(gentle upbeat music) >> Hello, everyone, welcome to theCUBE's coverage of International Women's Day. I'm John Furrier host here in Palo Alto, California. theCUBE's second year covering International Women's Day. It's been a great celebration of all the smart leaders in the world who are making a difference from all kinds of backgrounds, from technology to business and everything in between. Today we've got a great guest, Dominique Bastos, who's the senior Vice President of Cloud at Persistent Systems, formerly with AWS. That's where we first met at re:Invent. Dominique, great to have you on the program here for International Women's Day. Thanks for coming on. >> Thank you John, for having me back on theCUBE. This is an honor, especially given the theme. >> Well, I'm excited to have you on, I consider you one of those typecast personas where you've kind of done a lot of things. You're powerful, you've got great business acumen you're technical, and we're in a world where, you know the world's coming completely digital and 50% of the world is women, 51%, some say. So you got mostly male dominated industry and you have a dual engineering background and that's super impressive as well. Again, technical world, male dominated you're in there in the mix. What inspires you to get these engineering degrees? >> I think even it was more so shifted towards males. When I had the inspiration to go to engineering school I was accused as a young girl of being a tomboy and fiddling around with all my brother's toys versus focusing on my dolls and other kind of stereotypical toys that you would give a girl. I really had a curiosity for building, a curiosity for just breaking things apart and putting them back together. I was very lucky in that my I guess you call it primary school, maybe middle school, had a program for, it was like electronics, that was the class electronics. So building circuit boards and things like that. And I really enjoyed that aspect of building. I think it was more actually going into engineering school. Picking that as a discipline was a little bit, my mom's reaction to when I announced that I wanted to do engineering which was, "No, that's for boys." >> Really. >> And that really, you know, I think she, it came from a good place in trying to protect me from what she has experienced herself in terms of how women are received in those spaces. So I kind of shrugged it off and thought "Okay, well I'm definitely now going to do this." >> (laughs) If I was told not to, you're going to do it. >> I was told not to, that's all I needed to hear. And also, I think my passion was to design cars and I figured if I enroll in an industrial engineering program I could focus on ergonomic design and ultimately, you know have a career doing something that I'm passionate about. So yeah, so my inspiration was kind of a little bit of don't do this, a lot of curiosity. I'm also a very analytical person. I've been, and I don't know what the science is around left right brain to be honest, but been told that I'm a very much a logical person versus a feeler. So I don't know if that's good or bad. >> Straight shooter. What were your engineering degrees if you don't mind sharing? >> So I did industrial engineering and so I did a dual degree, industrial engineering and robotics. At the time it was like a manufacturing robotics program. It was very, very cool because we got to, I mean now looking back, the evolution of robotics is just insane. But you, you know, programmed a robotic arm to pick things up. I actually crashed the Civil Engineering School's Concrete Canoe Building Competition where you literally have to design a concrete canoe and do all the load testing and the strength testing of the materials and basically then, you know you go against other universities to race the canoe in a body of water. We did that at, in Alabama and in Georgia. So I was lucky to experience that two times. It was a lot of fun. >> But you knew, so you knew, deep down, you were technical you had a nerd vibe you were geeking out on math, tech, robotics. What happened next? I mean, what were some of the challenges you faced? How did you progress forward? Did you have any blockers and roadblocks in front of you and how did you handle those? >> Yeah, I mean I had, I had a very eye-opening experience with, in my freshman year of engineering school. I kind of went in gung-ho with zero hesitation, all the confidence in the world, 'cause I was always a very big nerd academically, I hate admitting this but myself and somebody else got most intellectual, voted by the students in high school. It's like, you don't want to be voted most intellectual when you're in high school. >> Now it's a big deal. (laughs) >> Yeah, you want to be voted like popular or anything like that? No, I was a nerd, but in engineering school, it's a, it was very humbling. That whole confidence that I had. I experienced prof, ooh, I don't want to name the school. Everybody can google it though, but, so anyway so I had experience with some professors that actually looked at me and said, "You're in the wrong program. This is difficult." I, and I think I've shared this before in other forums where, you know, my thermodynamic teacher basically told me "Cheerleading's down the hall," and it it was a very shocking thing to hear because it really made me wonder like, what am I up against here? Is this what it's going to be like going forward? And I decided not to pay attention to that. I think at the moment when you hear something like that you just, you absorb it and you also don't know how to react. And I decided immediately to just walk right past him and sit down front center in the class. In my head I was cursing him, of course, 'cause I mean, let's be real. And I was like, I'm going to show this bleep bleep. And proceeded to basically set the curve class crushed it and was back to be the teacher's assistant. So I think that was one. >> But you became his teacher assistant after, or another one? >> Yeah, I gave him a mini speech. I said, do not do this. You, you could, you could have broken me and if you would've done this to somebody who wasn't as steadfast in her goals or whatever, I was really focused like I'm doing this, I would've backed out potentially and said, you know this isn't something I want to experience on the daily. So I think that was actually a good experience because it gave me an opportunity to understand what I was up against but also double down in how I was going to deal with it. >> Nice to slay the misogynistic teachers who typecast people. Now you had a very technical career but also you had a great career at AWS on the business side you've handled 'em all of the big accounts, I won't say the names, but like we're talking about monster accounts, sales and now basically it's not really selling, you're managing a big account, it's like a big business. It's a business development thing. Technical to business transition, how do you handle that? Was that something you were natural for? Obviously you, you stared down the naysayers out of the gate in college and then in business, did that continue and how did you drive through that? >> So I think even when I was coming out of university I knew that I wanted to have a balance between the engineering program and business. A lot of my colleagues went on to do their PEs so continue to get their masters basically in engineering or their PhDs in engineering. I didn't really have an interest for that. I did international business and finance as my MBA because I wanted to explore the ability of taking what I had learned in engineering school and applying it to building businesses. I mean, at the time I didn't have it in my head that I would want to do startups but I definitely knew that I wanted to get a feel for what are they learning in business school that I missed out in engineering school. So I think that helped me when I transitioned, well when I applied, I was asked to come apply at AWS and I kind of went, no I'm going to, the DNA is going to be rejected. >> You thought, you thought you'd