is not well positioned for this new world in our view. Intel has to catch up on the process, Intel has to catch up on architecture, Intel has to play catch up on security, Intel has to play catch up on volume. The ARM ecosystem has cumulatively shipped 200 billion chips to date, and is shipping 10x Intel's wafer volume. Intel has to have an architecture that accommodates much more diversity. And while it's working on that, it's years behind. All that said, Pat Gelsinger is doing everything he can and more to close the gap. Here's a partial list of the moves that Pat is making. A year ago, he announced IDM 2.0, a new integrated device manufacturing strategy that opened up its world to partners for manufacturing and other innovation. Intel has restructured, reorganized, and many executives have boomeranged back in, many previous Intel execs. They understand the business and have a deep passion to help the company regain its prominence. As part of the IDM 2.0 announcement, Intel created, recreated if you will, a Foundry division and recently acquired Tower Semiconductor an Israeli firm, that is going to help it in that mission. It's opening up partnerships with alternative processor manufacturers and designers. And the company has announced major investments in CAPEX to build out Foundry capacity. Intel is going to spin out Mobileye, a company it had acquired for 15 billion in 2017. Or does it try and get a $50 billion valuation? Mobileye is about $1.4 billion in revenue, and is likely going to be worth more around 25 to 30 billion, we'll see. But Intel is going to maybe get $10 billion in cash from that, that spin out that IPO and it can use that to fund more FABS and more equipment. Intel is leveraging its 19,000 software engineers to move up the stack and sell more subscriptions and high margin software. He got to sell what he got. And finally Pat is playing politics beautifully. Announcing for example, FAB investments in Ohio, which he dubbed Silicon Heartland. Brilliant! Again, there's no doubt that Pat is moving fast and doing the right things. Here's Pat at his investor event in a T-shirt that says, "torrid, bringing back the torrid pace and discipline that Intel is used to." And on the right is Pat at the State of the Union address, looking sharp in shirt and tie and suit. And he has said, "a bet on Intel is a hedge against geopolitical instability in the world." That's just so good. To that statement, he showed this chart at his investor meeting. Basically it shows that whereas semiconductor manufacturing capacity has gone from 80% of the world's volume to 20%, he wants to get it back to 50% by 2030, and reset supply chains in a market that has become important as oil. Again, just brilliant positioning and pushing all the right hot buttons. And here's a slide underscoring that commitment, showing manufacturing facilities around the world with new capacity coming online in the next few years in Ohio and the EU. Mentioning the CHIPS Act in his presentation in The US and Europe as part of a public private partnership, no doubt, he's going to need all the help he can get. Now, we couldn't resist the chart on the left here shows wafer starts and transistor capacity growth. For Intel, overtime speaks to its volume aspirations. But we couldn't help notice that the shape of the curve is somewhat misleading because it shows a two-year (mumbles) and then widens the aperture to three years to make the curve look steeper. Fun with numbers. Okay, maybe a little nitpick, but these are some of the telling nuggets we pulled from the investor day, and they're important. Another nitpick is in our view, wafers would be a better measure of volume than transistors. It's like a company saying we shipped 20% more exabytes or MIPS this year than last year. Of course you did, and your revenue shrank. Anyway, Pat went through a detailed analysis of the various Intel businesses and promised mid to high double digit growth by 2026, half of which will come from Intel's traditional PC they center in network edge businesses and the rest from advanced graphics HPC, Mobileye and Foundry. Okay, that sounds pretty good. But it has to be taken into context that the balance of the semiconductor industry, yeah, this would be a pretty competitive growth rate, in our view, especially for a 70 plus billion dollar company. So kudos to Pat for sticking his neck out on this one. But again, the promise is several years away, at least four years away. Now we want to focus on Foundry because that's the only way Intel is going to get back into the volume game and the volume necessary for the company to compete. Pat built this slide showing the baby blue for today's Foundry business just under a billion dollars and adding in another $1.5 billion for Tower Semiconductor, the Israeli firm that it just acquired. So a few billion dollars in the near term future for the Foundry business. And then by 2026, this really fuzzy blue bar. Now remember, TSM is the new volume leader, and is a $50 billion company growing. So there's definitely a market there that it can go after. And adding in ARM processors to the mix, and, you know, opening up and partnering with the ecosystems out there can only help volume if Intel can win that business, which you know, it should be able to, given the likelihood of long term supply constraints. But we remain skeptical. This is another chart Pat showed, which makes the case that Foundry and IDM 2.0 will allow expensive assets to have a longer useful life. Okay, that's cool. It will also solve the cumulative output problem highlighted in the bottom right. We've talked at length about Wright's Law. That is, for every cumulative doubling of units manufactured, cost will fall by a constant percentage. You know, let's say around 15% in semiconductor world, which is vitally important to accommodate next generation chips, which are always more expensive at the start of the cycle. So you need that 15% cost buffer to jump curves and make any money. So let's unpack this a bit. You know, does this chart at the bottom right address our Wright's Law concerns, i.e. that Intel can't take advantage of Wright's Law because it can't double cumulative output fast enough? Now note the decline in wafer starts and then the slight uptick, and then the flattening. It's hard to tell what years we're talking about here. Intel is not going to share the sausage making because it's probably not pretty, But you can see on the bottom left, the flattening of the cumulative output curve in IDM 1.0 otherwise known as the death spiral. Okay, back to the power of Wright's Law. Now, assume for a second that wafer density doesn't grow. It does, but just work with us for a second. Let's say you produce 50 million units per year, just making a number up. That gets you cumulative output to $100 million in, sorry, 100 million units in the second year to take you two years to get to that 100 million. So in other words, it takes two years to lower your manufacturing cost by, let's say, roughly 15%. Now, assuming you can get wafer volumes to be flat, which that chart showed, with good yields, you're at 150 now in year three, 200 in year four, 250 in year five, 300 in year six, now, that's four years before you can take advantage of Wright's Law. You keep going at that flat wafer start, and that simplifying assumption we made at the start and 50 million units a year, and well, you get to the point. You get the point, it's now eight years before you can get the Wright's Law to kick in, and you know, by then you're cooked. But now you can grow the density of transistors on a chip, right? Yes, of course. So let's come back to Moore's Law. The graphic on the left says that all the growth is in the new stuff. Totally agree with that. Huge term that Pat presented. Now he also said that until we exhaust the periodic table of elements, Moore's Law is alive and well, and Intel is the steward of Moore's Law. Okay, that's cool. The chart on the right shows Intel going from 100 billion transistors today to a trillion by 2030. Hold that thought. So Intel is assuming that we'll keep up with Moore's Law, meaning a doubling of transistors every let's say two years, and I believe it. So bring that back to Wright's Law, in the previous chart, it means with IDM 2.0, Intel can get back to enjoying the benefits of Wright's Law every two years, let's say, versus IDM 1.0 where they were failing to keep up. Okay, so Intel is saved, yeah? Well, let's bring into this discussion one of our favorite examples, Apple's M1 ARM-based chip. The M1 Ultra is a new architecture. And you can see the stats here, 114 billion transistors on a five nanometer process and all the other stats. The M1 Ultra has two chips. They're bonded together. And Apple put an interposer between the two chips. An interposer is a pathway that allows electrical signals to pass through it onto another chip. It's a super fast connection. You can see 2.5 terabytes per second. But the brilliance is the two chips act as a single chip. So you don't have to change the software at all. The way Intel's architecture works is it takes two different chips on a substrate, and then each has its own memory. The memory is not shared. Apple shares the memory for the CPU, the NPU, the GPU. All of it is shared, meaning it needs no change in software unlike Intel. Now Intel is working on a new architecture, but Apple and others are way ahead. Now let's make this really straightforward. The original Apple M1 had 16 billion transistors per chip. And you could see in that diagram, the recently launched M1 Ultra has $114 billion per chip. Now if you take into account the size of the chips, which are increasing, and the increase in the number of transistors per chip, that transistor density, that's a factor of around 6x growth in transistor density per chip in 18 months. Remember Intel, assuming the results in the two previous charts that we showed, assuming they were achievable, is running at 2x every two years, versus 6x for the competition. And AMD and Nvidia are close to that as well because they can take advantage of TSM's learning curve. So in the previous chart with Moore's Law, alive and well, Intel gets to a trillion transistors by 2030. The Apple ARM and Nvidia ecosystems will arrive at that point years ahead of Intel. That means lower costs and significantly better competitive advantage. Okay, so where does that leave Intel? The story is really not resonating with investors and hasn't for a while. On February 18th, the day after its investor meeting, the stock was off. It's rebound a little bit but investors are, you know, they're probably prudent to wait unless they have really a long term view. And you can see Intel's performance relative to some of the major competitors. You know, Pat talked about five nodes in for years. He made a big deal out of that, and he shared proof points with Alder Lake and Meteor Lake and other nodes, but Intel just delayed granite rapids last month that pushed it out from 2023 to 2024. And it told investors that we're going to have to boost spending to turn this ship around, which is absolutely the case. And that delay in chips I feel like the first disappointment won't be the last. But as we've said many times, it's very difficult, actually, it's impossible to quickly catch up in semiconductors, and Intel will never catch up without volume. So we'll leave you by iterating our scenario that could save Intel, and that's if its Foundry business can eventually win back Apple to supercharge its volume story. It's going to be tough to wrestle that business away from TSM especially as TSM is setting up shop in Arizona, with US manufacturing that's going to placate The US government. But look, maybe the government cuts a deal with Apple, says, hey, maybe we'll back off with the DOJ and FTC and as part of the CHIPS Act, you'll have to throw some business at Intel. Would that be enough when combined with other Foundry opportunities Intel could theoretically produce? Maybe. But from this vantage point, it's very unlikely Intel will gain back its true number one leadership position. If it were really paranoid back when David Floyer sounded the alarm 10 years ago, yeah, that might have made a pretty big difference. But honestly, the best we can hope for is Intel's strategy and execution allows it to get competitive volumes by the end of the decade, and this national treasure survives to fight for its leadership position in the 2030s. Because it would take a miracle for that to happen in the 2020s. Okay, that's it for today. Thanks to David Floyer for his contributions to this research. Always a pleasure working with David. Stephanie Chan helps me do much of the background research for "Breaking Analysis," and works with our CUBE editorial team. Kristen Martin and Cheryl Knight to get the word out. And thanks to SiliconANGLE's editor in chief Rob Hof, who comes up with a lot of the great titles that we have for "Breaking Analysis" and gets the word out to the SiliconANGLE audience. Thanks, guys. Great teamwork. Remember, these episodes are all available as podcast wherever you listen. Just search "Breaking Analysis Podcast." You'll want to check out ETR's website @etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You could always get in touch with me on email, david.vellante@siliconangle.com or DM me @dvellante, and comment on my LinkedIn posts. This is Dave Vellante for "theCUBE Insights, Powered by ETR." Have a great week. Stay safe, be well, and we'll see you next time. (upbeat music)

>>Hello, and welcome back to the cubes. Continuous coverage of AWS 2021. We're here live real people, and we're pleased to bring you this hybrid event. The most important hybrid event of the year to wrap up really 20, 21 and kick off next year, we're going to dig into the intersection of machine learning and business intelligence, business intelligence, Innomar, and Corian is here as the senior vice president of technology alliances at click and costy Wasilla caucus is the head of product growth for low code, no code machine learning at AWS gentlemen. Welcome to the >>Cube. Thanks for having us. >>I think the first time you were on at reinvent Sev definitely early last decade of >>My life. I >>Had black hair and it was maybe a 2013, I want to say. So it's been quite a run >>And it's definitely been a, been a privilege. I had a, had a chance to attend pretty much all all reinvents from the first one, eh, with a much fewer people and say this growth year over year. And what's amazing about it. This is beyond the scale, how much you grow, the number of people. It's just the face of innovation. Keeps, keeps accelerating as an it's, just this phenomenal. >>We're lucky that we chose data as sort of a, our business passion. But, um, so speaking of data, what are you hearing from customers about what they want to do with their data and bringing together business intelligence and machine learning it's being injected in, but what are they telling you that they, that they want, that they need? What's the opportunity that you're hearing now? >>So, uh, I think first of all, this is a fascinating, fascinating topic because we're talking kind of about the intersection of, uh, what everybody wants to look to do as the next frontier of, uh, of data with predictive data, because descriptive analytics have been around for a long time, but what coconut use predictive analytics, prescriptive analytics to enrich what we've had with descriptive analytics to be the end of the day, improve the business and what, what I love talking to people around here and just listening to customers, express the, you know, their needs is how can they get more value out of data? So they have the data, they don't use. A lot of the data are in Applegate and they want to use it in more ways. And that's what exciting to discuss those new ways. They want to bring it together >>Because anything you'd add to that from AWS perspective, >>I'll tell you what we don't hear from our customers and that we've stopped hearing what is AI and machine learning. And on the contrary we are hearing, how can we make the teams that already AI and ML a lot more productive and make a lot more of it, for example, how can they iterate a lot faster across the ML workflow, how they can train and build really large state of the art, natural language processing models like DDB DBT three, how can we help customers build, train and tune customer specific models for all their, to be able to bring in hyper personalization to their products? And the other thing we're hearing is how can we help the teams that are not tapping into AI and ML get the most power of it in a way, how could you actually potentially either democratize the building and development of machine learning models? Or how can you, in another way, expose machine learning into applications that analytics users are already using? >>Yeah. So in my, when we first met success was measured in, yeah, I got the Hadoop cluster, the work technically, but to your point, they customers want to get more value out of that data now. And so they want to operationalize machine intelligence. Is that what active intelligence is? >>Um, so active intelligence is something that you have here click started to talk about, but we believe it really represents what customers are trying to achieve. And the reason we use the word active intelligence is if you're going to think about active, not being passive. So, uh, traditional BI, uh, kind of relied on pre-configured historical data sets, which were great for what they did, but today they're kind of out of gas in terms of supporting real time decisioning and action. So what active intelligence is all about is really enabling customers to make it take informed, informed action, not just informed decision informed action in the moment. So when that action needs needs to happen. So in order to accommodate that again, this is really the difference between active and passive. Is it active intelligence is all about innovations to bring real-time data. So it's all just historical data. >>I need real time data that's relevant to what's happening. Now. I need a way to get an intelligent data pipeline. And I lead this data pipeline that makes it real-time data available in the forum and the structure that allows me to make a decision or to take action. And finally, it's really to be designed to drive action, right? So whether it's a manual action or whether it's even completely automated, but it's intelligent, it's informed. So that's, that's what active intelligence is all about that by the way, predictive data fits really well into that entire paradigm. Right. >>I mean, we've been talking for years about real-time and it's like, okay, what is real time? Well, it's real time is before you lose the customer before you lose the patient before the machine explodes. Right? So your point about predictive. Yeah. Now you guys made an announcement yesterday, uh, ADA, which stands for AI, for data analytics, what what's that all about? Well, >>Ate them tries to aims to address the very point I mentioned before our customers that are asking us, how can we give access to our business teams? There are a lot more business needs to machine learning. An AI for data analytics is a set of partner solutions that are ML powered. And they're focusing across the spectrum of analytics from data warehousing, business intelligence, business process automation, and other business application. And the idea is to help our partners bring to our customers a lot of those more ways. And for example, we've built integrations with clique Tableau, snowflake, Workato Pegasystems. And through those, those usually take two flavors. Either we help our partners build a mail and embedded into their applications and in a way, make them more intelligent as Mr. Wright mentioned, or we help our partners expose machine learning capability from AWS, right within the UI. >>So for example, yes, they will launch snowflake integration with SageMaker. Now snowflake user can use the same user experience in three-year the same use, the SQL query that they love and trigger an auto ML process insights maker, right from the same UI and get ML into the same UI. And I'm quite excited to also discuss today about the integration we announced today with click SageMaker integration or that was about it. No, no, no other, so I think, um, what a setups, yeah. You mentioned customers want to create more machine learning. They, they want to build faster, new, more machine learning capabilities, which is whereby the way the, the, uh, no code local, you know, comes into mind. How can you use the autopilot, which is a SageMaker product for enabling faster creation of models. So I want to create models faster. They also want to be able to use models in a sense, monetize them, turn them into value to make them available to more users where they're you there's users are. >>Eh, so, you know, BI environments or experiences like as we started to think about him. So I says, well, be provided with Gleevec. And again, with our active intelligence platform is all about weaving the data into the applications, into the environments, either the analytic workflows that, uh, that users have. So we introduced and are super excited. Uh, we've announced, uh, two integrations. So very robust integration between cloud and Amazon SageMaker. And that includes both our new analytic connector for, uh, uh, Amazon SageMaker and our integration with Amazon SageMaker autopilot. So with integration with SageMaker, we now have ClixSense interacting directly and seamlessly with any model deployed within SageMaker. So again, very much like cost dimension in your experience as a user seamlessly, you now also have predictive predictive data. So as you working in application, as you're interacting with your data, dynamically data is interchanged between click and SageMaker in reaching your decision, making your actions with predictive datasets. And that's, what's so cool about it. So again, the clinic environment, we bring real-time data in, prepare it for analytics, and then feed that real-time data to SageMaker to get the real-time prediction back in the same experience for the user. So we're really, really excited about that. So >>Translate what that means for customers is that everything happens faster. Is it unlocked new capabilities? Can we unpack >>A little bit? Absolutely. So aware in a way, bridging the chasm between the data science world and the business teams. So the data science teams are building machine learning models to make predictions. And now with the first integration that Myra mentioned, we actually expose those machine learning models in an application that the business team uses click and with the same dashboards that they are very familiar with can now trigger those machine learning models and get real time predictions in the dashboards themselves powered by machine learning. So in a way, this chasm between the two worlds of data science and business users is completely bruised. And the second integration we built with autopilot, she helps data engineers use completely their own machine learning technology powered by AWS pacemaker. So a data engineers creating different pipelines and through those pipelines, they can now with a building block, add auto ML capabilities in that pipeline without them really knowing machine learning. So we bridge the gap of the business teams, getting access to the data science teams and also bringing the skillset gap for the data engineers to tap into machine learning. You mentioned >>Monitor monetization before. So this to me is key because who's going to do with doing the monetization. It's the business lines that are going to do that, not the data scientists data they're going to enable that, but ultimately it's those data consumers that are building those, I call them data products that they can ultimately monetize. And that's, I'm interested in low-code no-code who sits in your title too, so that all plays in doesn't it? >>Yeah, you guys, and we're heavily invested into that whole space. So for example, today we just launched SageMaker canvas. That is a low-code no-code capability for analysts and business users, but we realized we don't need to only innovate on the technology side. We need to also innovate on the partnerships that we built and those integrations help expose those, our technology to wherever our customers want to be the one to be in clique. So be it, let them use the machine learning technology that we are innovating on exactly where they wanted to be. >>Can you give us some customer examples and use cases, maybe make it real for us, >>Uh, for sure. And I, and I think as you, as you think about these use cases, one of the other things I want to do to kind of envision is the fact that all this predictive data and all this integration that we're talking about is not, can actually express itself in a lot of different experiences for the user. It can be a dashboard. It can also be a conversation analytics, which is part of what we offer in the cloud. So you can actually, he can arrive and interact with the data. You don't have to actually look at it. It can be alerts that actually look automatically and inform you that you need to take action. So you don't actually look at the data. The data will come to you when it, when it needs you including base on, on predictive data. So there's a lot of, uh, a lot of options about how you're going to do it. >>Then give me, let me give you, let me give you an example. I'll let me try and maybe pick one that is intuitive. I think for, for many, for many people sales, right? So you have sales, you have a lot of orders. You want to try to close to closing a quarter, you have a forecast, the deals you expect to close. Uh, and then you can use machine learning for example, to forecast or to try to project which, which deals you're going to lose. So now again, that can look at a lot of different aspects of the deal, the timing, the folder, the volume, the amounts, a lot of other parameters, right. Then predict if you're going to lose a deal. So now, if there's a deal that I, that my sales person is telling me, he's going to win, but the mall is telling me you may lose, well, I probably want to double click on that one. >>Right? So I cannot bring that information right in again, in the moment it is to the seller or to the management, so they can identify it and take action. Now, not only can I bring it to them, but I can also, you know, from the machine learning, you know, what is the likely reason that they lose? And if I know the likely reason, it also become prescriptive, I now can know what to do to try and fix it, right. So I can either do it again manually, or it can also integrate it, uh, again, you know, click cloud. We also also click on application automation, which is again, also kind of a low-code no-code environment to orchestrate processes. I can also take that automatically, also update back Salesforce or the CRM. Okay. So that the metadata management system gets updated. So you got an example, exactly. The example of active intelligence. It allows me to take informed action in the now in the moment about making the best example. >>And if Salesforce salesperson, maybe I prioritize and the machines helping me direct my resources. Is this available today? Is it in general availability >>Available right now? Right? Anyone can go start it right now and click LA >>Congratulations. Um, last question. So what's the future hold for this partnership? Where are you guys headed? Give us a little >>Direction. First of all, would love to scale those integrations. So if you're a customer of Blake, please go ahead and test them and do sir, the feedback. And second for us, we really want to learn from our customers and improve those integrations. We bring to them, we really want to hear what technologies they want to expose to a lot more users. And we are aspiring to build that partnership and get a lot more tight aligned with, uh, with Glick. And, uh, thank you costly. And, uh, we, we see tremendous additional opportunities. I think Amazon tells it where I would say is, well, we're in day one. That that's how we kind of feel about it. There's only so much we put into it, but the market is so dynamic. There's so many new needs that are coming up. So we kind of think about it that way. >>So first of all, we want to journey to expand Lee cloud, adding more services. It's actually a platform where we're bringing both data services. They integration data management, everything related to the analytics pipeline, and of course the analytic services. So it all comes together in one environment that makes it more agile, faster to build these new modern, active intelligence type experiences. So as we do that, we're going to be adding more services, creating more opportunities to integrate with more services from the AWS side. So we're really excited to look at that and just like close to, you mentioned with canvas, you know, Amazon keeps coming up with new new services and new capabilities. So there's gonna be a lot of more opportunity. Eh, we're gonna keep, uh, again, within spirit of our partnership where we want to, you know, jump first innovate quickly and, uh, you know, create is integration, adds value to customer >>Often the flywheel that's. I love it. Great. Great to have you guys awesome to reconnect. All right. Appreciate it. Thank you for watching. This is the queue and we're covering AWS reinvent 2021. We're the leader in high tech coverage, right back