be rejected from AWS. >> I thought I'd be, yeah, because I have very much a startup founder kind of disruptive personality. And to me, when I first saw AWS at the stage early 2016 I saw it as a corporation. Even though from a techie standpoint, I was like, these people are insane. This is amazing what they're building. But I didn't know what the cultural vibe would feel like. I had been with GE at the beginning of my career for almost three years. So I kind of equated AWS Amazon to GE given the size because in between, I had done startups. So when I went to AWS I think initially, and I do have to kind of shout out, you know Todd Weatherby basically was the worldwide leader for ProServe and it was being built, he built it and I went into ProServe to help from that standpoint. >> John: ProServe, Professional services >> Professional services, right. To help these big enterprise customers. And specifically my first customer was an amazing experience in taking, basically the company revolves around strategic selling, right? It's not like you take a salesperson with a conventional schooling that salespeople would have and plug them into AWS in 2016. It was very much a consultative strategic approach. And for me, having a technical background and loving to solve problems for customers, working with the team, I would say, it was a dream team that I joined. And also the ability to come to the table with a technical background, knowing how to interact with senior executives to help them envision where they want to go, and then to bring a team along with you to make that happen. I mean, that was like magical for me. I loved that experience. >> So you like the culture, I mean, Andy Jassy, I've interviewed many times, always talked about builders and been a builder mentality. You mentioned that earlier at the top of this interview you've always building things, curious and you mentioned potentially your confidence might have been shaken. So you, you had the confidence. So being a builder, you know, being curious and having confidence seems to be what your superpower is. A lot of people talk about the confidence angle. How important is that and how important is that for encouraging more women to get into tech? Because I still hear that all the time. Not that they don't have confidence, but there's so many signals that potentially could shake confidence in industry >> Yeah, that's actually a really good point that you're making. A lot of signals that women get could shake their confidence and that needs to be, I mean, it's easy to say that it should be innate. I mean that's kind of like textbook, "Oh it has to come from within." Of course it does. But also, you know, we need to understand that in a population where 50% of the population is women but only 7% of the positions in tech, and I don't know the most current number in tech leadership, is women, and probably a smaller percentage in the C-suite. When you're looking at a woman who's wanting to go up the trajectory in a tech company and then there's a subconscious understanding that there's a limit to how far you'll go, your confidence, you know, in even subconsciously gets shaken a little bit because despite your best efforts, you're already seeing the cap. I would say that we need to coach girls to speak confidently to navigate conflict versus running away from it, to own your own success and be secure in what you bring to the table. And then I think a very important thing is to celebrate each other and the wins that we see for women in tech, in the industry. >> That's awesome. What's, the, in your opinion, the, you look at that, the challenges for this next generation women, and women in general, what are some of the challenges for them and that they need to overcome today? I mean, obviously the world's changed for the better. Still not there. I mean the numbers one in four women, Rachel Thornton came on, former CMO of AWS, she's at MessageBird now. They had a study where only one in four women go to the executive board level. And so there's still, still numbers are bad and then the numbers still got to get up, up big time. That's, and the industry's working on that, but it's changed. But today, what are some of the challenges for this current generation and the next generation of women and how can we and the industry meet, we being us, women in the industry, be strong role models for them? >> Well, I think the challenge is one of how many women are there in the pipeline and what are we doing to retain them and how are we offering up the opportunities to fill. As you know, as Rachel said and I haven't had an opportunity to see her, in how are we giving them this opportunity to take up those seats in the C-suite right, in these leadership roles. And I think this is a little bit exacerbated with the pandemic in that, you know when everything shut down when people were going back to deal with family and work at the same time, for better or for worse the brunt of it fell on probably, you know the maternal type caregiver within the family unit. You know, I've been, I raised my daughter alone and for me, even without the pandemic it was a struggle constantly to balance the risk that I was willing to take to show up for those positions versus investing even more of that time raising a child, right? Nevermind the unconscious bias or cultural kind of expectations that you get from the male counterparts where there's zero understanding of what a mom might go through at home to then show up to a meeting, you know fully fresh and ready to kind of spit out some wisdom. It's like, you know, your kid just freaking lost their whatever and you know, they, so you have to sort a bunch of things out. I think the challenge that women are still facing and will we have to keep working at it is making sure that there's a good pipeline. A good amount of young ladies of people taking interest in tech. And then as they're, you know, going through the funnel at stages in their career, we're providing the mentoring we're, there's representation, right? To what they're aspiring to. We're celebrating their interest in the field, right? And, and I think also we're doing things to retain them, because again, the pandemic affected everybody. I think women specifically and I don't know the statistics but I was reading something about this were the ones to tend to kind of pull it back and say well now I need to be home with, you know you name how many kids and pets and the aging parents, people that got sick to take on that position. In addition to the career aspirations that they might have. We need to make it easier basically. >> I think that's a great call out and I appreciate you bringing that up about family and being a single mom. And by the way, you're savage warrior to doing that. It's amazing. You got to, I know you have a daughter in computer science at Stanford, I want to get to that in a second. But that empathy and I mentioned Rachel Thornton, who's the CMO MessageBird and former CMO of AWS. Her thing right now to your point is mentoring and sponsorship is very key. And her company and the video that's on the site here people should look at that and reference that. They talk a lot about that empathy of people's situation whether it's a single mom, family life, men and women but mainly women because they're the ones who people aren't having a lot of empathy for in that situation, as you called it out. This is huge. And I think remote work has opened up this whole aperture of everyone has to have a view into how people are coming to the table at work. So, you know, props are bringing that up, and I recommend everyone look at check out Rachel Thornton. So how do you balance that, that home life and talk about your daughter's journey because sounds like she's nerding out at Stanford 'cause you know Stanford's called Nerd Nation, that's their motto, so you must be proud. >> I am so proud, I'm so proud. And I will say, I have to admit, because I did encounter so many obstacles and so many hurdles in my journey, it's almost like I forgot that I should set that aside and not worry about my daughter. My hope for her was for her to kind of be artistic and a painter or go into something more lighthearted and fun because I just wanted to think, I guess my mom had the same idea, right? She, always been very driven. She, I want to say that I got very lucky that she picked me to be her mom. Biologically I'm her mom, but I told her she was like a little star that fell from the sky and I, and ended up with me. I think for me, balancing being a single mom and a career where I'm leading and mentoring and making big decisions that affect people's lives as well. You have to take the best of everything you get from each of those roles. And I think that the best way is play to your strengths, right? So having been kind of a nerd and very organized person and all about, you know, systems for effectiveness, I mean, industrial engineering, parenting for me was, I'm going to make it sound super annoying and horrible, but (laughs) >> It's funny, you know, Dave Vellante and I when we started SiliconANGLE and theCUBE years ago, one of the things we were all like sports lovers. So we liked sports and we are like we looked at the people in tech as tech athletes and except there's no men and women teams, it's one team. It's all one thing. So, you know, I consider you a tech athlete you're hard charging strong and professional and smart and beautiful and brilliant, all those good things. >> Thank you. >> Now this game is changing and okay, and you've done startups, and you've done corporate jobs, now you're in a new role. What's the current tech landscape from a, you know I won't say athletic per standpoint but as people who are smart. You have all kinds of different skill sets. You have the startup warriors, you have the folks who like to be in the middle of the corporate world grow up through corporate, climb the corporate ladder. You have investors, you have, you know, creatives. What have you enjoyed most and where do you see all the action? >> I mean, I think what I've enjoyed the most has been being able to bring all of the things that I feel I'm strong at and bring it together to apply that to whatever the problem is at hand, right? So kind of like, you know if you look at a renaissance man who can kind of pop in anywhere and, oh, he's good at, you know sports and he's good at reading and, or she's good at this or, take all of those strengths and somehow bring them together to deal with the issue at hand, versus breaking up your mindset into this is textbook what I learned and this is how business should be done and I'm going to draw these hard lines between personal life and work life, or between how you do selling and how you do engineering. So I think my, the thing that I loved, really loved about AWS was a lot of leaders saw something in me that I potentially didn't see, which was, yeah you might be great at running that big account but we need help over here doing go to market for a new product launch and boom, there you go. Now I'm in a different org helping solve that problem and getting something launched. And I think if you don't box yourself in to I'm only good at this, or, you know put a label on yourself as being the rockstar in that. It leaves room for opportunities to present themselves but also it leaves room within your own mind to see yourself as somebody capable of doing anything. Right, I don't know if I answered the question accurately. >> No, that's good, no, that's awesome. I love the sharing, Yeah, great, great share there. Question is, what do you see, what do you currently during now you're building a business of Persistent for the cloud, obviously AWS and Persistent's a leader global system integrator around the world, thousands and thousands of customers from what we know and been reporting on theCUBE, what's next for you? Where do you see yourself going? Obviously you're going to knock this out of the park. Where do you see yourself as you kind of look at the continuing journey of your mission, personal, professional what's on your mind? Where do you see yourself going next? >> Well, I think, you know, again, going back to not boxing yourself in. This role is an amazing one where I have an opportunity to take all the pieces of my career in tech and apply them to building a business within a business. And that involves all the goodness of coaching and mentoring and strategizing. And I'm loving it. I'm loving the opportunity to work with such great leaders. Persistent itself is very, very good at providing opportunities, very diverse opportunities. We just had a huge Semicolon; Hackathon. Some of the winners were females. The turnout was amazing in the CTO's office. We have very strong women leading the charge for innovation. I think to answer your question about the future and where I may see myself going next, I think now that my job, well they say the job is never done. But now that Chloe's kind of settled into Stanford and kind of doing her own thing, I have always had a passion to continue leading in a way that brings me to, into the fold a lot more. So maybe, you know, maybe in a VC firm partner mode or another, you know CEO role in a startup, or my own startup. I mean, I never, I don't know right now I'm super happy but you never know, you know where your drive might go. And I also want to be able to very deliberately be in a role where I can continue to mentor and support up and coming women in tech. >> Well, you got the smarts but you got really the building mentality, the curiosity and the confidence really sets you up nicely. Dominique great story, great inspiration. You're a role model for many women, young girls out there and women in tech and in celebration. It's a great day and thank you for sharing that story and all the good nuggets there. Appreciate you coming on theCUBE, and it's been my pleasure. Thanks for coming on. >> Thank you, John. Thank you so much for having me. >> Okay, theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE here in Palo Alto getting all the content, check out the other interviews some amazing stories, lessons learned, and some, you know some funny stories and some serious stories. So have some fun and enjoy the rest of the videos here for International Women's Days, thanks for watching. (gentle inspirational music)

(upbeat music) >> Hello, everyone, and welcome to theCUBE's presentation of the "AWS Startup Showcase." This is season three, episode one. The focus of this episode is AI/ML: Top Startups Building Foundational Models, Infrastructure, and AI. It's great topics, super-relevant, and it's part of our ongoing coverage of startups in the AWS ecosystem. I'm your host, John Furrier, with theCUBE. Today, we're excited to be joined by Jay Marshall, VP of Business Development at Neural Magic. Jay, thanks for coming on theCUBE. >> Hey, John, thanks so much. Thanks for having us. >> We had a great CUBE conversation with you guys. This is very much about the company focuses. It's a feature presentation for the "Startup Showcase," and the machine learning at scale is the topic, but in general, it's more, (laughs) and we should call it "Machine Learning and AI: How to Get Started," because everybody is retooling their business. Companies that aren't retooling their business right now with AI first will be out of business, in my opinion. You're seeing massive shift. This is really truly the beginning of the next-gen machine learning AI trend. It's really seeing ChatGPT. Everyone sees that. That went mainstream. But this is just the beginning. This is scratching the surface of this next-generation AI with machine learning powering it, and with all the goodness of cloud, cloud scale, and how horizontally scalable it is. The resources are there. You got the Edge. Everything's perfect for AI 'cause data infrastructure's exploding in value. AI is just the applications. This is a super topic, so what do you guys see in this general area of opportunities right now in the headlines? And I'm sure you guys' phone must be ringing off the hook, metaphorically speaking, or emails and meetings and Zooms. What's