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante semiconductors are the heart of technology innovation for decades technology improvements have marched the cadence of silicon advancements in performance cost power and packaging in the past 10 years the dynamics of the semiconductor industry have changed dramatically soaring factory costs device volume explosions fabulous chip companies greater programmability compressed time to tape out a lot more software content the looming presence of china these and other factors have changed the power structure of the semiconductor business chips today power every aspect of our lives and have led to a global semiconductor shortage that's been well covered but we've never seen anything like it before we believe silicon's success in the next 20 years will be determined by volume manufacturing capabilities design innovation public policy geopolitical dynamics visionary leadership and innovative business models that can survive the intense competition in one of the most challenging businesses in the world hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis it's our pleasure to welcome daniel newman in one of the leading analysts in the technology business and founder of futurum research daniel welcome to the program thanks so much dave great to see you thanks for having me big topic yeah i'll say i'm really looking forward to this and so here's some of the topics that we want to cover today if we have time changes in the semiconductor industry i've said they've been dramatic the shift to nofap companies we're going to talk about volume manufacturing those shifts that have occurred largely due to the arm model we want to cover intel and dig into that and what it has to do to to survive and thrive these changes and then we want to take a look at how alternative processors are impacting the world people talk about is moore's law dead is it alive and well daniel you have strong perspectives on all of this including nvidia love to get your thoughts on on that plus talk about the looming china threat as i mentioned in in the intro but daniel before we get into it do these topics they sound okay how do you see the state of the semiconductor industry today where have we come from where are we and where are we going at the macro level there are a lot of different narratives that are streaming alongside and they're not running in parallel so much as they're running and converging towards one another but it gradually different uh you know degrees so the last two years has welcomed a semiconductor conversation that we really hadn't had and that was supply chain driven the covid19 pandemic brought pretty much unprecedented desire demand thirst or products that are powered by semiconductors and it wasn't until we started running out of laptops of vehicles of servers that the whole world kind of put the semiconductor in focus again like it was just one of those things dave that we as a society it's sort of taken for granted like if you need a laptop you go buy a laptop if you needed a vehicle there'd always be one on the lot um but as we've seen kind of this exponentialism that's taken place throughout the pandemic what we ended up realizing is that semiconductors are eating the world and in fact the next industrial the entire industrial itself the complex is powered by semiconductor technology so everything we we do and we want to do right you went from a vehicle that might have had 50 or 100 worth of semiconductors on a few different parts to one that might have 700 800 different chips in it thousands of dollars worth of semi of semiconductors so you know across the board though yes you're dealing with the dynamics of the shortage you're dealing with the dynamics of innovation you're dealing with moore's law and sort of coming to the end which is leading to new process we're dealing with the foundry versus fab versus invention and product development uh situation so there's so many different concurrent semiconductor narratives that are going on dave and we can talk about any of them and all of them and i'm sure as we do we'll overlap all these different themes you know maybe you can solve this mystery for me there's this this this chip shortage and you can't invent vehicle inventory is so tight but yet when you listen to uh the the ads if the the auto manufacturers are pounding the advertising maybe they're afraid of tesla they don't want to lose their brand awareness but anyway so listen it's by the way a background i want to get a little bit academic here but but bear with me i want to introduce actually reintroduce the concept of wright's law to our audience we know we all know about moore's law but the earlier instantiation actually comes from theodore wright t.p wright he was this engineer in the airplane industry and the math is a little bit abstract to apply but roughly translated says as the cumulative number of units produced doubles your cost per unit declines by a fixed percentage now in airplanes that was around 15 percent in semiconductors we think that numbers more like 20 25 when you add the performance improvements you get from silicon advancements it translates into something like 33 percent cost cost declines when you can double your cumulative volume so that's very important because it confers strategic advantage to the company with the largest volume so it's a learning curve dynamic and it's like andy jassy says daniel there's no compression algorithm for experience and it definitely applies here so if you apply wright's law to what's happening in the industry today we think we can get a better understanding of for instance why tsmc is dominating and why intel is struggling any quick thoughts on that well you have to take every formula like that in any sort of standard mathematics and kind of throw it out the window when you're dealing with the economic situation we are right now i'm not i'm not actually throwing it out the window but what i'm saying is that when supply and demand get out of whack some of those laws become a little bit um more difficult to sustain over the long term what i will say about that is we have certainly seen this found um this fabulous model explode over the last few years you're seeing companies that can focus on software frameworks and innovation that aren't necessarily getting caught up in dealing with the large capital expenditures and overhead the ability to as you suggested in the topics here partner with a company like arm that's developing innovation and then and then um you know offering it uh to everybody right and for a licensee and then they can quickly build we're seeing what that's doing with companies like aws that are saying we're going to just build it alibaba we're just going to build it these aren't chip makers these aren't companies that were even considered chip makers they are now today competing as chip makers so there's a lot of different dynamics going back to your comment about wright's law like i said as we normalize and we figure out this situation on a global scale um i do believe that the who can manufacture the most will certainly continue to have significant competitive advantages yeah no so that's a really interesting point that you're bringing up because one of the things that it leads me to think is that the chip shortage could actually benefit intel i think will benefit intel so i want to introduce this some other data and then get your thoughts on this very simply the chart on the left shows pc shipments which peaked in in 2011 and then began at steady decline until covid and they've the pcs as we know have popped up in terms of volume in the past year and looks like they'll be up again this year the chart on the right is cumulative arm shipments and so as we've reported we think arm wafer volumes are 10x those of x86 volumes and and as such the arm ecosystem has far better cost structure than intel and that's why pat gelsinger was called in to sort of save the day so so daniel i just kind of again opened up this this can of worms but i think you're saying long term volume is going to be critical that's going to confer low cost advantages but in the in in the near to mid-term intel could actually benefit from uh from this chip shortage well intel is the opportunity to position itself as a leader in solving the repatriation crisis uh this will kind of carry over when we talk more about china and taiwan and that relationship and what's going on there we've really identified a massive gap in our uh in america supply chain in the global supply chain because we went from i don't have the stat off hand but i have a rough number dave and we can validate this later but i think it was in like the 30-ish high 30ish percentile of manufacturing of chips were done here in the united states around 1990 and now we're sub 10 as of 2020. so we we offshored almost all of our production and so when we hit this crisis and we needed more manufacturing volume we didn't have it ready part of the problem is you get people like elon musk that come out and make comments to the media like oh it'll be fixed later this year well you can't build a fab in a year you can't build a fab and start producing volume and the other problem is not all chips are the same so not every fab can produce every chip and when you do have fabs that are capable of producing multiple chips it costs millions of dollars to change the hardware and to actually change the process so it's not like oh we're going to build 28 today because that's what ford needs to get all those f-150s out of the lot and tomorrow we're going to pump out more sevens for you know a bunch of hp pcs it's a major overhaul every time you want to retool so there's a lot of complexity here but intel is the one domestic company us-based that has basically raised its hand and said we're going to put major dollars into this and by the way dave the arm chart you showed me could have a very big implication as to why intel wants to do that yeah so right because that's that's a big part of of foundry right is is get those volumes up so i want to hold that thought because i just want to introduce one more data point because one of the things we often talk about is the way in which alternative processors have exploded onto the scene and this chart here if you could bring that up patrick thank you shows the way in which i think you're pointing out intel is responding uh by leveraging alternative fat but once again you know kind of getting getting serious about manufacturing chips what the chart shows is the performance curve it's on a log scale for in the blue line is x86 and the orange line is apple's a series and we're using that as a proxy for sort of the curve that arm is on and it's in its performance over time culminating in the a15 and it measures trillions of operations per second so if you take the traditional x86 curve of doubling every 18 to 24 months that comes out roughly to about 40 percent improvement per year in performance and that's diminishing as we all know to around 30 percent a year because the moore's law is waning the orange line is powered by arm and it's growing at over a hundred percent really 110 per year when you do the math and that's when you combine the cpu the the the neural processing unit the the the xpu the dsps the accelerators et cetera so we're seeing apple use arm aws to you to your point is building chips on on graviton and and and tesla's using our list is long and this is one reason why so daniel this curve is it feels like it's the new performance curve in the industry yeah we are certainly in an era where companies are able to take control of the innovation curve using the development using the open ecosystem of arm having more direct control and price control and of course part of that massive arm number has to do with you know mobile devices and iot and devices that have huge scale but at the same time a lot of companies have made the decision either to move some portion of their product development on arm or to move entirely on arm part of why it was so attractive to nvidia part of the reason that it's under so much scrutiny that that deal um whether that deal will end up getting completed dave but we are seeing an era where we want we i said lust for power i talked about lust for semiconductors our lust for our technology to do more uh whether that's software-defined vehicles whether that's the smartphones we keep in our pocket or the desktop computer we use we want these machines to be as powerful and fast and responsive and scalable as possible if you can get 100 where you can get 30 improvement with each year and generation what is the consumer going to want so i think companies are as normal following the demand of consumers and what's available and at the same time there's some economic benefits they're they're able to realize as well i i don't want to i don't want to go too deep into nvidia arm but what do you handicap that that the chances that that acquisition actually happens oh boy um right now there's a lot of reasons it should happen but there are some reasons that it shouldn't i still kind of consider it a coin toss at this point because fundamentally speaking um you know it should create more competition but there are some people out there that believe it could cause less and so i think this is going to be hung up with regulators a little bit longer than we thought we've already sort of had some previews into that dave with the extensions and some of the timelines that have already been given um i know that was a safe answer and i will take credit for being safe this one's going to be a hard one to call but it certainly makes nvidia an amazing uh it gives amazing prospects to nvidia if they're able to get this deal done yeah i i agree with you i think it's 50 50. okay my i want to pose the question is intel too strategic to fail in march of this year we published this article where we posed that question uh you and i both know pat pretty well we talked about at the time the multi-front war intel is waging in a war with amd the arm ecosystem tsmc the design firms china and we looked at the company's moves which seemed to be right from a strategy standpoint the looking at the potential impact of the u.s government intel's partnership with ibm and what that might portend the us government has a huge incentive to make sure intel wins with onshore manufacturing and that looming threat from china but daniel is intel too strategic to fail and is pat gelsinger making the right moves well first of all i do believe at this current juncture where the semiconductor and supply chain shortage and crisis still looms that intel is too strategic to fail i also believe that intel's demise is somewhat overstated not to say intel doesn't have a slate of challenges that it's going to need to address long term just with the technology adoption curve that you showed being one of them dave but you have to remember the company still has nearly 90 of the server cpu market it still has a significant market share in client and pc it is seeing market share erosion but it's not happened nearly as fast as some people had suggested it would happen with right now with the demand in place and as high as it is intel is selling chips just about as quickly as it can make them and so we right now are sort of seeing the tam as a whole the demand as a whole continue to expand and so intel is fulfilling that need but where are they really too strategic to fail i mean we've seen in certain markets in certain uh process in um you know client for instance where amd has gained of course that's still x86 we've seen uh where the m1 was kind of initially thought to be potentially a pro product that would take some time it didn't take nearly as long for them to get that product in good shape um but the foundry and fab side is where i think intel really has a chance to flourish right now one it can play in the arm space it can build these facilities to be able to produce and help support the production of volumes of chips using arm designs so that actually gives intel and inroads two is it's the company that has made the most outspoken commitment to invest in the manufacturing needs of the united states both here in the united states and in other places across the world where we have friendly ally relationships and need more production capabilities if not in intel b and there is no other logical company that's us-based that's going to meet the regulator and policymakers requirements right now that is also raising their hand and saying we have the know-how we've been doing this we can do more of this and so i think pat is leaning into the right area and i think what will happen is very likely intel will support manufacturing of chips by companies like qualcomm companies like nvidia and if they're able to do that some of the market share losses that they're potentially facing with innovation challenges um and engineering challenges could be offset with growth in their fab and foundry businesses and i think i think pat identified it i think he's going to market with it and you know convincing the street that's going to be a whole nother thing that this is exciting um but i think as the street sees the opportunity here this is an area that intel can really lean into so i think i i think people generally would recognize at least the folks i talk to and it'll be interested in your thoughts who really know this business that intel you know had the best manufacturing process in in the world obviously that's coming to question but but but but for instance people say well intel's 10 nanometer you know is comparable to tsm seven nanometer and that's sort of overstated their their nanometer you know loss but but so so they they were able to point as they were able to sort of hide some of the issues maybe in design with great process and and i i believe that comes down to volume so the question i have then is and i think so i think patrick's pat is doing the right thing because he's going after volume and that's what foundry brings but can he get enough volume or does he need for inst for instance i mean one of the theories i've put out there is that apple could could save the day for intel if the if the us government gets apple in a headlock and says hey we'll back off on break up big tech but you got to give pat some of your foundry volume that puts him on a steeper learning curve do you do you worry sometimes though daniel that intel just even with like qualcomm and broadcom who by the way are competitors of theirs and don't necessarily love them but even even so if they could get that those wins that they still won't have the volume to compete on a cost basis or do you feel like even if they're numbered a number three even behind samsung it's good enough what are your thoughts on that well i don't believe a company like intel goes into a business full steam and they're not new to this business but the obvious volume and expansion that they're looking at with the intention of being number two or three these great companies and you know that's same thing i always say with google cloud google's not out to be the third cloud they're out to be one well that's intel will want to to be stronger if the us government and these investments that it's looking at making this 50 plus billion dollars is looking to pour into this particular space which i don't think is actually enough but if if the government makes these commitments and intel being likely one of the recipients of at least some of these dollars to help expedite this process move forward with building these facilities to make increased manufacturing very likely there's going to be some precedent of law a policy that is going to be put in place to make sure that a certain amount of the volume is done here stateside with companies this is a strategic imperative this is a government strategic imperative this is a putting the country at risk of losing its technology leadership if we cannot manufacture and control this process of innovation so i think intel is going to have that as a benefit that the government is going to most likely require some of this manufacturing to take place here um especially if this investment is made the last thing they're going to want to do is build a bunch of foundries and build a bunch of fabs and end up having them not at capacity especially when the world has seen how much of the manufacturing is now being done in taiwan so i think we're concluding and i i i correctly if i'm wrong but intel is too strategic to fail and and i i sometimes worry they can go bankrupt you know trying to compete with the likes of tsmc and that's why the the the public policy and the in the in the partnership with the u.s government and the eu is i think so important yeah i don't think bankruptcy is an immediate issue i think um but while i follow your train of thought dave i think what you're really looking at more is can the company grow and continue to get support where i worry about is shareholders getting exhausted with intel's the merry-go-round of not growing fast enough not gaining market share not being clearly identified as a leader in any particular process or technology and sort of just playing the role of the incumbent and they the company needs to whether it's in ai whether it's at the edge whether it's in the communications and service provider space intel is doing well you look at their quarterly numbers they're making money but if you had to say where are they leading right now what what which thing is intel really winning uh consistently at you know you look at like ai and ml and people will point to nvidia you look at you know innovation for um client you know and even amd has been super disruptive and difficult for intel uh of course you we've already talked about in like mobile um how impactful arm has been and arm is also playing a pretty big role in servers so like i said the market share and the technology leadership are a little out of skew right now and i think that's where pat's really working hard is identifying the opportunities for for intel to play market leader and technology leader again and for the market to clearly say yes um fab and foundry you know could this be an area where intel becomes the clear leader domestically and i think that the answer is definitely yes because none of the big chipmakers in the us are are doing fabrication you know they're they're all outsourcing it to overseas so if intel can really lead that here grow that large here then it takes some of the pressure off of the process and the innovation side and that's not to say that intel won't have to keep moving there but it does augment the revenue creates a new profit center and makes the company even more strategic here domestically yeah and global foundry tapped out of of sub 10 nanometer and that's why ibm's pseudonym hey wait a minute you had a commitment there the concern i have and this is where again your point is i think really important with the chip shortage you know to go from you know initial design to tape out took tesla and apple you know sub sub 24 months you know probably 18 months with intel we're on a three-year design to tape out cycle maybe even four years so they've got to compress that but that as you well know that's a really hard thing to do but the chip shortage is buying them time and i think that's a really important point that you brought out early in this segment so but the other big question daniel i want to test with you is well you mentioned this about seeing arm in the enterprise not a lot of people talk about that or have visibility on that but i think you're right on so will arm and nvidia be able to seriously penetrate the enterprise the server business in particular clearly jensen wants to be there now this data from etr lays out many of the enterprise players and we've superimposed the semiconductor giants in logos the data is an xy chart it shows net score that's etr's measure of spending momentum on the vertical axis and market share on the horizontal axis market share is not like idc market share its presence in the data set and as we reported before aws is leading the charge in enterprise architecture as daniel mentioned they're they're designing their own chips nitro and graviton microsoft is following suit as is google vmware has project monterey cisco is on the chart dell hp ibm with red hat are also shown and we've superimposed intel nvidia china and arm and now we can debate the position of the logos but we know that one intel has a dominant position in the data center it's got to protect that