going on over there at Neural Magic? >> No, absolutely, and you pretty much nailed most of it. I think that, you know, my background, we've seen for the last 20-plus years. Even just getting enterprise applications kind of built and delivered at scale, obviously, amazing things with AWS and the cloud to help accelerate that. And we just kind of figured out in the last five or so years how to do that productively and efficiently, kind of from an operations perspective. Got development and operations teams. We even came up with DevOps, right? But now, we kind of have this new kind of persona and new workload that developers have to talk to, and then it has to be deployed on those ITOps solutions. And so you pretty much nailed it. Folks are saying, "Well, how do I do this?" These big, generational models or foundational models, as we're calling them, they're great, but enterprises want to do that with their data, on their infrastructure, at scale, at the edge. So for us, yeah, we're helping enterprises accelerate that through optimizing models and then delivering them at scale in a more cost-effective fashion. >> Yeah, and I think one of the things, the benefits of OpenAI we saw, was not only is it open source, then you got also other models that are more proprietary, is that it shows the world that this is really happening, right? It's a whole nother level, and there's also new landscape kind of maps coming out. You got the generative AI, and you got the foundational models, large LLMs. Where do you guys fit into the landscape? Because you guys are in the middle of this. How do you talk to customers when they say, "I'm going down this road. I need help. I'm going to stand this up." This new AI infrastructure and applications, where do you guys fit in the landscape? >> Right, and really, the answer is both. I think today, when it comes to a lot of what for some folks would still be considered kind of cutting edge around computer vision and natural language processing, a lot of our optimization tools and our runtime are based around most of the common computer vision and natural language processing models. So your YOLOs, your BERTs, you know, your DistilBERTs and what have you, so we work to help optimize those, again, who've gotten great performance and great value for customers trying to get those into production. But when you get into the LLMs, and you mentioned some of the open source components there, our research teams have kind of been right in the trenches with those. So kind of the GPT open source equivalent being OPT, being able to actually take, you know, a multi-$100 billion parameter model and sparsify that or optimize that down, shaving away a ton of parameters, and being able to run it on smaller infrastructure. So I think the evolution here, you know, all this stuff came out in the last six months in terms of being turned loose into the wild, but we're staying in the trenches with folks so that we can help optimize those as well and not require, again, the heavy compute, the heavy cost, the heavy power consumption as those models evolve as well. So we're staying right in with everybody while they're being built, but trying to get folks into production today with things that help with business value today. >> Jay, I really appreciate you coming on theCUBE, and before we came on camera, you said you just were on a customer call. I know you got a lot of activity. What specific things are you helping enterprises solve? What kind of problems? Take us through the spectrum from the beginning, people jumping in the deep end of the pool, some people kind of coming in, starting out slow. What are the scale? Can you scope the kind of use cases and problems that are emerging that people are calling you for? >> Absolutely, so I think if I break it down to kind of, like, your startup, or I maybe call 'em AI native to kind of steal from cloud native years ago, that group, it's pretty much, you know, part and parcel for how that group already runs. So if you have a data science team and an ML engineering team, you're building models, you're training models, you're deploying models. You're seeing firsthand the expense of starting to try to do that at scale. So it's really just a pure operational efficiency play. They kind of speak natively to our tools, which we're doing in the open source. So it's really helping, again, with the optimization of the models they've built, and then, again, giving them an alternative to expensive proprietary hardware accelerators to have to run them. Now, on the enterprise side, it varies, right? You have some kind of AI native folks there that already have these teams, but you also have kind of, like, AI curious, right? Like, they want to do it, but they don't really know where to start, and so for there, we actually have an open source toolkit that can help you get into this optimization, and then again, that runtime, that inferencing runtime, purpose-built for CPUs. It allows you to not have to worry, again, about do I have a hardware accelerator available? How do I integrate that into my application stack? If I don't already know how to build this into my infrastructure, does my ITOps teams, do they know how to do this, and what does that runway look like? How do I cost for this? How do I plan for this? When it's just x86 compute, we've been doing that for a while, right? So it obviously still requires more, but at least it's a little bit more predictable. >> It's funny you mentioned AI native. You know, born in the cloud was a phrase that was out there. Now, you have startups that are born in AI companies. So I think you have this kind of cloud kind of vibe going on. You have lift and shift was a big discussion. Then you had cloud native, kind of in the cloud, kind of making it all work. Is there a existing set of things? People will throw on this hat, and then what's the difference between AI native and kind of providing it to existing stuff? 'Cause we're a lot of people take some of these tools and apply it to either existing stuff almost, and it's not really a lift and shift, but it's kind of like bolting on AI to something else, and then starting with AI first or native AI. >> Absolutely. It's a- >> How would you- >> It's a great question. I think that probably, where I'd probably pull back to kind of allow kind of retail-type scenarios where, you know, for five, seven, nine years or more even, a lot of these folks already have data science teams, you know? I mean, they've been doing this for quite some time. The difference is the introduction of these neural networks and deep learning, right? Those kinds of models are just a little bit of a paradigm shift. So, you know, I obviously was trying to be fun with the term AI native, but I think it's more folks that kind of came up in that neural network world, so it's a little bit more second nature, whereas I think for maybe some traditional data scientists starting to get into neural networks, you have the complexity there and the training overhead, and a lot of the aspects of getting a model finely tuned and hyperparameterization and all of these aspects of it. It just adds a layer of complexity that they're just not as used to dealing with. And so our goal is to help make that easy, and then of course, make it easier to run anywhere that you have just kind of standard infrastructure. >> Well, the other point I'd bring out, and I'd love to get your reaction to, is not only is that a neural network team, people who have been focused on that, but also, if you look at some of the DataOps lately, AIOps markets, a lot of data engineering, a lot of scale, folks who have been kind of, like, in that data tsunami cloud world are seeing, they kind of been in this, right? They're, like, been experiencing that. >> No doubt. I think it's funny the data lake concept, right? And you got data oceans now. Like, the metaphors just keep growing on us, but where it is valuable in terms of trying to shift the mindset, I've always kind of been a fan of some of the naming shift. I know with AWS, they always talk about purpose-built databases. And I always liked that because, you know, you don't have one database that can do everything. Even ones that say they can, like, you still have to do implementation detail differences. So sitting back and saying, "What is my use case, and then which database will I use it for?" I think it's kind of similar here. And when you're building those data teams, if you don't have folks that are doing data engineering, kind of that data harvesting, free processing, you got to do all that before a model's even going to care about it. So yeah, it's definitely a central piece of this as well, and again, whether or not you're going to be AI negative as you're making your way to kind of, you know, on that journey, you know, data's definitely a huge component of it. >> Yeah, you would have loved our Supercloud event we had. Talk about naming and, you know, around data meshes was talked about a lot. You're starting to see the control plane layers of data. I think that was the beginning of what I saw as that data infrastructure shift, to be horizontally scalable. So I have to ask you, with Neural Magic, when your customers and the people that are prospects for you guys, they're probably asking a lot of questions because I think the general thing that we see is, "How do I get started? Which GPU do I use?" I mean, there's a lot of things that are kind of, I won't say technical or targeted towards people who are living in that world, but, like, as the mainstream enterprises come in, they're going to need a playbook. What do you guys see, what do you guys offer your clients when they come in, and what do you recommend? >> Absolutely, and I think where we hook in specifically tends to be on the training side. So again, I've built a model. Now, I want to really optimize that model. And then on the runtime side when you want to deploy it, you know, we run that optimized model. And so that's where we're able to provide. We even have a labs offering in terms of being able to pair up our engineering teams with a customer's engineering teams, and we can actually help with most of that pipeline. So even if it is something where you have a dataset and you want some help in picking a model, you want some help training it, you want some help deploying that, we can actually help there as well. You know, there's also a great partner ecosystem out there, like a lot of folks even in the "Startup Showcase" here, that extend beyond into kind of your earlier comment around data engineering or downstream ITOps or the all-up MLOps umbrella. So we can absolutely engage with our labs, and then, of course, you know, again, partners, which are always kind of key to this. So you are spot on. I think what's happened with the kind of this, they talk about a hockey stick. This is almost like a flat wall now with the rate of innovation right now in this space. And so we do have a lot of folks wanting to go straight from curious to native. And so that's definitely where the partner ecosystem comes in so hard 'cause there just isn't anybody or any teams out there that, I literally do from, "Here's my blank database, and I want an API that does all the stuff," right? Like, that's a big chunk, but we can definitely help with the model to delivery piece. >> Well, you guys are obviously a featured company in this space. Talk about the expertise. A lot of companies are like, I won't say faking it till they make it. You can't really fake security. You can't really fake AI, right? So there's going to be a learning curve. They'll be a few startups who'll come out of the gate early. You guys are one of 'em. Talk about what you guys have as expertise as a company, why you're successful, and what problems do you solve for customers? >> No, appreciate that. Yeah, we actually, we love to tell the story of our founder, Nir Shavit. So he's a 20-year professor at MIT. Actually, he was doing a lot of work on kind of multicore processing before there were even physical multicores, and actually even did a stint in computational neurobiology in the 2010s, and the impetus for this whole technology, has a great talk on YouTube about it, where he talks about the fact that his work there, he kind of realized that the way neural networks encode and how they're executed by kind of ramming data layer by layer through these kind of HPC-style platforms, actually was not analogous to how the human brain actually works. So we're on one side, we're building neural networks, and we're trying to emulate neurons. We're not really executing them that way. So our team, which one of the co-founders, also an ex-MIT, that was kind of the birth of why can't we leverage this super-performance CPU platform, which has those really fat, fast caches attached to each core, and actually start to find a way to break that model down in a way that I can execute things in parallel, not having to do them sequentially? So it is a lot of amazing, like, talks and stuff that show kind of the magic, if you will, a part of the pun of Neural Magic, but that's kind of the foundational layer of all the engineering that we do here. And in terms of how we're able to bring it to reality for customers, I'll give one customer quote where it's a large retailer, and it's a people-counting application. So a very common application. And that customer's actually been able to show literally double the amount of cameras being run with the same amount of compute. So for a one-to-one perspective, two-to-one, business leaders usually like that math, right? So we're able to show pure cost savings, but even performance-wise, you know, we have some of the common models like your ResNets and your YOLOs, where we can actually even perform better than hardware-accelerated solutions. So we're trying to do, I need to just dumb it down to better, faster, cheaper, but from a commodity perspective, that's where we're accelerating. >> That's not a bad business model. Make things easier to use, faster, and reduce the steps it takes to do stuff. So, you know, that's always going to be a good market. Now, you guys have DeepSparse, which we've talked about on our CUBE conversation prior to this interview, delivers ML models through the software so the hardware allows for a decoupling, right? >> Yep. >> Which is going to drive probably a cost advantage. Also, it's also probably from a deployment standpoint it must be easier. Can you share the benefits? Is it a cost side? Is it more of a deployment? What are the benefits of the DeepSparse when you guys decouple the software from the hardware on the ML models? >> No you actually, you hit 'em both 'cause that really is primarily the value. Because ultimately, again, we're so early. And I came from this world in a prior life where I'm doing Java development, WebSphere, WebLogic, Tomcat open source, right? When we were trying to do innovation, we had innovation buckets, 'cause everybody wanted to be on the web and have their app and a browser, right? We got all the money we needed to build something and show, hey, look at the thing on the web, right? But when you had to get in production, that was the challenge. So to what you're speaking to here, in this situation, we're able to show we're just a Python package. So whether you just install it on the operating system itself, or we also have a containerized version you can drop on any container orchestration platform, so ECS or EKS on AWS. And so you get all the auto-scaling features. So when you think about that kind of a world where you have everything from real-time inferencing to kind of after hours batch processing inferencing, the fact that you can auto scale that hardware up and down and it's CPU based, so you're paying by the minute instead of maybe paying by the hour at a lower cost shelf, it does everything from pure cost to, again, I can have my standard IT team say, "Hey, here's the Kubernetes in the container," and it just runs on the infrastructure we're already managing. So yeah, operational, cost and again, and many times even performance. (audio warbles) CPUs if I want to. >> Yeah, so that's