business it cannot lose ground as it has in pcs because the margin pressure it would face two we know aws with its annapurna acquisition is trying to control its own destiny three we know vmware has project monterey and is following aws's lead to support these new workloads beyond x86 general purpose they got partnerships with pansando and arm and others and four we know cisco they've got chip design chops as does hpe maybe to a lesser extent and of course we know ibm has excellent semiconductor design expertise especially when it comes to things like memory disaggregation as i said jensen's going hard after the data center you know him well daniel we know china wants to control its own destiny and then there's arm it dominates mobile as you pointed out in iot can it make a play for the data center daniel how do you see this picture and what are your thoughts on the future of enterprise in the context of semiconductor competition it's going to take some time i believe but some of the investments and products that have been brought to market and you mentioned that shorter tape out period that shorter period for innovation whether it's you know the graviton uh you know on aws or the aiml chips that uh with trainium and inferentia how quickly aws was able to you know develop build deploy to market an arm-based solution that is being well received and becoming an increasing component of the services and and uh products that are being offered from aws at this point it's still pretty small and i would i would suggest that nvidia and arm in the spirit of trying to get this deal done probably don't necess don't want the enterprise opportunity to be overly inflated as to how quickly the company's going to be able to play in that space because that would somewhat maybe slow or bring up some caution flags that of the regulators that are that are monitoring this at the same time you could argue that arm offering additional options in competition much like it's doing in client will offer new form factors new designs um new uh you know new skus the oems will be able to create more customized uh hardware offerings that might be able to be unique for certain enterprises industries can put more focus you know we're seeing the disaggregation with dpus and how that technology using arm with what aws is doing with nitro but what what these different companies are doing to use you know semiconductor technology to split out security networking and storage and so you start to see design innovation could become very interesting on the foundation of arm so in time i certainly see momentum right now the thing is is most companies in the enterprise are looking for something that's fairly well baked off the shelf that can meet their needs whether it's sap or whether it's you know running different custom applications that the business is built on top of commerce solutions and so intel meets most of those needs and so arm has made a lot of sense for instance with these cloud scale providers but not necessarily as much sense for enterprises especially those that don't want to necessarily look at refactoring all the workloads but as software becomes simpler as refactoring becomes easier to do between different uh different technologies and processes you start to say well arm could be compelling and you know because the the bottom line is we know this from mobile devices is most of us don't care what the processor is the average person the average data you know they look at many of these companies the same in enterprise it's always mattered um kind of like in the pc world it used to really matter that's where intel inside was born but as we continue to grow up and you see these different processes these different companies nvidia amd intel all seen as very worthy companies with very capable technologies in the data center if they can offer economics if they can offer performance if they can offer faster time to value people will look at them so i'd say in time dave the answer is arm will certainly become more and more competitive in the data center like it was able to do at the edge in immobile yeah one of the things that we've talked about is that you know the software-defined data center is awesome but it also created a lot of wasted overhead in terms of offloading storage and and networking security and that much of that is being done with general purpose x86 processors which are more expensive than than for instance using um if you look at what as you mentioned great summary of what aws is doing with graviton and trainium and other other tooling what ampere is doing um in in in oracle and you're seeing both of those companies for example particularly aws get isvs to write so they can run general purpose applications on um on arm-based processors as well it sets up well for ai inferencing at the edge which we know arms dominating the edge we see all these new types of workloads coming into the data center if you look at what companies like nebulon and pensando and and others are doing uh you're seeing a lot of their offloads are going to arm they're putting arm in even though they're still using x86 in a lot of cases but but but they're offloading to arm so it seems like they're coming into the back door i understand your point actually about they don't want to overplay their hand there especially during these negotiations but we think that that long term you know it bears watching but intel they have such a strong presence they got a super strong ecosystem and they really have great relationships with a lot of the the enterprise players and they have influence over them so they're going to use that the the the chip shortage benefits them the uh the relationship with the us government pat is spending a lot of time you know working that so it's really going to be interesting to see how this plays out daniel i want to give you the last word your final thoughts on what we talked about today and where you see this all headed i think the world benefits as a whole with more competition and more innovation pressure i like to see more players coming into the fray i think we've seen intel react over the last year under pat gelsinger's leadership we've seen the technology innovation the angstrom era the 20a we're starting to see what that roadmap is going to look like we've certainly seen how companies like nvidia can disrupt come into market and not just using hardware but using software to play a major role but as a whole as innovation continues to take form at scale we all benefit it means more intelligent software-defined vehicles it puts phones in our hands that are more powerful it gives power to you know cities governments and enterprises that can build applications and tools that give us social networks and give us data-driven experiences so i'm very bullish and optimistic on as a whole i said this before i say it again i believe semiconductors will eat the world and then you know you look at the we didn't even really talk about the companies um you know whether it's in ai uh like you know grok or grav core there are some very cool companies building things you've got qualcomm bought nuvia another company that could you know come out of the blue and offer us new innovations in mobile and personal computing i mean there's so many cool companies dave with the scale of data the uh the the growth and demand and desire for connectivity in the world um it's never been a more interesting time to be a fan of technology the only thing i will say as a whole as a society as i hope we can fix this problem because it does create risks the supply chain inflation the economics all that stuff ties together and a lot of people don't see that but if we can't get this manufacturing issue under control we didn't really talk about china dave and i'll just say taiwan and china are very physically close together and the way that china sees taiwan and the way we see taiwan is completely different we have very little control over what can happen we've all seen what's happened with hong kong so there's just so many as i said when i started this conversation we've got all these trains on the track they're all moving but they're not in parallel these tracks are all converging but the convergence isn't perpendicular so sometimes we don't see how all these things interrelate but as a whole it's a very exciting time love being in technology and uh love having the chance to come out here and talk with you i love the optimism and you're right uh that competition in china that's going to come from china as well xi has made it a part of his legacy i think to you know re-incorporate taiwan that's going to be interesting to see i mean taiwan ebbs and flows with regard to you know its leadership sometimes they're more pro i guess i should say less anti-china maybe that's the better way to say it uh and and and you know china's putting in big fab capacity for nand you know maybe maybe people look at that you know some of that is the low end of the market but you know clay christensen would say well to go take a look at the steel industry and see what happened there so so we didn't talk much about china and that was my oversight but but they're after self-sufficiency it's not like they haven't tried before kind of like intel has tried foundry before but i think they're really going for it this time but but now what are your do you believe that china will be able to get self-sufficiency let's say within the next 10 to 15 years with semiconductors yes i would never count china out of anything if they put their mind to it if it's something that they want to put absolute focus on i think um right now china vacillates between wanting to be a good player and a good steward to the world and wanting to completely run its own show the the politicization of what's going on over there we all saw what happened in the real estate market this past week we saw what happened with tech ed over the last few months we've seen what's happened with uh innovation and entrepreneurship it is not entirely clear if china wants to give the more capitalistic and innovation ecosystem a full try but it is certainly shown that it wants to be seen as a world leader over the last few decades it's accomplished that in almost any area that it wants to compete dave i would say if this is one of gigi ping's primary focuses wanting to do this it would be very irresponsible to rule it out as a possibility daniel i gotta tell you i i love collaborating with you um we met face to face just recently and i hope we could do this again i'd love to have you you back on on the program thanks so much for your your time and insights today thanks for having me dave so daniel's website futuram research that's three use in futurum uh check that out for termresearch.com uh the the this individual is really plugged in he's forward thinking and and a great resource at daniel newman uv is his twitter so go follow him for some great stuff and remember these episodes are all available as podcasts wherever you listen all you do is search for breaking analysis podcast we publish each week on wikibon.com and siliconangle.com and by the way daniel thank you for contributing your your quotes to siliconangle the writers there love you uh you can always connect on twitter i'm at divalanto you can email me at david.velante at siliconangle.com appreciate the comments on linkedin and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time you

>>From around the globe. It's the cube with digital coverage of IBM. Think 20, 21 brought to you by IBM. >>Hello everyone. And welcome back to IBM. Think 20, 21, the virtual edition, the cubes continuous coverage. And we're excited to talk about people. How do you align people and technology? Of course, there's a lot of process in between. Those are the hard, hard things. Technology is sometimes easy. Amy Wright is here. She's managing partner of talent transformation at IBM, Amy. Great to see you. >>Thanks. Great to be here. And Dave, >>Yeah, you know, we love to talk tech and sometimes we kind of sweep the really hard stuff under the rug. And we talk about transformation. I mean, it's, it's ongoing. I mean, you think about the pandemic last year, it was sort of this forced March to digital. We had to transform overnight, you know, the vast majority of leaders. I think that in figures like close to 95, 96% say that they've accelerated their digital transformation by half a half a decade. Uh, and of course that was a lot of, it was like I say, it's a forced March, so it wasn't really planful, but now they've got time to plan about a digital first approach and how to deal with remote workers. I wonder if you could talk about the role that people play in that digital transformation, >>Right. Thanks, Dave. Uh, I'm happy to, you know, a lot of people think of digital transformation about being technology oriented. It's a total shift in tech and it is, but it really can't be successful with just tech. So you're right with the pandemic has done for digital transformation. Is it really, it pushed us to these technology extremes more than anyone could have anticipated, particularly with our ways of working, being remote. It also pushed us to extremes and highlighted the role that humanity play played plays will continue to play. So we've been pushed to reimagine jobs, push to re-imagine workplaces, uh, push to re-imagine, how technology can deliver this connected enterprise, um, you know, through, through virtual reality, um, and virtual working, wasn't really something that was accepted before, but now we've been, you know, forced to accept it, which is, which is really great for the digital transformation because it accelerated that. >>So the connected enterprise though, isn't really just working virtually it's these new levels of productivity and decision-making that are enabled by intelligent workflows and cloud and data. And so the technology is absolutely critically important, but automation doesn't have empathy. So it takes people to turn these insights that are brought to us through technology and automation. It takes people to turn them into action and it's that human technology partnership that's required for the digital transformation to get to that desired impact. So when you think about, when we think about people in the role they play and, uh, you know, the pivotal role they play truly, multi-part, it's kind of three parts. One is people are the ones that build tech. And so they influence whether or not the automation is going to work, whether it meets the needs of the enterprise, if it takes advantage of the latest thinking, um, and it fits, you know, it was irresistible. >>If you will. The second is the people use the technology to gain this meaningful insight and turn it into action. And then the third is the people are the ones that embed this tech change into culture. So that's actually sustainable. So to be able to drive this sustainable digital transformation, the people, it requires the people to make it happen. So if you look at healthcare, Dave, think about the dramatic shift in healthcare in the past year where doctors have shifted to telemedicine, nurses have shifted to using iPads as caregivers at the, you know, with their patients that not only required to shift in the tech, but an adoption of caregivers have a new way of working that again, couldn't have been successful unless they adopted an embedded and embedded a different way of working in a different culture in everything that they do. >>You know, what you said is really important, especially we talk a lot about what machines can do that people can't and what people can do that machines can't and you just nailed it with, with empathy. And, and when you think about to the remote work, I think prior to the pandemic, it was probably around 15, 16% of workers were remote. And when you, when we do, we do surveys with a partner ETR out in New York and they, they project based on the surveys that, that that's going to double, but somewhere between 33, 35%, but people don't really know when, when you talk to people, they go, Hey, I kind of like working at home. Other people say, I can't wait to get back to the office. So people obviously critical part of the digital transformation, but how do you think about creating those meaningful experiences at work, whether that's remote, part-time remote, you know, full-time back at work. >>So this is a really great, great question because I think our point of view on this has changed. So first of all, most enterprises we talk to will move back to some hybrid kind of environment. We're never going to be everybody back in the office. Again, that's, that's, that's not who we will be moving forward, but the expectations of employees have changed. Um, we all know that, you know, think about your consumer lives and, and how we experienced that personalized ex that, that, that personalization, when we go to buy something online, that's now bled over into the workplace. So the employees expect that exact same personalized experience at work, but it's now so much more than that. Now it's not only personalization, which, you know, obviously tech enables quite dramatically, but the experience is broader to look at a holistic relationship between the employer and the employee. >>That's a little bit less, it's less transactional. Like I do my job and my company pays me for doing this, this set of activities, but it's more supportive and integrated with their personal selves. So, you know, we did a recent study in which we, uh, looked at consumers and employees and their highest priority areas for the expectations that they now have for their employers is career and skill advancement opportunities with speed. Second is work-life balances that might take the form of what hours they worked, our ability to, um, you know, manage with what they're doing in their home with, with their families and children, uh, you know, their ability to be camera ready or not at all times of day and night and actually where they work from. So people are now working, not only at home, but they're moving to different cities and want that flexibility. >>And then third, a high area of priority now is ethics and values. So not only diversity equity inclusion, obviously critically important, but ways of working and meaningful and purposeful work. So when you look at all of those together, the employee experience has grown to be not only that of personalization, like we have an art consumer world that is, that is critically important, but now, um, it's all of these other things, as well. As a matter of fact, they become so important, Dave, that in our recent research, it shows that one in four employees will change employers in 2021, one in four, one in four will also change professions in 2021. And while about 75% of employers, companies believe that they are doing a good job of meeting the needs. These expanded needs of their employees, less than half of employees feel the same way. So there's a lot of work to be done. So you asked the question, why is this people experience so important? It's important because it's required for the digital transformation. And it's so much broader than what we used to think that it's now a competitive differentiator for employers as they try to not only achieve their digital transformation, but as their organizations disrupt over and over again, um, it's, it's a requirement in order to meet their meet their enterprises objectives. >>So it was a great, uh, great stats, Amy, to just put out there. I mean the career advancement, I think, I feel like it's always been there, but it's now much more front and center employees are more vocal about it, the work life balance, same thing. I mean, you're seeing some organizations, you know, a hundred hour weeks where we're revolting and then, you know, the ethics and values piece to me is one of the most interesting, I often joke Milton Friedman rolling over in his grave because he was the economist that said, Oh, it's just about shareholder value. That's it. And that's not anymore. Um, in fact, there's clearly a relationship between shareholder value in, in ESG and ethics and, and young people are very, very concerned about it. So here's the question who's accountable for making sure that you have a positive employee experiences occur. >>Yeah, really, really, really good question. And the thing is, this is what makes it so hard. There's not one group or one person it's actually all of us. And I know that answer sounds like a little bit like a cop out, but this is why it makes it so hard. Every leader's responsible for the employee experience, every manager is responsible for the employee experience. Every employee is actually not responsible for the experience of their teammates. And actually speaking up if the experience isn't using inclusiveness as a, as an example, if it's not inclusive, every experience, every employee has the responsibility to speak up. So some companies actually have employee experience leaders. Some companies have digital transformation leaders that embodies that, that, that includes that employee experience, but most actually start this journey through the, through the partnership between it and HR. So I teach responsible for this technology architecture, the cloud strategy, the data strategy, architectural framework, all those pieces that put together the foundation and the building brought blocks and the security that helped to Mo um, modernize this employee experience. >>And by the way, they're doing this at the same time where they're modernizing their entire way in which the it function operates. Um, so you got it. That's kind of setting the stage and the foundation for what's possible. And then you have HR who's operating as the steward of the employee experience that those people experiences, um, and putting them in place in a consistent and consistent and a positive way across the entire enterprise. So things like design thinking, um, that puts the employee at the center of the way we, um, architect and create these experiences using rapid iterative design principles with, again, with this, with the employee at the center, making that the cultural norm across the enterprise is a really big deal. So HR is usually in the lead on making that happen. But again, this is a cultural shift, not just I have a project, you know, I'll kind of have a project plan and here's, here's what I'm going to execute on leadership roles. >>So HR is, is the steward of leadership and those characteristics of leaders now are changing very dramatically to be more, even in a big enterprise, large global enterprise entrepreneurial transparent, co-creation really at the core of everything. So being transplant transparent with your teams and be able to co-create, um, you know, procreate for the future. So data and AI, we can use data. And I AI now to actually IM uh, uh, predict the impact that the workforce and the cultural will have on business results, predict attrition, predict what different work workforce design scenarios will look like to the supply chain, um, uh, predict the speed of hiring and how that will impact literally bottom line business results. So you said it right, when, when, when you talked about shareholder value, the P people is at the center of shareholder value now. So, um, our functions need to be modernized, but it's really this partnership between HR and it that's gonna be able to make it happen in a big way. >>It's interesting. I'm just thinking that AI as well, can be a Canary in a coal mine when there's potential problems. Um, and I love this transparency. That's critical. Co-creation so, Oh, okay. So tech is a key part of that, especially in terms of when you go from analog to digital, taking friction out of the system shows the employees that we're investing in, in your experience. Uh, but it's more than that. You're saying it's, it's cultural as it makes it so kind of fuzzy cultural it's it's it takes a village. So that's, that's part of what makes it so hard. How do you think about, you know, the journey? Where do you start and how do you keep iterating? You're never done in this, this world. Are you, >>Yeah, that's a question, uh, everybody's asking now, w w where do I start? So, as you said, this is very hard, um, and it's hard. One of the reasons it's hard is it's because it revolves culture. Um, it's not only about technology. They are hard. Technologies are harder in their own, right? It's not just about data, that's hard in its own, right? But once you involve technology, it makes, it makes it even even harder. And of course the people aspect, unless done very proactively and meaningfully, it can be kind of a wild card right on, who's going to adopt what, so where do you start? So, um, the way we like to think about, um, giving advice to enterprises, uh, regarding where this is, we've seen this work well is to pick a business problem. So what's a business problem that if you solved, you can actually make an impact, not only for your people, but for your people, but for the enterprise. >>So if you could pick a business problem and actually fix it, using data, using cloud, using people, experiences using a cultural shift, then you'll get that. Buy-in, you'll get the buy-in that, yes, we can do this. This is this, this is very doable. We can repeat, it's repeating PETA repeatable over and over again. And it has an impact on our culture. That's a great place to start. Okay. So then you say, if that's a place to start, how do we actually, there's got to be foundational things that have to be in place to make that work. So one of them is a consistency in data and the use of AI and the ability to make insights meaningful, you know, that come through data and AI. And the other part that's really important once you pick your business problem is the shift in the way of working the shifts so that, um, it can impact cultural, um, cultural change, uh, shifts so that there's co-creation with your people and there's transparency. >>So each one of these business problems and the way companies, uh, pick to fix them, they, they won't all work. And the way you get that trust and transparency with your people is as scary as it is to share with them what you're attempting to do and share with them how you're doing along the journey. And if it fails, okay, fails, you know, pick yourself back up and start again, that trust and transparency with your people. That's the way, that's the way we all make this cultural impact. So it, you know, kind of the, none of this is to be, to make sustainable change. We can all make short term change. We can do projects, but to make sustainable change. The humanity aspect has to come to life in these digital transformations. And that only comes to life with this cultural shift, >>Amy, right? You've thought about this a lot deep expertise in the area. Really appreciate your sharing it with our audience. And thanks for coming on the cube, Dave, my pleasure. All right. Keep it right there. But this is Dave Volante. You're watching IBM think 2021, the virtual, is it addition from the queue.