easier on the deployment too. And you don't have this kind of, you know, blank check kind of situation where you don't know what's on the backend on the cost side. >> Exactly. >> And you control the actual hardware and you can manage that supply chain. >> And keep in mind, exactly. Because the other thing that sometimes gets lost in the conversation, depending on where a customer is, some of these workloads, like, you know, you and I remember a world where even like the roundtrip to the cloud and back was a problem for folks, right? We're used to extremely low latency. And some of these workloads absolutely also adhere to that. But there's some workloads where the latency isn't as important. And we actually even provide the tuning. Now, if we're giving you five milliseconds of latency and you don't need that, you can tune that back. So less CPU, lower cost. Now, throughput and other things come into play. But that's the kind of configurability and flexibility we give for operations. >> All right, so why should I call you if I'm a customer or prospect Neural Magic, what problem do I have or when do I know I need you guys? When do I call you in and what does my environment look like? When do I know? What are some of the signals that would tell me that I need Neural Magic? >> No, absolutely. So I think in general, any neural network, you know, the process I mentioned before called sparcification, it's, you know, an optimization process that we specialize in. Any neural network, you know, can be sparcified. So I think if it's a deep-learning neural network type model. If you're trying to get AI into production, you have cost concerns even performance-wise. I certainly hate to be too generic and say, "Hey, we'll talk to everybody." But really in this world right now, if it's a neural network, it's something where you're trying to get into production, you know, we are definitely offering, you know, kind of an at-scale performant deployable solution for deep learning models. >> So neural network you would define as what? Just devices that are connected that need to know about each other? What's the state-of-the-art current definition of neural network for customers that may think they have a neural network or might not know they have a neural network architecture? What is that definition for neural network? >> That's a great question. So basically, machine learning models that fall under this kind of category, you hear about transformers a lot, or I mentioned about YOLO, the YOLO family of computer vision models, or natural language processing models like BERT. If you have a data science team or even developers, some even regular, I used to call myself a nine to five developer 'cause I worked in the enterprise, right? So like, hey, we found a new open source framework, you know, I used to use Spring back in the day and I had to go figure it out. There's developers that are pulling these models down and they're figuring out how to get 'em into production, okay? So I think all of those kinds of situations, you know, if it's a machine learning model of the deep learning variety that's, you know, really specifically where we shine. >> Okay, so let me pretend I'm a customer for a minute. I have all these videos, like all these transcripts, I have all these people that we've interviewed, CUBE alumnis, and I say to my team, "Let's AI-ify, sparcify theCUBE." >> Yep. >> What do I do? I mean, do I just like, my developers got to get involved and they're going to be like, "Well, how do I upload it to the cloud? Do I use a GPU?" So there's a thought process. And I think a lot of companies are going through that example of let's get on this AI, how can it help our business? >> Absolutely. >> What does that progression look like? Take me through that example. I mean, I made up theCUBE example up, but we do have a lot of data. We have large data models and we have people and connect to the internet and so we kind of seem like there's a neural network. I think every company might have a neural network in place. >> Well, and I was going to say, I think in general, you all probably do represent even the standard enterprise more than most. 'Cause even the enterprise is going to have a ton of video content, a ton of text content. So I think it's a great example. So I think that that kind of sea or I'll even go ahead and use that term data lake again, of data that you have, you're probably going to want to be setting up kind of machine learning pipelines that are going to be doing all of the pre-processing from kind of the raw data to kind of prepare it into the format that say a YOLO would actually use or let's say BERT for natural language processing. So you have all these transcripts, right? So we would do a pre-processing path where we would create that into the file format that BERT, the machine learning model would know how to train off of. So that's kind of all the pre-processing steps. And then for training itself, we actually enable what's called sparse transfer learning. So that's transfer learning is a very popular method of doing training with existing models. So we would be able to retrain that BERT model with your transcript data that we have now done the pre-processing with to get it into the proper format. And now we have a BERT natural language processing model that's been trained on your data. And now we can deploy that onto DeepSparse runtime so that now you can ask that model whatever questions, or I should say pass, you're not going to ask it those kinds of questions ChatGPT, although we can do that too. But you're going to pass text through the BERT model and it's going to give you answers back. It could be things like sentiment analysis or text classification. You just call the model, and now when you pass text through it, you get the answers better, faster or cheaper. I'll use that reference again. >> Okay, we can create a CUBE bot to give us questions on the fly from the the AI bot, you know, from our previous guests. >> Well, and I will tell you using that as an example. So I had mentioned OPT before, kind of the open source version of ChatGPT. So, you know, typically that requires multiple GPUs to run. So our research team, I may have mentioned earlier, we've been able to sparcify that over 50% already and run it on only a single GPU. And so in that situation, you could train OPT with that corpus of data and do exactly what you say. Actually we could use Alexa, we could use Alexa to actually respond back with voice. How about that? We'll do an API call and we'll actually have an interactive Alexa-enabled bot. >> Okay, we're going to be a customer, let's put it on the list. But this is a great example of what you guys call software delivered AI, a topic we chatted about on theCUBE conversation. This really means this is a developer opportunity. This really is the convergence of the data growth, the restructuring, how data is going to be horizontally scalable, meets developers. So this is an AI developer model going on right now, which is kind of unique. >> It is, John, I will tell you what's interesting. And again, folks don't always think of it this way, you know, the AI magical goodness is now getting pushed in the middle where the developers and IT are operating. And so it again, that paradigm, although for some folks seem obvious, again, if you've been around for 20 years, that whole all that plumbing is a thing, right? And so what we basically help with is when you deploy the DeepSparse runtime, we have a very rich API footprint. And so the developers can call the API, ITOps can run it, or to your point, it's developer friendly enough that you could actually deploy our off-the-shelf models. We have something called the SparseZoo where we actually publish pre-optimized or pre-sparcified models. And so developers could literally grab those right off the shelf with the training they've already had and just put 'em right into their applications and deploy them as containers. So yeah, we enable that for sure as well. >> It's interesting, DevOps was infrastructure as code and we had