>>mhm Yes. >>Welcome back to the cubes coverage of red hat summit 2021 virtual. I'm john for a host of the cube we're here in Palo alto. Were remote with our great guest here cube alumni. I've been on many times chris wright, Senior vice president and CTO of red hat chris great to see you. Always a pleasure to have you on the screen here too. But we're not in person but thanks for coming in remote. >>Yeah, you bet. Glad to be here. >>Not only were talking about speeds and feeds, digital transformation going under the hood here we're gonna talk about red hats, expanded collaboration with boston University to help fund education and research for open source projects. So you guys have a huge relationship with boston University. Talk about this continued commitment. What's the news, what's the, what's the story? >>Well, we have a couple different things going on uh and and the relationship we have with the EU is many years in. So this itself isn't brand new. Um one of the things that's important to highlight here is we are giving something north of $550 million dollars worth of software to be you really in pursuit of running uh powering and running scaled infrastructure. That's part of the open hybrid class. Um and that's that's an important piece which we can touch on a little bit as we talk to this conversation. The other one is like I said, this isn't a new relationship with the U. And what we're doing now is really expanding the relationship. So we've we've built a great connection directly with the You were substantially expanding that. Um The original relationship we had was a $5 million relationship spread over five years now. We're talking about a $20 million Relationship spread over five years. So really a significant expansion. And of course that expansion is connected to some of the work that we plan to do together in this open hybrid cloud infrastructure and research space. So a lot of things coming together at once to really really advance the red hat ca laboratory at the U. That combined effort in bringing you know, cloud research and open source and all these things together >>and a lot of actually going on. So basically the boston area lot of universities, but I love the shirt you're wearing with his red hat innovation in the open. This is kind of one of those things you also mentioned out of this huge subscription of software grant that's going to be you just a huge number give value for for the boston University. But you also have another project that's been going on the collaborative research and education agreement called red hat collaborative orI Okay, this was in place. You mentioned that. How's that tying in because that was pre existing. Now. You've got the grant, you got your funding more and more research. Talk about how this connects into the open cloud initiative because this is kind of interesting. You're not bringing hybrid cloud kind of research and practical value in A i ops is hot. You can't you can't go anywhere these days without having great observe ability. Cloud native more and more is more complex and you've got these young students and researchers dying and get their hands on it. Take us through the connection between the CA laboratory and open open cloud. >>So the CA laboratory is a clever name that just talks about collaboration and research laboratory type research. And initially the CA laboratory focus was on the infrastructure running the cloud and some of the application workloads that can run on top of an open cloud infrastructure uh that are that's very data centric. And so this is uh an opportunity for multidisciplinary work looking at modeling for um for health care, for example for how you can improve imaging and we've had a great results in this collaboration. Um We've talked at times about the relationship with the boston Children's Hospital and the chris project not related to me, but just similar acronym that spells chris. Um and these things come together in part through connecting relationships to academia, where academia as research is increasingly built in on and around open source software. So if you think of two parallel worlds, open source software development, just the activity of building open source software, it brings so many people together and it moves so quickly that if you're not directly connected to that as an academic researcher, you risk producing academic research results that aren't relevant because it's hard for them to connect back to these large, fast moving projects, which may have invented a solution to the problem you've been focused on as an academic if you're not directly connected. So we see academia and open source coming together to build really a next generation of understanding of the scientific in depth and he's joining the >>train operations you're talking about here though, this is significant because there's dollars behind it, right? There's real money, it's not >>just the right software, >>it's it's a center, it's a joint operation. >>That's right. And so when you think about just the academic research of producing um ideas that manifest themselves as code and software projects, we want to make sure we're first connecting the software projects to open source communities in with our own engineering experience, bringing code into these open, open source projects to just advance the the feeds and speeds and speeds, the kind of functionality the state of the art of the actual project. We're also taking this to a new level with this expanded relationship and that is software today. When you, when you operate software as a cloud, a critical part of the software is the operationalization of that software. So software just sitting there on the shelf doesn't do anybody any good. Even if the shelf is an open source project, it's a tar ball waiting for you to download. If you don't ever grab it and run it, it's not doing anybody any good. And if the challenge of running it is substantial enough that it stops you from using that software, you've created a barrier to the value that's locked inside that project. The focus here is how can we take that the operations experience of running a cloud, which itself is a big complex distributed system, tie some of those experiences back into the projects that are used to build that infrastructure. So you're taking not just the output of the project, but also the understanding of what it takes to run a project and bringing that understanding and even the automation and code associated with that back into the project. So, your operational izing this open source software and you're building deeper understanding of what it means to operate things that scale, including data and data sets that you can use to build models that show how you can create the remediation and closed loop systems with AI and machine learning, you know, sort of synthesizing all the data that you generate out of a big distributed infrastructure and feed that back into the operations of that same infrastructure. So a lot going on there at the same time operationalization as as an open source initiative but also um really the understanding advancement of A I and data centric operations, so ai ops and closed the remediation. >>Yeah, I mean, devops developer and operations to operationalize it and certainly cloud Native put an emphasis on Day two operations, which leads a lot more research, a lot more uh student work on understanding the coding environment. Um so with that I got to ask um I asked you about this uh massachusetts focused or this open cloud initiative because you guys are talking about this open cloud initiative including this massachusetts. Open Cloud, what is that? What is the massachusetts? Open Cloud sounds like you're offering a kind of open person, not just bu but other um Yeah, institutions. >>That's right. So the the M o C massachusetts open cloud is itself a cross um organizational collaboration bringing together five different academic institutions in New England In massachusetts. It's bu it's Harvard mit, its Northeastern and its U. Mass. Coming together to support a common set of infrastructure which is cloud. It's a cloud that runs in a data center and then um it serves a couple of different purposes. One is research on clouds directly. So what does it mean to run a cloud? What does it look like from a research point of view to understand large scale distributed systems? And then the other is more on top. When you have a cloud you can run workloads and those workloads scaled out to do say data processing, looking at the implications of across different fields which could be natural sciences, could be medicine, could be, even political science or social science is really a multidisciplinary view of what it means to leverage a cloud and run data centric workloads on top. So two different areas that are of a focus for the M. O. C. And this becomes this sort of vehicle for collaboration between Red Hat View and the Red Hot Laboratory. >>So I have to ask only because I'm a big fan of the area and I went to one of those schools, is there like a bean pot for technical hackathons where you get all the schools matched up against each other on the mass open cloud and compete for who gets bragging rights and the text city there. >>It's a great question. Not yet. But I'll jot that down here in hell. Up on that. >>Happy to sponsor. We'll we'll do the play by play coverage, you know. Great. >>I love that. Yeah, kind of twitch tv style. The one thing that there is which is very practical is academic research grants themselves are competitive, right? People are vying for research dollars to put together proposals, Bring those proposals to um the agency that's that's that's giving out grants and winning those grants is certainly prestigious. It's important as part of her research institutes continue to fund the work that they're doing. Uh Now we've been associated uh through the work we've done to date with the U. With Yeah almost $15 million 20 papers. So there's there's a lot of work you can't quite call the play by play. It's a >>scoreboard. I mean their numbers you can put numbers on the board. I mean that's what's one of the things you can measure. But let me ask you on those grants. So you're saying this is just the bu you guys actually have data on um the impact of the relationship in terms of grants and papers and stuff like that academic work. >>That's right. That's right. And so those numbers that I'm giving you are examples of how we've worked together with the u to help their faculty generate grant dollars that then fund some of the research that's happening there together with redhead engineers and on and on the infrastructure like the massachusetts Open cloud. >>That's a good way to look at the scoreboard. It's a good point. We have to research that if you don't mind me asking on this data that you have um are all those projects contributing to open source or do they have to be? That's just generic. Is that all of you all papers around bu is part of the research. In other words, I'm trying to think if I'm in open source, has this contributed to me as an >>open source? Yeah, it's a big and complex question because there's so much research that can happen through a research institution. And those research grants tend to be governed with agreements and some of those agreements have intellectual property rights um front and center and might require things like open source software as a result, the stuff that we're working on clearly isn't that focus area of open source software and and research activities that help kind of propel our understanding forward of what does it mean to do large scale distributed systems creation and then operation. So how do you develop software that does it? How do you how do you run the software that builds these big large distributed systems? So we're focused in that area. Um some of the work that we facilitated through that focus includes integrating non open source software that might be part of um same medical imaging. So for example work we've done with the boston Children's Hospital That isn't 100 doesn't require us to be involved 100 of the open source pieces. All the infrastructure there to support it is. And so we're learning how we can build integrated pipelines for data analysis and image analysis and data sharing across different institutions uh at the open source project level. Well maybe we have a specific imaging program that is not generated from this project. And of course that's okay with >>us. You know chris you bring up a good point with all those conversations. I could see this really connecting the dots. Most computer science programs. Most engineering programs haven't really traditionally focused on it at the scale we're talking about because we look at cloud scale but now scaling with hybrid it's real engineering going on to think about the large scale. We know all the big hyper scale ear's right so it's not just I. T. Provisioning you know network connection and doing some I. T. Work. We're talking about large scale. So I have to ask you as you guys look at these relationships with academics uh academia like like bu and others um how are the students responding to this? Are you guys seeing any specific graduate level advancements? Because you're talking about operational roles that are becoming so important whether it's cyber security and as cloud needed because once more data driven you need to have all this new scale engineered up. That's >>what how >>do you look at that? >>There's two different pieces that I would highlight. One is just the data science itself. So schools still need to produce data scientists. And having data is a big part of being a data scientist and knowing what your what your goals are with that data and then experimenting with different techniques, whether it's algorithms or tools. It's a big part of being a data scientist sort of spelunking through the data. So we're helping produce data. We're looking at data science efforts around data that's used to operationalize infrastructure, which is an interesting data science endeavor by itself. The other piece is really what you highlighted, which is there's an emergence of a skill set in the industry, often referred to as SRE site reliability engineering. Um it is a engineering discipline. And if you back up a little bit and you start thinking about what are the underlying principles behind large scale distributed systems, you get to some information theory and computer science. So this isn't just something that you might think of as um some simple training of a few key tools and knowing how to interpret a dashboard. And you're good to go, this is a much more sophisticated view of what does it mean to really operate large scale infrastructure, which to date, there aren't a lot of these large scale infrastructures available to academics to research because their commercial endeavors >>and their new to me. I was talking to some young folks my son's age and daughters age and I was saying, you know, architect in a building, a skyscraper isn't trivial. You can't just do that overnight. There's a lot of engineering that goes on in that science, but you're bringing kind of operating systems theory, systems thinking to distributed computing. I mean that's combination of a interdisciplinary shift and you got, I won't say civil engineering, but like concept is there, you've got structure, you've got networks, they're changing and then you've got software so again completely new area. >>That's right and there's not a lot of even curriculum that explores this space. So one of the opportunity, there's a great program that really focuses on um that that space of site reliability engineering or operational izing software. Um And then the other piece that I'm I'm really excited about is connecting to open source communities so that as we build software, we have a way to run and operationalize that software that doesn't have to be directly tied to a commercial outlet. So products running in the cloud will have a commercial S. L. A. And commercial agreements between the user and the producer of that service. How do you do that in open source context? How do you leverage a community, bring that community software to a community run service, learn through the running of that service. How to best build architect the service itself and then operationalized with the tooling and automation that service? How do you, how do you bring that into the open source community? And that's something that we've been referring to as the operate first initiative. How do you get the operationalization of software? Really thought of as a primary focal point in the software project where you normally think about the internals of software, the features, the capabilities of functionality, less about the operationalization. So important shift at the open source project level, which is something that I think will really be interesting and we'll see a lot of reaping a lot of rewards. Just an open source communities directly. >>Yeah, speed and durability. Certainly having that reliability is great. You know, I love talking with you guys at red hat because, you know, software, you know, open source and you know, operating systems because as it comes together in this modern era, what a great, great fit, great work you're doing with Boston University's and the mass open cloud initiative. Congratulations on that. I got I got to ask you about this Red Hat Graduate Fellows program you have because this kind of speaks to what you guys are doing, you have this kind of this redhead graduate fellows network and the work that's being done. Does that translate into red hat at all? From an engineering standpoint? How does that, how does that work together? >>Basically, what we do is we support um PhD students, we support post docs. So there's a real direct support to the, you know, that is the Red Jack Graduate Fellow program on our focus there is connecting those um uh academics, the faculty members and the students to our engineers to work together on key research initiatives that we think will help drive open source software agendas forward really broad can be in all different areas from security to virtualization too, the operating systems to cloud distributed systems, uh and one of the things that we've discovered is it creates a great relationship with the university and we find students that will be excited to leave university and come into the the industry workforce and work at Red hat. So there is a direct talent relationship between the work that we do at bu and the talent that we can bring into red hat, which is awesome. Uh We know these people we've worked with well with them, but also we're kind of expanding understanding of open source across, you know, more and more of academia, which I think is really valuable and important for red hat. We just go out to the the industry at large, um, and helping bring a set of skills to the industry that whether they're coming, whether these are students that come into red hat or go elsewhere into the industry, these are important skills to have in the industry. So we look at the, how do you work in open source communities? How to operationalize software at scale? These are important things. They didn't >>expand, expand the territory if you will in terms of systems thinking. We just talked about great collaboration. You guys do a great job chris great to have you on a quick final word from you on this year at red hat summer. I know it's virtual again, which we could be in person, but we're starting to come out of the covid kind of post covid right around the corner. Um, what's the update? How would you describe the current state of red hat? Obviously you guys still got that, that vibe. You still pumping strong a lot going on. What's the current? What's the current, uh, bumper sticker? What's the vibe? >>Well, in many ways, because we're so large and distributed. Um, the last year has been, uh, can't say business as usual because it's been an impact on everybody, but it hasn't required us to fundamentally change. And as we work across open source communities, there's been a lot of continuity that's come through a workforce that's gone completely distributed. People are anxious to get to the next phase, whatever back to normal means. Uh, and people at Red Hat are no different. So we're looking forward to what it can mean to spend time with colleagues in offices, were looking forward to what it means to spend time together with our friends and families and travel and all those things. But from a, from a business point of view, Red Hat's focus on the open hybrid cloud and that distributed view of how we work with open source communities. That's something that's, it's only continued to grow and pick up over the course of the last year. So it's clearly an important area for the industry and we've been busier than ever the last year. So, uh, interesting, interesting times for everybody. >>Well, it's great to see and I love how the culture maintains its its relevance, its coolness intersection between software, Open Source and systems. Great, Great working congratulations chris. Thanks for coming on. >>Thank you. >>All right. I'm John for here with the Cube for Red Hat Summit 2021. Thanks for watching. Mhm.

>>from >>around the globe. It's the cube with digital coverage >>of IBM. Think 20 >>21 brought to you by IBM. >>Hello, I want to welcome back to IBM think 2021 the virtual edition. The cubes continuous coverage. And we're excited to talk about people. How do you align people and technology? Of course there's a lot of process in between. Those are the hard, hard things technology sometimes easy amy right is here. She's managing partner of talent transformation at IBM Amy great to see you. >>Thanks Great to be here Dave >>Yeah. You know we love to talk tech and sometimes we kind of sweep the really hard stuff under the rug and we talk about transformation. I mean it's it's ongoing. I mean you think about the pandemic last year was sort of this forced march to digital, we had to transform overnight. You know the vast majority of leaders I think that figures like close to 95 96 say that they've accelerated their digital transformation by half a half a decade. And of course that was a lot of it was like I say, it's a forced march, so it wasn't really planned fel but now they've got time to plan about a digital first approach and how to deal with remote workers. I wonder if you could talk about the role that people play in that digital transformation. >>All right, thanks. Dave I'm happy to, you know, a lot of people think of digital transformation about being technology oriented. It's a total shift in tech and it is but it really can't be successful with just tech. So you're right with the pandemic has done for digital transformation, Is it really it pushed us to these technology extremes more than anyone could have anticipated, particularly with our ways of working being remote. It also pushed us to extremes and highlighted the role that humanity played place, it will continue to play. So we've been pushed to reimagine jobs, pushed to reimagine workplaces, push to reimagine how technology can deliver this connected enterprise. Um you know, through through virtuality. Um and virtual working wasn't really something that was accepted before, but now we've been forced to accept it, which is which is really great for the digital transformation because it accelerated that. So the connected enterprise though isn't really just working virtually. It's these new levels of productivity and decision making that are enabled by intelligent workflows and cloud and data. And so technology is absolutely critically important. But automation doesn't have empathy. So it takes people to turn these insights that are brought to us through technology and automation. It takes people to turn them into action. And it's that human technology partnership that's required for the digital transformation to get to that desired impact. So when you think about, when we think about people in the role they play, and, you know, it's the pivotal role they play. It's really multi part, it's kind of three parts. one is people are the ones that build the tech and so they influence whether or not the automation is going to work, whether it meets the needs of the enterprise, if it takes advantage of the latest thinking, um and if it's, you know, it's irresistible if you will. The second is the people use the technology to gain this meaningful insight and turn into action. And then the third is the people are the ones that embed this tech change into culture, so that's actually sustainable. So to be able to drive this sustainable digital transformation the people, it requires the people to make it happen. So, if you look at health care, Dave think about the dramatic shift in health care in the past year, where doctors have shifted to telemedicine, nurses have shifted to using ipads as caregivers at the, you know, with their patients that not only required to shift in the tech, but an adoption of caregivers of a new way of working that again, could have been successful unless they adopted and embedded, embedded a different way of working in a different culture and everything that they do. >>You know what you said is really important. Especially we talk a lot about what machines can do that people can what people can do that machines can you just nailed it with empathy. And and when you think about to the remote work, I think prior to the pandemic, it was probably around 15 16% of workers were remote. And when you when we do we do surveys with the partner E. T. R. In new york. And the They project based on these surveys that that that's gonna double somewhere between you know, 33 35%. But people don't really know when when you talk to people like I kind of like working at home, other people say I can't wait to get back to the office. So people obviously critical part of the digital transformation. But how do you think about creating those meaningful experiences at work? Whether that's remote? Part time, remote? Full time back at work? >>So this is a really great great question because I think our point of view on this has changed. So first of all, most enterprises we talked to will move back to some hybrid kind of environment. We're never going to be everybody back in the office again. That's that's that's not who we will be moving forward. But the expectations of employees have changed. Um We all know that, you know, think about your consumer lives and and how we experience that personalized that that that personalization when we go to buy something online that's now bled over into the workplace. So the employees expect that exact same personalized experience at work. But it's now so much more than that now. It's not only personalization which you know, obviously tech enables quite dramatically, but the experience is broader to look at a holistic relationship between the employer and employee. That's a little bit less, it's less transactional. Like I do my job and my company pays me for doing this set of activities, but it's more supportive and integrated with their personal cells. So, you know, we did a recent study in which we looked at consumers and employees and their highest priority areas for the expectations that they now have for their employers is career and skill advancement opportunities with speed. Second is work life balance is that might take the form of what hours they work. Their ability to um you know, manage with what they're doing in their home with their families and Children, uh you know, their ability to be camera ready or not at all times of day and night and actually where they work from. So people are now working not only at home, but they're moving to different cities and want that flexibility. And then third, hi, area of priority now is ethics and values. So not only diversity, equity, inclusion, obviously critically important, but ways of working and meaningful and purposeful work. So when you look at all of those together, the employee experience has grown to be not only that of personalization, like we have in our consumer world, that is that is critically important, but now um it's all of these other things as well as a matter of fact, they become so important dave that in our recent research, it shows that one in four Employees will change employers in 2021, 1 and 41 and four will also change professions in 2021. And while about 75 of employers, companies believe that they are doing a good job of meeting the needs, these expanded needs of their employees, less than half of employees feel the same way. So there's a lot of work to be done. So you ask the question why is this? People experience so important? It's important because it's required for the digital transformation and it's so much broader than what we used to think that it's now a competitive differentiator for employers as they try to not only achieve their digital transformation but as their organizations disrupt over and over again. Um It's a requirement in order to meet their meet their enterprises objectives. >>So it was a great great stats and you just put out there in the career advancement. I think I feel like it's always been there but it's now much more front and center employees are more vocal about it. The work like balance, same thing. I mean you're seeing some organizations in 100 hour weeks were revolting and and then you know, the ethics and values piece to me is one of the most interesting I often joke Milton Friedman rolling over in his grave because he was, the economist said uh it's just about shareholder value, that's it and that's not anymore. In fact there's clearly a relationship between shareholder value and E. S. G. And and ethics and young people are very very concerned about it. So here's the question who's accountable for making sure that you have a positive employee experiences occur. >>Yeah. Really really really good question. And the thing is this is what makes it so hard. There's not one group or one person it's actually all of us. And I know that answer sounds like a little bit like a cop out. But this is why it makes it so hard. Every leader is responsible for the employee experience, every manager is responsible for the employee experience. Every employee is actually not responsible for the experience of their teammates. And actually speaking up if the experience isn't using inclusiveness as an example if it's not inclusive. Every experience every employee has the responsibility to speak up. So some companies actually have employee experienced leaders. Some companies have digital transformation leaders that embodies that that that includes that employee experience. But most actually start this journey through the through the partnership between I. T. And H. R. So I. T. Is responsible for this technology architecture, the cloud strategy, the data strategy architectural framework. All those pieces that put together the foundation and the building blocks and the security that helped to um modernize this employee experience and by the way they're doing this at the same time with their modernizing their entire way in which the function operates. Um So you got I. T. That's kind of setting the stage and the foundation for what's possible. And then you have HR. Who's operating as the steward of the employee experience that those people experiences um and putting them in place in a consistent and consistent in a positive way across the entire enterprise. So things like design thinking um that puts the employee at the center of the way we um architect and create these experiences using rapid iterative design principles. With again with this with the employee at the centre making that the cultural norm across the enterprise is a really big deal. So HR is usually in the lead on making that happen. But again this is a cultural shift, not just I have a problem, you know kind of a project plan and here's here's what I'm going to execute on leadership roles. So HR is the steward of leadership and those characteristics of leaders now are changing very dramatically to be more even in a big enterprise large global enterprise entrepreneurial transparent co creation really at the core of everything. So being transplant transparent with your teams and be able to co create um you know co create for the future. So data and ai we can use data and I ai now to actually in uh predict the impact that the workforce and the cultural will have on business results, predict attrition, predict what different work workforce design scenarios will look like to the supply chain um uh predict the speed of hiring and how that will impact literally bottom line business results. So you said it right when when when you talked about shareholder value, the people is at the center of shareholder value now. So our functions need to be modernized. But it's really this partnership between HR. And I. T. That's going to be able to make it happen in a big way. >>It's interesting. I'm just thinking that Ai as well can be a canary in a coal mine when there's potential problems. And I love this transparency. That's critical co creation. So, okay, so tech is a key part of that, especially in terms of when you go from analog to digital, taking friction out of the system shows the employees that we're investing in in your experience. But it's more than that you're saying it's it's cultural and as it makes its kind of fun cultural, it takes a village. So that's that's part of what makes it so hard. How do you think about, you know the journey, where do you start and and how do you keep iterating? You're you're never done in this this world, are you? >>Yeah, that's a question uh everybody's asking now is where do I start? So as you said, this is very hard and and and it's hard. One of the reasons it's hard, it's because it revolves culture. Um it's not only about technology, they are hard. Technologies are hard in their own right. It's not just about data that's hard in its own right. But once you involve technology it makes it makes it even even harder. And of course the people aspect unless done very pro actively and meaningfully, it can be kind of a wild card right on who's gonna adopt what. So where do you start? So um the way we like to think about um giving advice to enterprises regarding where this is, we've seen this work well is to pick a business problem. So what's a business problem that if you solved you can actually make an impact not only for your people but for your people but for the enterprise. So if you could pick a business problem and and actually fix it using data using cloud using people, experiences using a cultural shift, then you'll get that. Buy in, you get the buy in that. Yes we can do this, this is this this is very doable. We can repeat it, repeat peat, repeatable over and over again and it has an impact on our culture. That's a great place to start. Okay so then you say if that's a place to start, how do we actually, there's got to be foundational things that have to be in place to make that work. So one of them is a consistency in data and the use of AI and the ability to make insights meaningful, you know that come through data and AI. And the other part that's really important once you pick your business problem is the shift in the way of working the shift so that it can impact cultural, a cultural change, a shift so that there's co creation with your people and there's transparency so each one of these business problems and the way companies pick to fix them, they won't all work. And the way you get that trust and transparency with your people is as scary as it is to share with them what you're attempting to do and share with them, how you're doing along the journey. And if it fails, okay fails, you know, pick yourself back up and start again that trust and transparency with your people. That's the way that's the way we all make this cultural impact. So, you know, kind of the none of this is to be to make sustainable change. We can all make short term change, we can do projects but to make sustainable change, the humanity aspect has to come to life in these digital transformations and that only comes to life with this cultural shift. >>Amy right? You've thought about this a lot, deep expertise in the area, really appreciate your sharing it with our audience and thanks for coming on the cube. >>Dave my pleasure. >>All right, keep it right there. But this is Dave Volonte. You're watching IBM think 2021 the virtual edition from the Cube. >>Yeah. Mhm.

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Much of the discussion around Intel's current challenges, is focused on manufacturing issues and it's ongoing market share skirmish with AMD. Of course, that's very understandable. But the core issue Intel faces is that it has lost the volume game forever. And in Silicon volume is king. As such incoming CEO Pat Gelsinger faces some difficult decisions. I mean, on the one hand he could take some logical steps to shore up the company's execution, maybe outsource a portion of its manufacturing. Make some incremental changes that would unquestionably please Wall Street and probably drive shareholder value when combined with the usual stock buybacks and dividends. On the other hand, Gelsinger could make much more dramatic moves shedding it's vertically integrated heritage and transforming Intel into a leading designer of chips for the emerging multi-trillion dollar markets that are highly fragmented and generally referred to as the edge. We believe Intel has no choice. It must create a deep partnership in our view with a semiconductor manufacturer with aspirations to manufacture on US soil and focus Intel's resources on design. Hello, everyone. And welcome to this week's Wikibon's Cube Insights powered by ETR. In this breaking analysis will put forth our prognosis for what Intel's future looks like and lay out what we think the company needs to do not only to maintain its relevance but to regain the position it once held as perhaps the most revered company in tech. Let's start by looking at some of the fundamental factors that we've been tracking and that have shaped and are shaping Intel and our thinking around Intel today. First, it's really important to point out that new CEO Gelsinger is walking into a really difficult situation. Intel's ascendancy and its dominance it was created by PC volumes. And its development of an ecosystem that the company created around the x86 instruction set. In semiconductors volume is everything. The player with the highest volumes has the lowest manufacturing costs. And the math around learning curves is very clear and it's compelling. It's based on Wright's law named after Theodore Wright T.P Wright. He was an aeronautical engineer and he discovered that for every cumulative doubling of units manufactured, costs are going to fall by a constant percentage. Now in semiconductor way for manufacturing that cost is roughly around 22% declines. And when you consider the economics of manufacturing a next generation technology, for example going from ten nanometers to seven nanometers this becomes huge. Because the cost of making seven nanometer tech for example is much higher relative to 10 nanometers. But if you can fit more circuits on a chip your wafer costs can drop by 30% or even more. Now this learning curve benefit is why volume is so important. If the time it takes to double volume is elongated then the learning curve benefit they get elongated as well and it become less competitive from a cost standpoint. And that's exactly what is happening to Intel. You see x86 PC volumes, they peaked in 2011 and that marked the beginning of the end of Intel's dominance from manufacturing and cost standpoint. You know, ironically HDD hard disk drive volumes peaked around the same time and you're seeing a similar fundamental shift in that market relative to flash. Now because Intel has a vertically integrated model it's designers are limited by the constraints in the manufacturing process. What used to be Intel's ace in the hole its process manufacturing has become a hindrance, frustrating Intel's chip designers and really seeding advantage to a number of competitors including AMD, ARM and Nvidia. Now, during this time we've seen high profile innovators adapting alternative processors companies like Apple which chose its own design based on ARM for the M1. Tesla is a fascinating case study where Intel was really not in the running. AWS probably Intel's largest customer is developing its own chips. You know through Intel, a little bone at the recent reinvent it announced its use of Intel's Habana chips in a practically the same sentence that talked about how it was developing a similar chip that would provide even better price performance. And just last month it was reported that Microsoft Intel's monopoly partner in the PC era was developing its own ARM-based chips for the surface PCs and for its servers. Intel's Zenith was marked by those peak PC volumes that we talked about. Now to stress this point this chart shows x86 PC volumes over time. That red highlighted area shows the peak years. Now, volumes actually grew in 2020 in part due to COVID which is not really reflected in this chart but the volume game was lost for Intel. When it has been widely reported that in 2005 Steve Jobs approached Intel as it was replacing IBM microprocessors with with Intel processors for the Mac and asked Intel to develop the chip for the iPhone Intel passed and the die was cast. Now to the earlier point, PC markets are actually quite good if you're Dell. Here's some ETR data that shows Dell's laptop net score. Net score is a measure of spending momentum for 2020 and into 2021. Dell's client business has been very good and profitable and frankly, it's been a pleasant surprise. You know, PCs they're doing well. And as you can see in this chart, Dell has momentum. There's approximately 275 million maybe as high as 300 million PC units shipped worldwide in 2020, you know up double digits by some estimates. However, ARM chip units shipped exceeded 20 billion units last year worldwide. And it's not apples to apples. You know, we're comparing x86 based PCs to ARM chips. So this excludes x86 servers, but the way for volume for ARM dwarfs that of x86 probably by a factor of 10 times. Back to Wright's law, how long is it going to take Intel to double wafer volumes? It's not going to happen. And trust me, Pat Gelsinger understands this dynamic probably better than anyone in the world and certainly better than I do. And as you look out to the future, the story for Intel and it's vertically integrated approach it's even tougher. This chart shows Wikibon's 2020 forecast for ARM based compared to x86 based PCs. It also includes some other devices but as you can see what happens by the end of the decade is ARM really starts to eat in to x86. As we've seen with the M1 at Apple, ARM is competing in PCs in much better position for these emerging devices that support things like video and virtual reality systems. And we think even will start to eat into the enterprise. So again, the volume game is over for Intel, period. They're never going to win it back. Well, you might ask what about revenue? Intel still dominates in the data center right? Well, yes. And that is much higher revenue per unit but we still believe that revenue from ARM-based systems are going to surpass that of x86 by the end of the decade. Arm compute revenue is shown in the orange area in this chart with x86 in the blue. This means to us that Intel's last mot is going to be its position in the data center. It has to protect that at all costs. Now the market knows this. It knows something's wrong with Intel. And you can see that is reflected in the valuations of semiconductor companies. This chart compares the trailing 12 month revenue in the market valuations for Intel, Nvidia, AMD and Qualcomm. And you can see at a trailing 12 month multiple revenue with 3 X compared to about 22 X for Nvidia about 10 X for AMT and Qualcomm, Intel is lagging behind in the street's view. And Intel, as you can see here, it's now considered a cheap stock by many, you know. Here's a graph that shows the performance over the past 12 months compared to the NASDAQ which you can see that major divergence. NASDAQ has been powered part by COVID and all the new tech and the work from home. The stock reacted very well to the appointment of Gelsinger. That's no surprise. The question people are asking is what's next for Intel? How will Pat turn the company's fortunes around? How long is it going to take? What moves can he and should he make? How will they be received by the market? And internally, very importantly, within Intel's culture. These are big chewy questions and people are split on what should be done. I've heard everything from Pat should just clean up the execution issues. It's no.. This is, you know, very workable and not make any major strategic moves all the way to Intel should do a hybrid outsourced model to Intel should aggressively move out of manufacturing. Let me read some things from Barron's and some other media. Intel has fallen behind rivals and the rest of tech Intel is replacing Bob Swan. Investors are cheering the move. Intel would likely turn to Taiwan semiconductor for chips. Here's who benefits most. So let's take a look at some of the opinions that are inside these articles. So, first one I'm going to pull out Intel has indicated a willingness to try new things and investors expect the company to announce a hybrid manufacturing approach in January. Now, if you take a look at that and you quote a CEO Swan, he says, what has changed is that we have much more flexibility in our designs. And with that type of design we have the ability to move things in and move things out. And that gives us a little more flexibility about what we will make and what we might take from the outside. So let's unpack that a little bit. The new Intel, we know is a highly vertically integrated workflow from design to manufacturing production. But to me, the designers are the artists and the flexibility you would think would come from outsourcing manufacturer to give designers more flexibility to take advantage of say seven nanometer or five nanometer process technologies versus having to wait for Intel to catch up. It used to be that Intel's process was the industry's best and it could supercharge a design or even mask certain design challenges so that Intel could maintain its edge but that's no longer the case. Here's a sentiment from an analyst, Daniel Donnelly. Donnelly is at Citi. It says he's confident. Donnelly is confident that Intel's decision to outsource more of its production won't result in the company divesting its entire manufacturing segment. And he cited three reasons. One, it would take roughly three years to bring a chip to market. And two, Intel would have to share IP. And three, it would hurt Intel's profit margins. He said it would negatively impact gross margins by 10 points and would cause a 25% decline in EPS. Now I don't know about this. I would... To that I would say one, Intel needs to reduce its current cycle time, to go from design to production from let's say three to four years where it is today. It's got to get it under you know, at least at two years maybe even less. Second, I would say is what good is intellectual property if it's not helping you win in the market? And three, I think profitability is nuance. So here's another take from a UBS analyst. His name is Timothy Arcuri. And he says, quote, We see but no option but for Intel to aggressively pursue an outsourcing strategy. He wrote that Intel could be 80% outsourced by 2026. And just by going to 50% outsourcing, he said would save the company $4 billion annually in CapEx and 25% would drop to free cashflow. So look, maybe Gelsinger has to sacrifice some gross margin in EPS for the time being. Reduce the cost of goods sold by outsourcing manufacturing lower its CapEx and fund innovation in design with free cash flow. Here's our take, Pat Gelsinger needs to look in the mirror and ask what would Andy Grove do? You know, Grove's quote that only the paranoid survive its famous less well-known are the words that proceeded that quote. Success breeds complacency and complacency breeds failure. Intel in our view is headed on a path to a long drawn out failure if it doesn't act aggressively. It simply can't compete on cost as an integrated manufacturer because it doesn't have the volume. So what will Pat Gelsinger do? You know, we've probably done 30 Cube interviews with Pat and I just don't think he's taking the job to make some incremental changes to Intel to get the stock price back up. Why would that excite Pat Gelsinger? Trends, markets, people, society, he's a dot connector and he loves Intel deeply. And he's a legend at the company. Here's what we strongly believe. We think Intel has to do a deal with TSM or maybe Samsung perhaps some kind of joint venture or other innovative structure that both protects its IP and secures its future. You know, both of these manufacturers would love to have a stronger US presence. In markets where Intel has many manufacturing facilities they may even be willing to take a loss to get this started and deeply partner with Intel for some period of time This would allow Intel to better compete on a cost basis with AMD. It would protect its core data center revenue and allow it to fight the fight in PCs with better cost structures. Maybe even gain some share that could count for, you know another $10 billion to the top line. Intel should focus on reducing its cycle times and unleashing its designers to create new solutions. Let a manufacturing partner who has the learning curve advantages enable Intel designers to innovate and extend ecosystems into new markets. Autonomous vehicles, factory floor use cases, military security, distributed cloud the coming telco explosion with 5G, AI inferencing at the edge. Bite the bullet, give up on yesterday's playbook and reinvent Intel for the next 50 years. That's what we'd like to see. And that's what we think Gelsinger will conclude when he channels his mentor. What do you think? Please comment on my LinkedIn posts. You can DM me at dvellante or email me at david.vellante@siliconangle.com. I publish weekly on wikibon.com and siliconangle.com. These episodes remember are also available as podcasts for your listening pleasure. Just search Breaking Analysis podcast. Many thanks to my friend and colleague David Floyer who contributed to this episode and that has done great work in the last better part of the last decade and has really thought through some of the cost factors that we talked about today. Also don't forget to check out etr.plus for all the survey action. Thanks for watching this episode of the Cube Insights powered by ETR. Be well. And we'll see you next time. (upbeat music)

>>Yeah, yeah, >>Hello. We're back with Today's the last session in the creating engaging analytics experiences for all track breaking down data silos. A conversation with Snowflake on Western Union Earlier today, we did a few deep dives into the thought spot product with sessions on thoughts about one. Thoughts were everywhere on spot. Take you to close out this track. We're joined by industry leading experts Christian Kleinerman s VP of product at Snowflake and Tom Matzzie, Pharaoh, chief data officer at Western Union, for a thought provoking conversation on data transformation on how to avoid the pitfalls of traditional analytics. They'll be discussing in key challenges faced by organizations, why user engagement matters and looking towards the future of the industry. No Joining Thomas and Christian in conversation is Angela Cooper, vice president of customer success at Thought spot. Thank you all for being here today. We're so excited for what is what this conversation has in store. Handing it over now to Christian to kick things off. >>Hi. So, a few years ago, when when someone asked about Snowflake, the most common answer, it was like, what is snowflake and what do you do? Hopefully in the last couple off months, things have changed and and here I am showing a couple of momentum data points on, uh, where we have accomplished here it Snowflake. So we we have received Ah, a lot of attention and buzz. Recently, we were listed in the New York Stock Exchange And we even though we still think of ourselves as a small start up company, we have crossed the 2000 employees mark. More important, we count with 3 3000 plus amazing customers. And something that we obsess about is the a satisfaction of our customers. We really are working hard. The laboring technology that having a platform for better decisions, better analytics and then the promoters course off 71 depicted here is a testament of that. And last, but certainly not least about snowflake. It's very important that we know that we succeed with our partners. We know that we don't go to market by ourselves. We actually have Ah, fantastic set of partners and of course, thoughts. But it is one of our most important partners. >>Good morning. Good afternoon. Eso Amman Thomas affair on the chief kid officer here at Western Union. It's gonna be a background of a Western union and what we, uh, what we do and how we service our customers. So today we are in over 200 countries and territories worldwide. We have a 550,000 retail Asian network to service all of our customers, uh, needs from what he transfer and picking up in a depositing cash. We also have our digital transformation underway, where we now have educate abilities up and running and over 35 countries with paled options to accounts in over 120 countries. We think about our overall business and how support are over our customers and our services. It really has transformed over the past 12 months with Cove it and it's part of that We have to be able to really accelerate our transformation on a digital front to help to enable in the super those customers going forward. Eso as part of that, You know, a big, big help in a big supporter of that transformation has been snowflake and has been thought spot as part of that transformation. If you go the next to the next slide are our current, uh B I in our illegal tools right to date, uh, have been very useful up until the last one or two years. As data explodes and as as our customer needs transform and as our solutions and our time to act in our time to react in the overall market becomes faster and faster, we need to be able to basically look across our entire company, our entire organization and cross functionally to visit to leverage data leverage our insights to really basically pivot our overall business and our overall model to support our customers and our and to enable those services and products going forward. So as part of that, snowflakes been a huge part of that journey, right, allowing us to consolidate over our 30 plus data stores across the company on able to really leverage that overall data and insights to drive, uh, quick reaction right with the pivot, our business offered to enable new services and improve customer experiences going forward and then being able to use a snowflake and then being put the applications on top of that like thought spot, which allows, uh, users that are both technical and nontechnical to the go in and just, um, ask the question as if the searching on Google or Yahoo or being they can just ask any question they want and then get the results back in real time, made that business call and then really go forward through these is this larger ecosystem as a whole. It's really enabled us to really transform our business and supporter customers going forward. >>Wonderful. Thank you, Tom. Thank you, Christian, for the overview of both snowflake and Western Union. Both have big presence in Denver, which is where Tom and I are tonight. Um, I'm here. I'm the vice president of customer success for Thought spot, and I wanted to ask both of you some questions about the industry and specific things that you're facing within Western Union. So first I was hoping Christian that you could talk to me a little bit about Snowflake has thousands of customers at this point, servicing essentially located data sets. But what are you seeing? Has been the top challenges that businesses air facing and how it snowflake uniquely positioned to help. Yeah, >>so certainly the think the challenges air made. I would say that the macro challenge above everything is how to turn data into a competitive differentiator, their study after study that says companies that embrace data and insights and analytics they are outperforming their competitors. So that would be my macro challenge. Once you go into the next level, maybe I can think of three elements. The first one Tom already perfectly teed up the topic of of silence and the reality For most organizations, data is fragmented across different database systems. Even filed systems in some instances transactional databases, analytical data bases and what customers expect is to have, ah, unified experience like I am dealing with company extra company. Why? And I really don't care if behind the scenes there's 10 different teams or 100 different systems. I just want a unified experience. And the Congress is true. The opportunity to deliver personalized custom experiences is reliant on a single view of the day. The other topic that comes to mind this is the one of data governance, Um, as data becomes more important than a reorganization, understanding the constraints and security and privacy also become critical to not only advanced data capability but do it doing so responsibly and within the norms off regulation and the last one which is something court to tow our vision. We are pioneering the concept of the data cloud and the challenge that that we're addressing there is the problem around access to data, right. You can no longer as an organization think of making decisions just on your own data. But there's lots of data collaboration, data enrichment. Maybe I wanna put my data in context. And that's what we're trying to simplify and democratize access and simplify connecting to the data that improves decisions on all three fronts. Obviously, we're obsessed. That's no bling on on tearing down the silos on delivering a solution that is very focused on data governance. And for sure, the data cloud simplifies access to data. >>Wonderful. Now, I know we we really focused on those data silos is a business challenge. But Tom, going through your digital transformation journey are there specific challenges that you faced with Western Union That thought spot and snowflake have helped you overcome? >>Yeah. So? So first off fully agree what Christian just said, right? Those are absolutely, you know, problems that we faced. And we've had overcome, um, service, any company right being able to the transforming to modernize the cloud. Um, for us, one of the biggest things is being able to not just access our information, but have it in a way that it can be consumed, right? Have it in a way that it could be understood, right? Have it in a way that we can then drive business business decision points and and be able to use that information to either fix a problem that we see or better service our customers or offer a product that we're seeing right now is a miss in the marketplace to service in a underserved community or underserved, um, customer base. Also, from our standpoint, being able toe look, um um, uh and predict in forecast what's going to happen and be able to use that information and use our insights to then be proactive and thio in either, You know, be thoughtful about how do we shift our focus, or how do we then change our strategy to take advantage of that for that forecast in that position that we're seeing into the future? >>Wonderful. I've heard from many customers you could not have predicted what was going to happen to our businesses in the year 2020 with the traditional models and especially with what did you say? 30 plus different data silos. Being able to do that type of prediction across those systems must have been very, very difficult. You also mentioned going through a digital transformation at Western Union. So can you talk to me, Tom? A little bit about kind of present day? And why? Why is it important to enable your frontline knowledge workers with the right data at the right time with the right technology? >>Yeah, so? So you're spot on, by the way. But, uh, no one predicted that that we would have a pandemic that would literally consume the entire globe right And change how consumers, um uh, use and buy services and products, or how economies would either shut down or at the reopening shut down again. And then how different interests to be impacted by this? Right. So, uh, what we learned and what we were able to pivot was being able to do exactly what you just said, right. Being able to understand what's happening the date of the right time, right then being able to with the right technology with the right capabilities, understand? what's happening. I understand. Then what should our pivot be? And how should we then go focus on that pivot to go into go and transform? I think it's e. It's more than just just the front lines. Also, our executives. It's also are back office operations, right, because as you think through this, right as customers were having issues right, go into retail locations that were closed. It end of Q one Earlier, Q two. We obviously had a a large surplus right of phone calls coming into our call centers, asking for help, asking for How can we transact better? Where can we go? Right? How do we handle the operationally? Right? As we had a massive surge onto our digital platform where we were, we had 100% increase year over year in Q one and Q two. How do we make sure that our platform the technology can scale right and still provide the right S L A's and and and and the right, um uh, support to our internal customers as well as our extra customers in the future? Eso so really interesting, though, you know, on on on the front line side, our sales staff, right? And even our front line associates with our agent locations A to retail side, you know, for us, is really around. How do we best support them? So how do we partner with them to understand? You know, when a certain certain governments or certain, uh, regions were going toe lock down, how do we support them to keep them open, right. How do we make them a essential service going forward? How do we enable them? Right, the Wright systems or technology to do things a bit differently than they have in the past to adopt right with the changing times. But, you know, I'll tell you the amount of transformation in the basement we've done this year, I think you