a last season, a series on data as code, which we kind of coined. This is data as code. This is a whole nother level of opportunity where developers just want to have programmable data and apps with AI. This is a whole new- >> Absolutely. >> Well, absolutely great, great stuff. Our news team at SiliconANGLE and theCUBE said you guys had a little bit of a launch announcement you wanted to make here on the "AWS Startup Showcase." So Jay, you have something that you want to launch here? >> Yes, and thank you John for teeing me up. So I'm going to try to put this in like, you know, the vein of like an AWS, like main stage keynote launch, okay? So we're going to try this out. So, you know, a lot of our product has obviously been built on top of x86. I've been sharing that the past 15 minutes or so. And with that, you know, we're seeing a lot of acceleration for folks wanting to run on commodity infrastructure. But we've had customers and prospects and partners tell us that, you know, ARM and all of its kind of variance are very compelling, both cost performance-wise and also obviously with Edge. And wanted to know if there was anything we could do from a runtime perspective with ARM. And so we got the work and, you know, it's a hard problem to solve 'cause the instructions set for ARM is very different than the instruction set for x86, and our deep tensor column technology has to be able to work with that lower level instruction spec. But working really hard, the engineering team's been at it and we are happy to announce here at the "AWS Startup Showcase," that DeepSparse inference now has, or inference runtime now has support for AWS Graviton instances. So it's no longer just x86, it is also ARM and that obviously also opens up the door to Edge and further out the stack so that optimize once run anywhere, we're not going to open up. So it is an early access. So if you go to neuralmagic.com/graviton, you can sign up for early access, but we're excited to now get into the ARM side of the fence as well on top of Graviton. >> That's awesome. Our news team is going to jump on that news. We'll get it right up. We get a little scoop here on the "Startup Showcase." Jay Marshall, great job. That really highlights the flexibility that you guys have when you decouple the software from the hardware. And again, we're seeing open source driving a lot more in AI ops now with with machine learning and AI. So to me, that makes a lot of sense. And congratulations on that announcement. Final minute or so we have left, give a summary of what you guys are all about. Put a plug in for the company, what you guys are looking to do. I'm sure you're probably hiring like crazy. Take the last few minutes to give a plug for the company and give a summary. >> No, I appreciate that so much. So yeah, joining us out neuralmagic.com, you know, part of what we didn't spend a lot of time here, our optimization tools, we are doing all of that in the open source. It's called SparseML and I mentioned SparseZoo briefly. So we really want the data scientists community and ML engineering community to join us out there. And again, the DeepSparse runtime, it's actually free to use for trial purposes and for personal use. So you can actually run all this on your own laptop or on an AWS instance of your choice. We are now live in the AWS marketplace. So push button, deploy, come try us out and reach out to us on neuralmagic.com. And again, sign up for the Graviton early access. >> All right, Jay Marshall, Vice President of Business Development Neural Magic here, talking about performant, cost effective machine learning at scale. This is season three, episode one, focusing on foundational models as far as building data infrastructure and AI, AI native. I'm John Furrier with theCUBE. Thanks for watching. (bright upbeat music)

(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

(upbeat music) >> Hey everyone. Welcome back to "The Cube"'s live coverage of "Women in Data Science 2023". As every year we are here live at Stanford University, profiling some amazing women and men in the fields of data science. I have my co-host for this segment is Hannah Freitag. Hannah is from Stanford's Data Journalism program, really interesting, check it out. We're very pleased to welcome our first guest of the day fresh from the keynote stage, Gayatree Ganu, the VP of Data Science at Meta. Gayatree, It's great to have you on the program. >> Likewise, Thank you for having me. >> So you have a PhD in Computer Science. You shared some really cool stuff. Everyone knows Facebook, everyone uses it. I think my mom might be one of the biggest users (Gayatree laughs) and she's probably watching right now. People don't realize there's so much data behind that and data that drives decisions that we engage with. But talk to me a little bit about you first, PhD in Computer Science, were you always, were you like a STEM kid? Little Gayatree, little STEM, >> Yeah, I was a STEM kid. I grew up in Mumbai, India. My parents are actually pharmacists, so they were not like math or stats or anything like that, but I was always a STEM kid. I don't know, I think it, I think I was in sixth grade when we got our first personal computer and I obviously used it as a Pacman playing machine. >> Oh, that's okay. (all laugh) >> But I was so good at, and I, I honestly believe I think being good at games kind of got me more familiar and comfortable with computers. Yeah. I think I always liked computers, I, yeah. >> And so now you lead, I'm looking at my notes here, the Engagement Ecosystem and Monetization Data Science teams at Facebook, Meta. Talk about those, what are the missions of those teams and how does it impact the everyday user? >> Yeah, so the engagement is basically users coming back to our platform more, there's, no better way for users to tell us that they are finding value on the things that we are doing on Facebook, Instagram, WhatsApp, all the other products than coming back to our platform more. So the Engagement Ecosystem team is looking at trends, looking at where there are needs, looking at how users are changing their behaviors, and you know, helping build strategy for the long term, using that data knowledge. Monetization is very different. You know, obviously the top, top apex goal is have a sustainable business so that we can continue building products for our users. And so, but you know, I said this in my keynote today, it's not about making money, our mission statement is not, you know, maximize as much money as you can make. It's about building a meaningful connection between businesses, customers, users, and, you know especially in these last two or three funky, post-pandemic years, it's been such a big, an important thing to do for small businesses all over all, all around the world for users to find like goods and services and products that they care about and that they can connect to. So, you know, there is truly an connection between my engagement world and the monetization world. And you know, it's not very clear always till you go in to, like, you peel the layers. Everything we do in the ads world is also always first with users as our, you know, guiding principle. >> Yeah, you mentioned how you supported especially small businesses also during the pandemic. You touched a bit upon it in the keynote speech. Can you tell our audience what were like special or certain specific programs you implemented to support especially small businesses during these times? >> Yeah, so there are 200 million businesses on our platform. A lot of them small businesses, 10 million of them run ads. So there is a large number of like businesses on our platform who, you know use the power of social media to connect to the customers that matter to them, to like you, you know use the free products that we built. In the post-pandemic years, we built a lot of stuff very quickly when Covid first hit for business to get the word out, right? Like, they had to announce when special shopping hours existed for at-risk populations, or when certain goods and services were available