know, has a massive and actually on Lee, you know, created a larger wave for us to actually ride into the future as we can, to base to innovate, you know, in partnership with both thought spot and with the snowflake into the future. >>Absolutely. I've seen many, many a industry analyst reports talking about how companies now in 2020 have accelerated that digital transformation movement because of current day. In current time, Christian What are you seeing with the rest of the industry and other global companies about enabling data across the globe at the right time? >>Yeah, so I can't agree more with with with with what? Tom said. And he gave some very, um, compelling and very riel use cases where the timeliness of data and and and and and at the right time concept make a big difference. Right? They aske part of our data marketplace with snowflake with deliver, for example, um, up to date low ladies information on, uh, covert 19 data sets where we're infection spiking. And what were the trends? And the use case was very, very riel. Every single company was trying to make sense of the numbers. Uh, all machine learning models were sort of like, out of whack, because no trends and no patterns may make sense anymore. And it was They need to be able to join my data and my activity with this health data set and make decisions at the right time. Imagine if if the cycle to makes all these decisions waas Ah, monthlong. You would never catch up, right? And he speaks to tow a concept that it that is, um, dear, it wasa snowflake and is the lifetime value data right? The notion of ableto act on a piece of data on an event at the right time and obviously with the slow laden see it's possible, makes a big difference. And and there is no end of example. Stomach gives her all again very compelling ones. Um, there's many others, but if you're running a marketing campaign and would you want to know five minutes later that it's not working out, you're burning your daughters? Or would you want to know the next day? Or if someone is going to give you you have a subscription based business and you're going toe, for example, have a model that predicts the turn of your customer? How useful is if you find out Hey, your customer is gonna turn, but you found out two months later. Once probably you are really toe action and change the outcome. Eyes different and and and this order to manage that I'm talking about days or months are not uncommon. Many organizations today, and that's where the topic of right technology matters. Um, I love asking questions about Do you know, an organization and customers. Do you run data, transformations and ingests at two and three in the morning? And the most common answer is yes. And then you start asking why. And usually the answer is some flavor off technology made me do it and a big part of what we're trying to do, like what we're pioneering is. How about ingesting data, transforming data enriching data when the business needs it at the right time with the right timeliness? Not when the technology had cycles. So they were Scipio available, so the importance can't be overstated. There is value in in in analyzing understanding data on time, and we provide technology and platform to any of this. >>That's such a good point. Christian. We ended up on Lee doing processes and loading in the middle of the night because that's what the technology at that time would allow. You couldn't have the concurrency. You couldn't have, um, data happening all at the same time. And so wonderful point that stuff like enables. I think another piece that's interesting that you guys a hit on is that it's important to have the same user experiencing user interface at the right time. And so what I found talking to customers. And Tom what? You and I have discussed this. When you have 30 different data sets and you have a interface that's different, you have a legacy reports system. Maybe you have excel on top of another. You have thought spot on one. You have your dashboard of choice on another, those different sources in different ways. To view that data, it can all be so disjointed. And the combination of thought spot with snowflake and all the data in one place with a centralized, unified user experience just helps users take advantage off the insights that they need right at that right moment. So kind of finishing up for our last question for today I'm interested to hear about Christian will go back to you quickly about what do you see from snowflakes? Perspective is ahead. Future facing for data and analytics. >>One of the topics you just alluded toe Angela, which is the fact that many data sets are gonna be part of the processes by which we make decisions and that that's where were the experience with thoughts but a single unified search experience for a single unified. Um automatic insects, which is what's para que does That is the future, right? I I don't think that x many years from now on, and I think that that X is a small number. Organizations are going to say I had some business activity. I collected some data. I did some analysis and I have conclusions because it always has to be okay, put it in context or look at industry trends and look at other activity that can help him make more sense about my data. The example of tracking they covert are breaking is ah, timely one. But you can always say go on, put it in context with, I don't know, maybe the GDP of the country or the adoption of a platform and things like that. So I think that's ah big trend on having multiple data sets. Contributing towards better decisions towards better product experience is for better services. And, of course, Snowflake is trying to do its part, is doing its part with vision and simplify answers today and the answer on hot spot simplifying blending the interface so that would be super useful. The other big piece, of course, is, um, Predictive Analytics people Talk machine Learning and AI, which is a little bit to buzz worthy. But it is true that we have the technology to drive predictions and and do a better job of understanding behaviors off what's supposed to happen based on understanding the best and the last one. If if if I'm allowed one. Exco What's ahead for data industry, which sounds obvious, but But we're not all the way. There is both cloud the adoption and moving to the cloud as well as the topic of multi Cloud. Increasingly, I think we we finally shifted conversations from Should I go to the cloud or not? Now it's How fast do I do it? And increasingly what we hear is I may want to take the best of the different clouds and how doe I go in and and and embrace a multi cloud reality without having to learn 100 plus different services and nuances of services on on every car and this work technologies like snowflake and thoughts about that can can support a different multiple deployment are being well received by different customs, nerve fault, >>Tom industry trends, or one thing I know. Western Union is really leading in the digital transformation and in your space, What's next for Western Union? >>Yeah, so just add on Requip Thio Christian before I dive into a Western Union use case just to your point. Christian, I really see a convergence happening between how people today work or or manage their personal life, where the applications, the user experiences and the responses are at your fingertips. Easy to use don't need to learn different tools. It's just all there, right, whether you're an android user or an apple user rights, although your fingertips I ask you the same innovation and transmission happening now on the work side, where I see to your point right a convergence happening where not just that the technology teams but even the business teams. They wanna have that same feature, that same functionality, where all their insights their entire way to interact with the business with the business teams with their data with their systems with their products for their services are at their fingertips right where they can go and they can make a change on an iPad or an iPhone and instant effect. They can go change a rule. They could go and modify Uh uh, an algorithm. They can go and look at expanding their product base, and it's just there. It's instant now. This would take time, right? Because this is going to be a transformational journey right across many different industries, but it's part of that. I really see that type of instant gratification, uh, satisfaction, that type of being able to instantly get those insights. Be able thio to really, you know, do what you do on your personal life in your work life every single day. That trend is absolutely it's actually happening. And it's kind of like tag team that into what we're doing at Western Union is exactly that we are actually transforming how our business teams, uh, in our technology teams are able to interact with our customers, interact with our products, interact with our services, interact with our data and our systems instantly. Right? Perfect example that it's that spot where they could go on typing any question they want. And they instigate an answer like that that that was unheard of a year ago, at least for our business. Right being able to to to go and put in in a new rule and and have it flow through the rules engine and have an instant customer impact that's coming right. Being able to instantly change or configure a new product or service with new fee structure and launch in 15 minutes. That's coming, right? All these new transformations about how do we actually better, uh, leverage our capabilities, our products and our services to meet those customer demands instantly. That's where I see the industry going the next couple of years. >>Wonderful. Um, excited to have both of you on the panel this afternoon. So thank you so much for joining us, Christian and Tom as just a quick wrap up. I, you know, learned quite a bit about industry trends and the problems facing companies today. And from the macro view with snowflake and thousands of customers and thought spots, customers and Western Union. The underlying theme is data unity, right? No more fragmented silos, no more fragmented user experiences, but truly bringing everything together in a governed safe way for users. Toe have trust in the data to have trust in what to answer and what insight is being put in front of them. And all of this pulled together so that businesses can make those better decisions more informed and more personalized. Consumer like experiences for your customers in modern technology stacks. So again, thank you both today for joining us, and we look forward to many more conversations in the future. Thank you >>for having me very happy to be here. >>Thank you so much. >>Thanks. >>Thank you, Angela. And thank you, Tom and Christian for sharing your stories. It was really interesting to hear how the events of this year have prompted Western Union to accelerate their digital transformation with snowflake and thought spot and just reflecting on alot sessions in this track, I love seeing how we're making the search experience even easier and even more consumer like in that first session and then moving on to the second session with our customer Hayes. It was really impressive to see how quickly they'd embedded thought spot into their own MD audit product. And then, of course, we heard about Spot Ike, which is making it easier for everybody to get to the Y faster with automated insights. So I'm afraid that wraps up the sessions in this track. We've come to an end, But remember to join us for the exciting product roadmap session coming right up. And then after that, put your questions to the speakers that you've heard in Track two in I'll meet the Experts Roundtable, creating engaging analytics experiences for all. Now all that remains is for me to say thank you for joining us. We really appreciate you taking the time. I hope it's been interesting and valuable. And if it has, we'd love to pick up with you for a 1 to 1 conversation Bye for now.

>>Yeah, >>yeah. Hello and welcome to this track to creating engaging analytics experiences for all. I'm Hannah Sinden Thought spots Omiya director of marketing on. I'm delighted to have you here today. A boy Have we got to show for you now? I might be a little bit biased as the host of this track, but in my humble opinion, you've come to a great place to start because this track is all about everything. Thought spot. We'll be talking about embedded search in a I thought spot one spot I. Q. We've got great speakers from both thoughts about andare customers as well as some cool product demos. But it's not all product talk. We'll be looking at how to leverage the tech to give your users a great experience. So first up is our thoughts about one deep dive. This session will be showing you how we've built on our already superb search experience to make it even easier for users across your company to get insight. We've got some great speakers who are going to be telling you about the cool stuff they've been working on to make it really fantastic and easy for non technical people to get the answers they need. So I'm really delighted to introduce Bob Baxley s VP of design and experience That thought spot on Vishal Kyocera Thought spots director of product management. So without further ado, I'll hand it over to Bob. Thanks, >>Hannah. It's great to be here with everybody today and really excited to be able to present to you thought spot one. We've been working on this for months and months and are super excited to share it before we get to the demo with Shawl, though, I just want to set things up a little bit to help people understand how we think about design here. A thought spot. The first thing is that we really try to think in terms of thought. Spot is a consumer grade product, terms what we wanted. Consumer grade you x for an analytics. And that means that for reference points rather than looking at other enterprise software companies, we tend to look at well known consumer brands like Google, YouTube and WhatsApp. We firmly believe that people are people, and it doesn't matter if they're using software for their own usage or thought are they're using software at work We wanted to have a great experience. The second piece that we were considering with thoughts about one is really what we call the desegregation of bundles. So instead of having all of your insights wraps strictly into dashboards, we want to allow users to get directly to individual answers. This is similar to what we saw in music. Were instead of you having to buy the entire album, of course, you could just buy individual songs. You see this in iTunes, Spotify and others course. Another key idea was really getting rid of gate keepers and curators and kind of changing people from owning the information, helping enable users to gather together the most important and interesting insights So you can follow curator rather than feeling like you're limited in the types of information you can get. And finally, we wanted to make search the primary way, for people are thinking about thought spot. As you'll see, we've extended search from beyond simply searching for your data toe, also searching to be able to find pin boards and answers that have been created by other people. So with that, I'll turn it over to my good friend Rachel Thio introduce more of thought, spot one and to show you a demo of the product. >>Thank you, Bob. It's a pleasure to be here to Hello, everyone. My name is Michelle and Andy, product management for Search. And I'm really, really excited to be here talking about thoughts about one our Consumer analytics experience in the Cloud. Now, for my part of the talk, we're gonna first to a high level overview of thoughts about one. Then we're going to dive into a demo, and then we're gonna close with just a few thoughts about what's coming next. So, without any today, let's get started now at thought spot. Our mission is to empower every user regardless of their expertise, to easily engage with data on make better data driven decisions. We want every user, the nurse, the neighborhood barista, the teacher, the sales person, everyone to be able to do their jobs better by using data now with thoughts about one. We've made it even more intuitive for all these business users to easily connect with the insights that are most relevant for them, and we've made it even easier for analysts to do their jobs more effectively and more efficiently. So what does thoughts about one have? There's a lot off cool new features, but they all fall into three main categories. The first main category is enhanced search capabilities. The second is a brand new homepage that's built entirely for you, and the third is powerful tools for the analysts that make them completely self service and boost their productivity. So let's see how these work Thought Spot is the pioneer for search driven analytics. We invented search so that business users can ask questions of data and create new insights. But over the years we realized that there was one key piece off functionality that was missing from our search, and that was the ability to discover insights and content that had already been created. So to clarify, our search did allow users to create new content, but we until now did not have the ability to search existing content. Now, why does that matter? Let's take an example. I am a product manager and I am always in thought spot, asking questions to better understand how are users are using the product so we can improve it now. Like me, A lot of my colleagues are doing the same thing. Ah, lot of questions that I asked have already been answered either completely are almost completely by many of my colleagues, but until now there's been no easy way for me to benefit from their work. And so I end up recreating insights that already exists, leading to redundant work that is not good for the productivity off the organization. In addition, even though our search technology is really intuitive, it does require a little bit of familiarity with the underlying data. You do need to know what metric you care about and what grouping you care about so that you can articulate your questions and create new insights. Now, if I consider in New employees product manager who joins Hotspot today and wants to ask questions, then the first time they use thought spot, they may not have that data familiarity. So we went back to the drawing board and asked ourselves, Well, how can we augment our search so that we get rid off or reduced the redundant work that I described? And in addition, empower users, even new users with very little expertise, maybe with no data familiarity, to succeed in getting answers to their questions the first time they used Hot Spot, and we're really proud and excited to announce search answers. Search answers allows users to search across existing content to get answers to their questions, and its a great compliment to search data, which allows them to search the underlying data directly to create new content. Now, with search answers were shipping in number of cool features like Answer Explainer, Personalized search Results, Answer Explorer, etcetera that make it really intuitive and powerful. And we'll see how all of these work in action in the demo. Our brand new homepage makes it easier than ever for all these business users to connect with the insights that are most relevant to them. These insights could be insights that these users already know about and want to track regularly. For example, as you can see, the monitor section at the top center of the screen thes air, the KP eyes that I may care most about, and I may want to look at them every day, and I can see them every day right here on my home page. By the way, there's a monitoring these metrics in the bankrupt these insights that I want to connect with could also be insights that I want to know more about the search experience that I just spoke about ISS seamlessly integrated into the home page. So right here from the home page, I can fire my searchers and ask whatever questions I want. Finally, and most interestingly, the homepage also allows me to connect with insights that I should know about, even if I didn't explicitly ask for them. So what's an example? If you look at the panel on the right, I can discover insights that are trending in my organization. If I look at the panel on the left, I can discover insights based on my social graph based on the people that I'm following. Now you might wonder, How do we create this personalized home page? Well, our brand new, personalized on boarding experience makes it a piece of cake as a new business user. The very first time I log into thought spot, I pay three people I want to follow and three metrics that I want to follow, and I picked these from a pool of suggestions that Ai has generated. And just like that, the new home page gets created. And let's not forget about analysts. We have a personalized on boarding experience specifically for analysts that's optimized for their needs. Now, speaking of analysts, I do want to talk about the tools that I spoke off earlier that made the analysts completely self service and greatly boost their productivity's. We want analysts to go from zero to search in less than 30 minutes, and with our with our new augmented data modeling features and thoughts about one, they can do just that. They get a guided experience where they can connect, model and visualize their data. With just a few clicks, our AI engine takes care off a number of tasks, including figuring out joints and, you know, cleaning up column names. In fact, our AI engine also helps them create a number of answers to get started quickly so that these analysts can spend their time and energy on what matters most answering the most complicated and challenging and impactful questions for the business. So I spoke about a number of different capabilities off thoughts about one, but let's not forget that they are all packaged in a delightful user experience designed by Bob and his team, and it powers really, really intuitive and powerful user flows, from personalized on boarding to searching to discover insights that already exist on that are ranked based on personalized algorithms to making refinements to these insights with a assistance to searching, to create brand new insights from scratch. And finally sharing all the insights that you find interesting with your colleagues so that it drives conversations, decisions and, most importantly, actions so that your business can improve. With that said, let's drive right into the demo for this demo. We're going to use sales data set for a company that runs a chain off retail stores selling apparel. Our user is a business user. Her name is Charlotte. She's a merchandiser, She's new to this company, and she is going to be leading the genes broader category. She's really excited about job. She wants to use data to make better decisions, so she comes to thought spot, and this is what she sees. There are three main sections on the home page that she comes to. The central section allows you to browse through items that she has access to and filter them in various ways. Based for example, on author or on tags or based on what she has favorited. The second section is this panel on the right hand side, which allows her to discover insights that are trending within her company. This is based on what other people within her company are viewing and also personalized to her. Finally, there's this search box that seamlessly integrated into the home page. Now Charlotte is really curious to learn how the business is doing. She wants to learn more about sales for the business, so she goes to the search box and searches for sales, and you can see that she's taken to a page with search results. Charlotte start scanning the search results, and she sees the first result is very relevant. It shows her what the quarterly results were for the last year, but the result that really catches her attention is regional sales. She'd love to better understand how sales are broken down by regions. Now she's interested in the search result, but she doesn't yet want to commit to clicking on it and going to that result. She wants to learn more about this result before she does that, and she could do that very easily simply by clicking anywhere on the search result card. Doing that reveals our answer. Explain our technology and you can see this information panel on the right side. It shows more details about the search results that she selected, and it also gives her an easy to understand explanation off the data that it contains. You can see that it tells her that the metrics sales it's grouped by region and splitter on last year. She can also click on this preview button to see a preview off the chart that she would see if she went to that result. It shows her that region is going to be on the X axis and sales on the Y axis. All of this seems interesting to her, and she wants to learn more. So she clicks on this result, and she's brought to this chart now. This contains the most up to date data, and she can interact with this data. Now, as she's looking at this data, she learns that Midwest is the region with the highest sales, and it has a little over $23 million in sales, and South is the region with the lowest sales, and it has about $4.24 million in sales. Now, as Charlotte is looking at this chart, she's reminded off a conversation she had with Suresh, another new hire at the company who she met at orientation just that morning. Suresh is responsible for leading a few different product categories for the Western region off the business, and she thinks that he would find this chart really useful Now she can share this chart with Suresh really easily from right here by clicking the share button. As Charlotte continues to look at this chart and understand the data, she thinks, uh, that would be great for her to understand. How do these sales numbers across regions look for just the genes product category, since that's the product category that she is going to be leading? And she can easily narrow this data to just the genes category by using her answer Explorer technology. This panel on the right hand side allows her to make the necessary refinements. Now she can do that simply by typing in the search box, or she can pick from one off the AI generated suggestions that are personalized for her now. In this case, the AI has already suggested genes as a prototype for her. So with just a single click, she can narrow the data to show sales data for just jeans broken down by region. And she can see that Midwest is still the region with the highest sales for jeans, with $1.35 million in sales. Now let's spend a minute thinking about what we just saw. This is the first time that Charlotte is using Thought spot. She does not know anything about the data sources. She doesn't know anything about measures or attributes. She doesn't know the names of the columns. And yet she could get to insights that are relevant for her really easily using a search interface that's very much like Google. And as she started interacting with search results, she started building a slightly better understanding off the underlying data. When she found an insight that she thought would be useful to a colleague offers, it was really seamless for her to share it with that colleague from where she Waas. Also, even though she's searching over content that has already been created by her colleagues in search answers. She was in no way restricted to exactly that data as we just saw. She could refine the data in an insight that she found by narrowing it. And there's other things you can do so she could interact with the data for the inside that she finds using search answers. Let's take a slightly more complex question that Charlotte may have. Let's assume she wanted to learn about sales broken down by, um, by category so that she can compare her vertical, which is jeans toe other verticals within the company. Again, she can see that the very first result that she gets is very relevant. It shows her search Sorry, sales by category for last year. But what really catches her attention about this result is the name of the author. She's thrilled to note that John, who is the author of this result, was also an instructor for one off for orientation sessions and clearly someone who has a lot of insight into the sales data at this company. Now she would love to see mawr results by John, and to do that, all she has to do is to click on his name now all of the search results are only those that have been authored by John. In fact, this whole panel at the top of the results allow her to filter her search results or sort them in different ways. By clicking on these authors filter, she can discover other authors who are reputed for the topic that she's searching for. She can also filter by tags, and she can sort these results in different ways. This whole experience off doing a search and then filtering search results easily is similar to how we use e commerce search engines in the consumer world. For example, Amazon, where you may search for a product and then filter by price range or filter by brand. For example, Let's also spend a minute talking about how do we determine relevance for these results and how they're ranked. Um, when considering relevance for these results, we consider three main categories of things. We want to first make sure that the result is in fact relevant to the question that the user is asking, and for that we look at various fields within the result. We look at the title, the author, the description, but also the technical query underpinning that result. We also want to make sure that the results are trustworthy, because we want users to be able to make business decisions based on the results that they find. And for that we look at a number of signals as well. For example, how popular that result is is one of those signals. And finally, we want to make sure the results are relevant to the users themselves. So we look at signals to personalize the result for that user. So those are all the different categories of signals that we used to determine overall ranking for a search result. You may be wondering what happens if if Charlotte asks a question for which nobody has created any answer, so no answers exist. Let's say she wants to know what the total sales of genes for last year and no one's created that well. It's really easy for her to switch from searching for answers, which is searching for content that has already been created to searching the data directly so she can create a new insight from scratch. Let's see how that works. She could just click here, and now she's in the search data in her face and for the question that I just talked about. She can just type genes sales last year. And just like that, she could get an answer to her question. The total sales for jeans last year were almost $4.6 million. As you can see, the two modes off search searching for answers and searching, the data are complementary, and it's really easy to switch from one to the other. Now we understand that some business users may not be motivated to create their own insights from scratch. Or sometimes some of these business users may have questions that are too complicated, and so they may struggle to create their own inside from scratch. Now what happens usually in these circumstances is that these users will open a ticket, which would go to the analyst team. The analyst team is usually overrun with these tickets and have trouble prioritizing them. And so we started thinking, How can we make that entire feedback loop really efficient so that analysts can have a massive impact with as little work as possible? Let me show you what we came up with. Search answers comes with this system generated dashboard that analysts can see to see analytics on the queries that business users are asking in search answers so it contains high level K P. I is like, You know how many searches there are and how many users there are. It also contains one of the most popular queries that users are asking. But most importantly, it contains information about what are popular queries where users are failing. So the number on the top right tells you that about 10% off queries in this case ended with no results. So the user clearly failed because there were no results on the table. Right below it shows you here are the top search queries for original results exist. So, for example, the highlighted row there says jean sales with the number three, which tells the analysts that last week there were three searches for the query jean sales and the resulted in no results on search answers. Now, when an analyst sees a report like this, they can use it to prioritize what kind of content they could be creating or optimizing. Now, in addition to giving them inside into queries which led to no results or zero results. This dashboard also contains reports on creatives that lead to poor results because the user did get some results but didn't click on anything, meaning that they didn't get the answer that they were looking for. Taking all these insights, analysts can better prioritize and either create or optimize their content to have maximum impact for their business users with the least amount of for. So that was the demo. As you can see with search answers, we've created a very consumer search interface that any business user can use to get the answers to their questions by leveraging data or answers that have already been created in the system by other users in their organization. In addition, we're creating tools that allow analysts toe create or optimized content that can have the highest impact for these business users. All right, so that was the demo or thoughts about one and hope you guys liked it. We're really excited about it. Now Let me just spend a minute talking about what's coming next. As I've mentioned before, we want to connect every business user with the insights that are most relevant for them, and for that we will continue to invest in Advanced AI and personalization, and some of the ways you will see it is improved relevance in ranking in recommendations in how we understand your questions across the product within search within the home page everywhere. The second team that will continue to invest in is powerful analyst tools. We talked about tools and, I assure you, tools that make the analysts more self service. We are committed to improving the analyst experience so that they can make the most off their time. An example of a tool that we're really excited about is one that allows them to bridge the vocabulary difference that this even business user asks questions. A user asked a question like revenue, but the column name for the metric in the data set its sales. Now analysts can get insights into what are the words that users air using in their questions that aren't matching anything in the data set and easily create synonyms so that that vocabulary difference gets breached. But that's just one example of how we're thinking about empowering the analysts so that with minimal work, they can amplify their impact and help their business users succeed. So there's a lot coming, and we're really excited about how we're planning to evolve thoughts about one. With all that said, Um, there's just, well, one more thing that my friend Bob wants to talk to you guys about. So back to you, Bob. >>Thanks, Michelle. It's such a great demo and so fun to see all the new work that's going on with thought. Spot one. All the happenings for the new features coming out that will be under the hood. But of course, on the design side, we're going to continue to evolve the front end as well, and this is what we're hoping to move towards. So here you'll see a new log in screen and then the new homepage. So compared to the material that you saw just a few minutes ago, you'll notice this look is much lighter. A little bit nicer use of color up in the top bar with search the features over here to allow you to switch between searching against answers at versus creating new answers, the settings and user profile controls down here and then on the search results page itself also lighter look and feel again. Mork color up in the search bar up the top. A little bit nicer treatments here. We'll continue to evolve the look and feel the product in coming months and quarters and look forward to continue to constantly improving thoughts about one Hannah back to you. >>Thanks, Bob, and thank you both for showing us the next generation of thought spot. I'd love to go a bit deeper on some of the points you touched on there. I've got a couple of questions here. Bob, how do you think about designing for consumer experience versus designing for enterprise solutions? >>Yes, I mentioned Hannah. We don't >>really try to distinguish so much between enterprise users and consumer users. It's really kind of two different context of use. But we still always think that users want some product and feature and experience that's easy to use and makes sense to them. So instead of trying to think about those is two completely different design processes I think about it may be the way Frank Lloyd Wright would approached architecture. >>Er I >>mean, in his career, he fluidly moved between residential architecture like falling water and the Robie House. But he also designed marquis buildings like the Johnson wax building. In each case, he simply looked at the requirements, thought about what was necessary for those users and designed accordingly. And that's really what we do. A thought spot. We spend time talking to customers. We spend time talking to users, and we spent a lot of time thinking through the problem and trying to solve it holistically. And it's simply a possible >>thanks, Bob. That's a beautiful analogy on one last question for you. Bischel. How frequently will you be adding features to this new experience, >>But I'm glad you asked that, Hannah, because this is something that we are really really excited about with thoughts about one being in the cloud. We want to go really, really fast. So we expect to eventually get to releasing new innovations every day. We expect that in the near future, we'll get to, you know, every month and every week, and we hope to get to everyday eventually fingers crossed on housing. That can happen. Great. Thanks, >>Michelle. And thank you, Bob. I'm so glad you could all join us this morning to hear more about thoughts about one. Stay close and get ready for the next session. which will be beginning in a few minutes. In it will be introduced to thoughts for >>everywhere are >>embedded analytics product on. We'll be hearing directly from our customers at Hayes about how they're using embedded analytics to help healthcare providers across billing compliance on revenue integrity functions. To make more informed decisions on make effective actions to avoid risk and maximize revenue. See you there.

(gentle music) >> Narrator: From around the globe, it's theCUBE with digital coverage of AnsibleFest 2020 brought to you by Red Hat. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. Welcome back to our continuous coverage of AnsibleFest 2020. We're not in person this year, as everybody knows, but we're back covering the event. We're excited to be here and really our next guest we've had him on a lot of times. He's super insightful coming right off the keynote, driving into some really interesting topics that we're excited to get into. It's Chris Wright, he's the Chief Technology Officer of Red Hat Chris, great to see you. >> Hey, great to see you. Thanks for having me on. >> Absolutely. So let's jump into it. I mean, you covered so many topics in your keynote. The first one though, that just jumps off the page, right, is automation and really rethinking automation. You know, and I remember talking to a product manager at a hyperscaler many months ago, and he talked about the process of them mapping out their growth and trying to figure out how are they going to support it in their own data center. And he just basically figured out, we cannot do this at scale without automation. So I think the hyperscaler has been doing it, but really it's kind of a new approach for enterprises to incorporate new and more automation in what they do every day. >> It's a fundamental part of scaling. And I think we've learned over time that one we need programming interfaces on everything. So that's a critical part of beginning of the automation journey. So now you have a programmatic way to interact with all the things out there. But the other piece is just creating really confidence in knowing that when you're automating and you're taking tasks away from humans which are actually error prone and typing on the keyboard is not always the greatest way to get things done. The confidence that those automation scripts or playbooks are going to do the right things at the right time. And so, creating really a business and a mindset around infusing automation everything you do is a pretty big journey for the enterprise >> Right. And that's one of the topics you talked about as well. And you know it comes up all the time with digital transformation or software development. This kind of shift the focus from, you know, kind of it's a destination to it's a journey. And you talked very specifically that you need to think about automation as a journey and as a process and even a language, and really bake it into as many processes as you possibly can. I'm sure that shocks a lot of people and probably scares them but really that's the only way to achieve these types of scales that we're seeing out there. >> Well, I think so. And part of what I was trying to highlight is the notion that a business is filled with people with domain expertise. So everybody brings something to the table. You're a business analyst, you understand the business part of what you're providing. You're the technologist. You really understand the technology. There's a partner ecosystem coming in with a critical parts of the technology stack. And when you want to bring this all together, you need to have a common way to communicate. And the... What I was really trying to point out is a language for communication across all those different cross functional parts of your business is critical. Number one, and number two, that language can actually be an automation language. And so, choosing that language wisely obviously we're talking to AnsibleFest. So we're going to be talking a lot about Ansible in this context. Treating that language wisely is part of how you build the end to end sort of internalized view of what automation means to your business. >> Right. I wrote down a bunch of quotes that you talked about, you know, Ansible is the language of automation, and automation should be a primary communication language. Again, very different kind of language that we don't hear. Now, it's more than a tool but a process a constant process and should be an embedded component of any organization. So, I mean, you're really talking about automation as a first class citizen, not kind of this last thing for the most advanced or potentially last thing for the most simple things where we can apply this process, but really needs to be a fundamental core of the way you think about everything that you do. Really a very different way to think about things and probably really appropriate, you know, as we come out of 2020 in this kind of new world where, you know, everyone like distributed teams, well now you have distributed teams. And so, you know, the forcing function on better tooling, that's really wrapped in better culture has never been greater than we're seeing today. >> I completely agree with that. And that domain expertise, I think we understand well in certain areas. So for example, application developers, they rely on one another. So, you maybe as an application developer consuming a service from somebody else in your microservices architecture, and so you're dependent on that other engineering team's domain expertise. Maybe that's even the database service, and you're not a database DBA or an engineer that really builds schemas for databases. So we kind of get that notion of encapsulating domain expertise in the building and delivering about applications that notion the CICD pipeline, which itself is automating how you build and deliver applications, that notion of encapsulating domain expertise across a series of different functions in your business can go much broader than just building and delivering the application. It's running your business. And that's where it becomes fundamental. It becomes a process. That's the journey, you know, not the end state, but it's the... And it's not the destination, it's the journey that matters. And I've seen some really interesting ways that people actually work on this and try to approach it from the, "How do you change your mindset?" Here's one example that I thought was really unique. I was speaking with a customer who quite literally automated their existing process, and what they did was automate everything from generating the emails to the PDFs, which would then be shared as basically printed out documents for how they walked through business change when they're making a change in their product. And the reason they did that was not because that was the most efficient model at all. It was... That was the way they could get the teams comfortable with automation. If it produced the same artifacts that they were already used to, then it created confidence and then they could sort of evolve the model to streamline it because printing out a piece of paper to review it is not going to be the efficient way to (indistinct) change your business. >> Well, just to follow up on that, right? Cause I think what would probably scares a lot of people about automation, one is exception handling and can you get all the Edge cases in the use cases? So in the one you just talked about, how do they deal with that? And then I think the other one is just simply control. Do I feel confident enough that I can get the automation to a place that I'm comfortable to hand over control? And I'm just curious in that case you just outlined how do they deal with kind of those two factors? >> Well, they always enabled a human checkpoint, so especially in the beginning. So it was sort of trust but verify that model and over time you can look at the things that you really understand well and start with those and the things that have more kind of gray zones, where the exceptions may be the rule or maybe the critical part of the decision making process. Those can be sort of flagged as needs real kind of human intervention. And that's a way to sort of evolve and iterate and not start off with the notion that everything has to be automated. You can do it piecemeal and grow over time and you'll build confidence and you'll understand how to flag those exceptions, where you actually need to change your process itself because you may have bottlenecks that don't really make sense for the business anymore and where you can incorporate the exception handling into the automation essentially. >> Right, that's great. Thank you for sharing that example. I want to shift gears a little bit cause another big topic that you covered in your keynote that we talk about all the time on theCUBE is Edge. So everybody knows what a data center is, everybody knows what a public cloud is, you know lots of conversations around hybrid cloud and multicloud, et cetera, et cetera, et cetera. But this new thing is Edge and I think people talk about Edge in kind of an esoteric way, but I think you just nailed it. I mean you just nailed it very simply, moving the compute to where the data is collected and or consumed. You know I thought that was super elegant, but what you didn't get into on all the complexity is what means. I mean data centers are our pristine environments that they're very, very controlled, the environment's controlled, the network is controlled, the security is controlled and you have the vision of an Edge device and the one everyone always likes to use let's say like a wind farm. Those things are out in crazy harsh conditions and then there's still this balancing act as to what information does get stored and processed and used and then what does have to go back to the data center because it's not a substitute for the data center it's really an extension of the data center or maybe the data center is actually an extension of the Edge. Maybe that's a better way to think of it but we've had all these devices out there, now suddenly we're connecting them and bringing them into a network and add a control. And I just thought the Edge represents such a big shift in the way we're going to see compute change probably as fundamental I would imagine as the cloud shift has been. >> I believe it is, I absolutely believe it's as big a change in the industry as the cloud has been. The cloud really created scale, it created automation, programmatic interfaces to infrastructure and higher level services. But it also was built around a premise of centralization. I mean clouds themselves are distributed and so you can create availability zones and resilient applications, but there's still a sense of centralization. Edge is really embracing the notion that data production is kind of only up into the right and the way to scale processing that data and turning that data into insights and information that's valuable for our business is to bring compute closer to data. Not really a new concept, but the scale at which it's happening is what's really changing how we think about building infrastructure and building the support behind all that processing and it's that scale that requires automation. Because you're just not going to be able to manage thousands or tens of thousands or in certain scenarios even millions of devices without putting automation at the forefront. It's critical. >> Right. And we can't talk about Edge without talking about 5G and I laugh every time I'm watching football on Sundays and they have the 5G commercials on talking about my handset that I can order my food to get delivered faster at my house like completely missing the point. 5G is about machine to machine communication and the scale and the speed and the volume of machine to machine is so fundamentally different than humans talking voice to voice. And that's really this big drivers to instrument as you said, all these machines, all these devices there's already been sensors on them forever but now the ability to actually connect them and pull them into this network and start to use the data and control the machines is a huge shift in the way things are going to happen going forward. >> A couple of things that are important in there. Number one, that data production and sensors and bringing computer closer to data, what that represents is bringing the digital world and the physical world closer together. We'll experience that at a personal level with how we communicate we're already distributed in today's environment and the ways we can augment our human connections through a digital medium are really going to be important to how we maintain our human connections. And then on the enterprise side, we're building this infrastructure in 5G that when you think about it from a consumer point of view and ordering your pizza faster it really isn't the right way to think about it. Couple of key characteristics of 5G. Greater bandwidth, so you can just push more package to the network. Lower latency, so you're closer to the data and higher connection density and more reliable connections. And that kind of combination of characteristics make it really valuable for enterprise businesses. You can bring your data and compute close together you have these highly reliable and dense connections that allow for device proliferation and that's the piece that's really changing where the world's going. I like to think of it in a really simple way which is, 4G and the cloud and the smartphone created a world that today we take for granted, 10 years ago we really couldn't imagine what it looked like. 5G, device proliferation and Edge computing today is building the footprint for what we can't really imagine what we will be taking for granted in 10 years from now. So we're at this great kind of change inflection point in the industry. >> I have to always take a moment to call out (indistinct). I think it's the most underappreciated law and it's been stolen by other people and repackage many ways, but it's basically we overestimate the impact of these things in the short term and we way, way, way, way, kind of underestimate the impact in the longterm and I think your story in they keynote about once we had digital phones and smartphones, we don't even think twice about looking at a map and where are we and where is a store close buy-in are they open and is there a review? I mean the infrastructure to put that together kind of an API based economy which is pulling together all these bits and pieces the stupid relay expectation of performance and how fast that information is going to be delivered to me. I think we still take it for granted, as you said I think it's like magic and we never thought of all the different applications of these interconnected apps enabled by and always on device that's always connected and knows where we are it's a huge change. And as you say that when we think about 5G, 10 years from now, oh my goodness, where are we going to be? >> It's hard to imagine? It really is hard to imagine and I think that's okay. And what we're doing today is introducing everything that we need to help businesses evolve, take advantage of that and that scale of the Edge is a fundamental characteristic of the Edge. And so automating to manage that scale is the only way you're going to be successful and extending what we've learned in the data center, how to the Edge using the same tools, the things we already understand really is a great way to evolve a business. And that's where that common language and the discussions that I was trying to generate around Ansible as a great tool, but it's not just the tool, it's the whole process, the mindset. The culture changed the way you change how you operate your business that's going to allow us to take advantage of the future where my clothes are full of sensors and you can look through a video camera and tell immediately that I'm happy with this conversation. That's a very different kind of augmented reality than we have today and maybe it's a bad example but it's hard to imagine really what it will be like. >> So, Chris, I just want to close on a slight shift. We've been talking a lot about technology, but you talk about culture all the time and really it's about the people and I think a number of times in the keynote you reinforced this is about people and culture. And I just had InaMarie Johnson on the Chief Diversity Officer from Zendesk and she said culture eats strategy for breakfast. Great line. So I wonder if you can talk about the culture because it's very different and you've seen it in opensource from Red Hat for a long time really a shifting culture around opensource the shifting culture around DevOps and continuous delivery and change is a good thing, not a bad thing and we want to be able to change our code frequently and push out new features. So again, as you think of automation and culture, what kind of comes to mind and what should people be thinking about when they think about the people and less about the technology? >> Well, there's a couple of things. Some I'll reinforce what we already touched on which is the notion of creating confidence in the automation. So there's an element of trust associated with that and that's more maybe trusting the technology. So when you're automating something you've already got a process, you already understand how something works, it's turning that something into an automated script or playbook in the Ansible context and trusting that it's going to do the right thing. There's another important part of trust which is getting more to the people part. And I've learned this a lot from open source communities collaboration and communities are fundamentally built around trust and human trust relationships. And the change in process, trusting not only that the tools are going to the right job but that people are really assuming good intent and working with or trying to build for the right outcomes for your business. I think that's a really important part of the overall picture. And then finally that trust is extended to knowing that that change for the business isn't going to compromise your job. So thinking differently about what is your job? Is your job to do the repetitive task or is your job to free up your time from that repetitive task to think more creatively about value you can bring to the business. And that's where I think it's really challenging for organizations to make changes because you build a personal identity around the jobs that you do and making changes to those personal identities really gets to the core of who you are as a person. And that's why I think it's so complicated. The tools almost start the easy part, it's the process changes and the cultural changes, the mindset changes behind that which is difficult but more powerful in the end. >> Well, I think people process tools the tech is always the easy part relative to culture and people in changing the way people do things and as you said, who their identity is, how they get kind of wrapped into what they do and what they think their value is and who they are. So to free them up from that that's a really important point. Well, Chris, I always love having you on, thank you for coming on again, sharing your insight, great keynote. And give your the last word about AnsibleFest 2020. What are you looking forward to take away from this little show? >> Well, number one, my personal hope is that the conversation that I was trying to sort of ignite through the keynote is an opportunity for the community to see where Ansible fits in the Edge and automation and helping really the industry at large scale. And that key part of bringing a common language to help change how we communicate internally is the message I was hoping to impart on the AnsibleFest Community. And so hopefully we can take that broader and appreciate the time here to really amplify some of those messages. >> All right, great. Well, thanks a lot Chris and have a great day. >> Thanks Jeff, thank you. >> All right. He's Chris, I'm Jeff you're watching theCUBE and our ongoing coverage of AnsibleFest 2020. Thanks for watching we'll see you next time. (gentle music)

>> Narrator: From around the globe, it's theCube. With digital coverage of AnsibleFest 2020. Brought to you by Red Hat. (twinkly music) >> Hey, welcome back, everybody. Jeff Frick here with theCube. Welcome back to our continuous coverage of AnsibleFest 2020. We're not in-person this year, as everybody knows, but we're back covering the event. We're excited to be here, and really our next guest... We've had him on a lot of times. He's super insightful. Coming right off the keynote, diving into some really interesting topics that we're excited to get into, and it's Chris Wright. He's the chief technology officer of Red Hat. Chris, great to see you. >> Hey, great to see you. Thanks for having me on. >> Absolutely. So let's jump into it. I mean, you covered so many topics in your keynote. The first one though, that just jumps off the page, right, is automation, and really rethinking automation. And I remember talking to a product manager at a hyperscaler many moons ago, and he talked about the process of them mapping out their growth and trying to figure out how they were going to support it in their own data center. And he just basically figured out we cannot do this at scale without automation. So I think the hyperscalers have been doing it, but really it's kind of a new approach for enterprises to incorporate new, and more, automation into what they do every day. >> It's a fundamental part of scaling, and I think we've learned over time that, one, we need programming interfaces on everything. So that's a critical part of beginning of the automation journey, so now you have a programmatic way to interact with all the things out there. But the other piece is just creating, really, confidence in knowing that when you're automating and you're taking tasks away from humans, which are actually error-prone, and typing on a keyboard is not always the greatest way to get things done, the confidence that those automation scripts, or playbooks, are going to do the right things at the right time. And so creating, really, a business and a mindset around infusing automation into everything you do is a pretty big journey for the enterprise. >> Right. And that's one of the topics you talked about as well, and it comes up all the time with digital transformation or software development; this kind of shift the focus from kind of it's a destination to it's a journey. And you talked very specifically that you need to think about automation as a journey, and as a process, and even a language, and really bake it into as many processes as you possibly can. I'm sure that shocks a lot of people and probably scares them, but really that's the only way to achieve the types of scales that we're seeing out there. >> Well, I think so. And part of what I was trying to highlight is the notion that a business is filled with people with domain expertise. So everybody brings something to the table. You're a business analyst. You understand the business part of what you're providing. You're the technologist. You really understand the technology. There's a partner ecosystem coming in with a critical parts of the technology stack. When you want to bring this all together, you need to have a common way to communicate. What I was really trying to point out is a language for communication across all those different cross-functional parts of your business is critical, number one, and number two, that language can actually be an automation language. And so choosing that language wisely... Obviously, we're talking at AnsibleFest, so we're going to be talking a lot about Ansible in this context. Choosing that language wisely is part of how you build the end-to-end sort of internalized view of what automation means to your business. >> Right. I mean, I wrote down a bunch of quotes that you talked about. "Ansible is the language of automation, and automation should be a primary communication language." Again, very different kind of language that we don't hear. And that it's "more than a tool, but a process, a constant process, and should be an embedded component of any organization." So I mean, you're really talking about automation as a first class citizen, not kind of this last thing for the most advanced, or potentially last thing for the most simple things where we can apply this process, but really needs to be a fundamental core of the way you think about everything that you do. Really a very different way to think about things, and probably really appropriate as we come out of 2020 in this kind of new world where everyone liked distributed teams. Well, now you have distributed teams, and so the forcing function on better tooling that's really wrapped in better culture has never been greater than we're seeing today. >> I completely agree with that. That domain expertise I think we understand well in certain areas. So for example, application developers, they rely on one another. So you're, maybe as an application developer, consuming a service from somebody else in your microservices architecture, and so you're dependent on that other engineering team's domain expertise. Maybe that's even the database service, and you're not a database, a DBA, or an engineer that really builds schemas for databases. We kind of get that notion of encapsulating domain expertise in the building and delivering of applications. That notion, the CI/CD pipeline, which itself is automating how you build and deliver applications, that notion of encapsulating domain expertise across a series of different functions in your business can go much broader than just building and delivering the application. It's running your business. And that's where it becomes fundamental. It becomes a process that's the journey. Not the end state. And it's not the destination. It's the journey that matters. And I've seen some really interesting ways that people actually work on this and try to approach it from the "how do you change your mindset?" Here's one example that I thought was really unique. I was speaking with a customer who quite literally automated their existing process, and what they did was automate everything from generating the emails to the PDFs, which would then be shared as basically printed out documents for how they walked through business change when they're making a change in their product. And the reason they did that was not because that was the most efficient model at all. It was that was the way they could get the teams comfortable with automation. If it produced the same artifacts that they were already used to, then it created confidence, and then they could sort of evolve the model to streamline it, because printing out a piece of paper to review, it is not going to be the efficient way to make changes in your business. >> Well, just to follow up on that, right, cause I think what probably scares a lot of people about automation... One is exception handling, right? And can you get all the edge cases in the use case. So in the one you just talked about, how do they deal with that? And then I think the other one is just simply control. Do I feel confident enough that I can get the automation to a place that I'm comfortable to hand over control? And I'm just curious, in that case you just outlined, how do they deal with kind of those two factors? >> Well, they always enabled a human checkpoint. Especially in the beginning. So it was sort of "trust but verify" that model, and over time you can look at the things that you really understand well and start with those, and the things that have more kind of gray zones, where the exceptions may be the rule, or may be the critical part of the decision making process, those can be sort of flagged as "needs real kind of human intervention," and that's a way to sort of evolve, and iterate, and not start off with the notion that everything has to be automated. You can do it piecemeal and grow over time, and you'll build confidence, and you'll understand where... How to flag those exceptions, where you actually need to change your process itself, because you may have bottlenecks that don't really make sense for the business anymore, and where you can incorporate the exception handling into the automation, essentially. >> Right. That's great. Thank you for sharing that example. I want to shift gears a little bit, cause another big topic that you covered in your keynote that we talk about all the time on theCube is edge, right? So everybody knows what a data center is. Everybody knows what a public cloud is. Lots of conversations around hybrid cloud and multi cloud, et cetera, et cetera, et cetera... But this new thing is edge, and I think people talk about edge in kind of an esoteric way, but I think you just nailed it. I mean, you just nailed it. It's very simply moving the compute to where the data is collected and/or consumed. I thought that was super elegant, but what you didn't get into on all the complexity is what that means, right? I mean, data centers are pristine environments that... They're very, very controlled. The environment's controlled. The network is controlled. The security is controlled, and you have the vision of an edge device. And the one everyone always likes to use is say like a wind farm, right? Those things are out in crazy harsh conditions, and then there's still this balancing act as to what information does get stored, and processed, and used, and then what does have to go back to the data center, because it's not a substitute for the data center. It's really an extension of the data center, or maybe the data center is actually an extension of the edge. Maybe that's a better way to think of it, but we've had all these devices out there. Now, suddenly we're connecting them and bringing them into a network and adding control. And I just thought the edge represents such a big shift in the way we're going to see compute change. Probably as fundamental, I would imagine, as the cloud shift has been. >> I believe it is. I absolutely believe it's as big a change in the industry as the cloud has been. The cloud really created scale. It created automation, programmatic interfaces to infrastructure and higher level services. But it also was built around a premise of centralization. I mean, clouds themselves are distributed, and so you can create availability zones and resilient applications, but there's still a sense of centralization. Edge is really embracing the notion that data production is kind of only up and to the right, and the way to scale, processing that data, and turning that data into insights and information that's valuable for a business, is to bring compute closer to data. It's not really a new concept, but the scale at which it's happening is what's really changing how we think about building infrastructure and building the support behind all that processing. And it's that scale that requires automation, because you're just not going to be able to manage thousands, or tens of thousands, or in certain scenarios even millions of devices, without putting automation at the forefront. It's critical. >> Right. And we can't talk about edge without talking about 5G, and I laugh every time I'm watching football on Sundays and they have the 5G commercials on talking about my handset, that I can order my food to get delivered faster at my house, completely missing the point, right? 5G's about machine-to-machine communication, and the scale, and the speed, and the volume of machine-to-machine is so fundamentally different than humans talking voice-to-voice. And that's really this big driver to instrument, as you said, all these machines, all these devices. There's been sensors on them forever, but now the ability to actually connect them, and pull them into this network, and start to use the data, and control the machines is a huge shift in the way things are going to happen going forward. >> Well, it's a couple of things that are important in there. Number one, that data production, and sensors, and bringing compute closer to data, what that represents is bringing the digital world and the physical world closer together. We'll experience that at a personal level with how we communicate. We're already distributed in today's environment, and the ways we can augment our human connections through a digital medium are really going to be important to how we maintain our human connections. And then on the enterprise side, we're building this infrastructure in 5G that when you think about it from a consumer point of view and ordering your pizza faster, it really isn't the right way to think about it. Couple of key characteristics of 5G: greater bandwidth, so you can just push more packets through the network; lower latency, so you're closer to the data; and higher connection density and more reliable connections, and that kind of combination of characteristics make it really valuable for enterprise businesses. You can bring your data and compute close together. You have these highly reliable and dense connections that allow for device proliferation, and that's the piece that's really changing where the world's going. I like to think of it in a really simple way, which is 4G, and the cloud, and the smartphone created a world that today we take for granted. 10 years ago, we really couldn't imagine what it looked like. >> 5G- >> Jeff: Like tomorrow... Excuse me. >> Device proliferation, and edge computing today is building the footprint for what we can't really imagine what we will be taking for granted in 10 years from now. So we're at this great kind of change in inflection point in the industry. >> Yeah. I have to always take a moment to call out a Amara's law. I think it's the most underappreciated law. It's been stolen by other people and repackaged many ways, but it's basically we overestimate the impact of these things in the short term, and we way, way, way, way kind of underestimate the impact in the longterm. And I think your story in they keynote about once you had digital phones and smartphones, we don't even think twice about looking at a map, and where are we, and where's a store close by, and are they open, and is there a review? I mean, the infrastructure to put that together, kind of an API-based economy, which is pulling together all these bits and pieces... (scoffs) The stupid rely... Expectation, right, of performance, and how fast that information's going to be delivered to me. I think we so take it for granted. As you say, I think it's like magic, and we never thought of all the different applications of these interconnected apps enabled by an always-on device that's always connected and knows where we are. It is a huge change, and as you say that when we think about 5G... (chuckling) 10 years from now. Oh, my goodness. Where are we going to be? >> It's hard to imagine? I mean, it really is hard to imagine, and I think that's okay. And what we're doing today is introducing everything that we need to help businesses evolve. Take advantage of that. And that scale of the edge is... It's a fundamental characteristic of the edge, and so automating to manage that scale is the only way you're going to be successful, and extending what we've learned in the data center out to the edge using the same tools, the things we already understand, really is a great way to evolve a business. And that's where that common language and the discussions that I was trying to generate around Ansible as a great tool. But it's not just the tool, it's the whole process, the mindset, the culture change, the way you change how you operate your business that's going to allow us to take advantage of the future where my clothes are full of sensors and you can look through a video camera and tell immediately that I'm happy with this conversation. That's a very different kind of augmented reality than we have today. Maybe it's a bad example, but it's hard to imagine really what it'll be like. >> So Chris, I just want to close on a slight shift, right? We've been talking a lot about technology, but you talk about culture all the time, and really, it's about the people. And I think a number of times in the keynote you reinforced this is about people and culture. And I just had I'm InaMarie Johnson on, the chief diversity officer from Zendesk. And she said culture eats strategy for breakfast. Great line. So I wondered if you can talk about the culture, because it's very different and you've seen it in opensource from Red Hat for a long time, really, a shift in culture around opensource, the shift in culture around devops, and continuous delivery, and "change is a good thing, not a bad thing," and we want to be able to change our code frequently and push out new features. So again, as you think of automation and culture, what kind of comes to mind, and what should people be thinking about when they think about the people and less about the technology? >> Well, there's a couple of things. I'll reinforce what we already touched on, which is the notion of creating confidence in the automation. There's an element of trust associated with that, and that's more maybe trusting the technology. So when you're automating something, you've already got a process. You already understand how something works. It's turning that something into an automated script, or playbook in the Ansible context, and trusting that it's going to do the right thing. There's another important part of trust, which is getting more to the people part, and I've learned this a lot from opensource communities. Collaboration and communities are fundamentally built around trust, and human trust relationships, and the change in process, trusting not only that the tools are going to do the right job, but the people are really... Assuming good intent, and working with they're trying to build for the right outcomes for your business, I think that's a really important part of the overall picture. And then finally, that trust is extended to knowing that that change for the business isn't going to compromise your job, right? So thinking differently about what is your job. Is your job to do the repetitive task, or is your job to free up your time from that repetitive task to think more creatively about value you can bring to the business? That's where I think it's really challenging for organizations to make changes because you build a personal identity around the jobs that you do, and making changes to those personal identities really gets to the core of who you are as a person. That's why I think it's so complicated. The tools almost are the easy part. It's the process changes and the cultural changes, the mindset changes behind that which is difficult, but more powerful in the end. >> Yeah. Yeah. Well, I think people, process, tools... The tech is always the easy part relative to culture, and people, and changing the way people do things, and as you said, who their identity is, how they get kind of wrapped into what they do, and what they think their value is, and who they are. So to free them up from that, that's a really important point. Well, Chris, I always love having you on. Thank you for coming on again, sharing your insight. Great keynote, and give me the last word about AnsibleFest 2020. What are you looking forward to take away from this little show? >> Well, number one, my personal hope is that the conversation that I was trying to sort of ignite through the keynote is an opportunity for the community to see where Ansible fits in the edge and automation, and helping, really the industry at large, scale. And that key part of bringing a common language to help change how we communicate internally is the message I was hoping to impart on the AnsibleFest community, and so hopefully we can take that broader. Appreciate the time here to really amplify some of those messages. >> All right. Great. Well, thanks a lot, Chris, and have a great day. >> Thanks, Jeff. Thank you. >> All right. He's Chris. I'm Jeff. You're watching theCube, and our ongoing coverage of AnsibleFest 2020. Thanks for watching. We'll see you next time. (twinkly music)