versus not. We had grants, there's $100 million grant that we gave out to small businesses. Users could show sort of, you know show their support with a bunch of campaigns that we ran, and of course we continue running ads. Our ads are very effective, I guess, and, you know getting a very reliable connection with from the customer to the business. And so, you know, we've run all these studies. We support, I talked about two examples today. One of them is the largest black-owned, woman black-owned wine company, and how they needed to move to an online program and, you know, we gave them a grant, and supported them through their ads campaign and, you know, they saw 60% lift in purchases, or something like that. So, a lot of good stories, small stories, you know, on a scale of 200 million, that really sort of made me feel proud about the work we do. And you know, now more than ever before, I think people can connect so directly with businesses. You can WhatsApp them, I come from India, every business is on WhatsApp. And you can, you know, WhatsApp them, you can send them Facebook messages, and you can build this like direct connection with things that matter to you. >> We have this expectation that we can be connected anywhere. I was just at Mobile World Congress for MWC last week, where, obviously talking about connectivity. We want to be able to do any transaction, whether it's post on Facebook or call an Uber, or watch on Netflix if you're on the road, we expect that we're going to be connected. >> Yeah. >> And what we, I think a lot of us don't realize I mean, those of us in tech do, but how much data science is a facilitator of all of those interactions. >> Yeah! >> As we, Gayatree, as we talk about, like, any business, whether it is the black women-owned wine business, >> Yeah. >> great business, or a a grocer or a car dealer, everybody has to become data-driven. >> Yes. >> Because the consumer has the expectation. >> Yes. >> Talk about data science as a facilitator of just pretty much everything we are doing and conducting in our daily lives. >> Yeah, I think that's a great question. I think data science as a field wasn't really defined like maybe 15 years ago, right? So this is all in our lifetimes that we are seeing this. Even in data science today, People come from so many different backgrounds and bring their own expertise here. And I think we, you know, this conference, all of us get to define what that means and how we can bring data to do good in the world. Everything you do, as you said, there is a lot of data. Facebook has a lot of data, Meta has a lot of data, and how do we responsibly use this data? How do we use this data to make sure that we're, you know representing all diversity? You know, minorities? Like machine learning algorithms don't do well with small data, they do well with big data, but the small data matters. And how do you like, you know, bring that into algorithms? Yeah, so everything we do at Meta is very, very data-driven. I feel proud about that, to be honest, because while data gets a bad rap sometimes, having no data and making decisions in the blind is just the absolute worst thing you can do. And so, you know, we, the job as a data scientist at Facebook is to make sure that we use this data, use this responsibly, make sure that we are representing every aspect of the, you know, 3 billion users who come to our platform. Yeah, data serves all the products that we build here. >> The responsibility factor is, is huge. You know, we can't talk about AI without talking about ethics. One of the things that I was talking with Hannah and our other co-host, Tracy, about during our opening is something I just learned over the weekend. And that is that the CTO of ChatGPT is a woman. (Gayatree laughs) I didn't know that. And I thought, why isn't she getting more awareness? There's a lot of conversations with their CEO. >> Yeah. >> Everyone's using it, playing around with it. I actually asked it yesterday, "What's hot in Data Science?" (all laugh) I was like, should I have asked that to let itself in, what's hot? (Gayatree laughs) But it, I thought that was phenomenal, and we need to be talking about this more. >> Yeah. >> This is something that they're likening to the launch of the iPhone, which has transformed our lives. >> I know, it is. >> ChatGPT, and its chief technologist is a female, how great is that? >> And I don't know whether you, I don't know the stats around this, but I think CTO is even less, it's even more rare to have a woman there, like you have women CEOs because I mean, we are building upon years and years of women not choosing technical fields and not choosing STEM, and it's going to take some time, but yeah, yeah, she's a woman. Isn't it amazing? It's wonderful. >> Yes, there was a great, there's a great "Fast Company" article on her that I was looking at yesterday and I just thought, we need to do what we can to help spread, Mira Murati is her name, because what she's doing is, one of the biggest technological breakthroughs we may ever see in our lifetime. It gives me goosebumps just thinking about it. (Gayatree laughs) I also wanted to share some stats, oh, sorry, go ahead, Hannah. >> Yeah, I was going to follow up on the thing that you mentioned that we had many years with like not enough women choosing a career path in STEM and that we have to overcome this trend. What are some, like what is some advice you have like as the Vice-President Data Science? Like what can we do to make this feel more, you know, approachable and >> Yeah. >> accessible for women? >> Yeah, I, there's so much that we have done already and you know, want to continue, keep doing. Of course conferences like these were, you know and I think there are high school students here there are students from my Alma Mater's undergrad year. It's amazing to like get all these women together to get them to see what success could look like. >> Yeah. >> What being a woman leader in this space could look like. So that's, you know, that's one, at Meta I lead recruiting at Meta and we've done a bunch to sort of open up the thinking around data science and technical jobs for women. Simple things like what you write in your job description. I don't know whether you know this, or this is a story you've heard before, when you see, when you have a job description and there are like 10 things that you need to, you know be good at to apply to this job, a woman sees those 10 and says, okay, I don't meet the qualifications of one of them and she doesn't apply. And a man sees one that he meets the qualifications to and he applies. And so, you know, there's small things you can do, and just how you write your job description, what goals you set for diversity and inclusion for your own organization. We have goals, Facebook's always been pretty up there in like, you know, speaking out for diversity and Sheryl Sandberg has been our Chief Business Officer for a very long time and she's been, like, amazing at like pushing from more women. So yeah, every step of the way, I think, we made a lot of progress, to be honest. I do think women choose STEM fields a lot more than they did. When I did my Computer Science I was often one of one or two women in the Computer Science class. It takes some time to, for it to percolate all the way to like having more CTOs and CEOs, >> Yeah. >> but it's going to happen in our lifetime, and you know, three of us know this, women are going to rule the world, and it (laughs) >> Drop the mic, girl! >> And it's going to happen in our lifetime, so I'm excited about it. >> And we have responsibility in helping make that happen. You know, I'm curious, you were in STEM, you talked about Computer Science, being one of the only females. One of the things that the nadb.org data from 2022 showed, some good numbers, the number of women in technical roles is now 27.6%, I believe, so up from 25, it's up in '22, which is good, more hiring of women. >> Yeah. >> One of the biggest challenges is attrition. What keeps you motivated? >> Yeah. >> To stay what, where you are doing what you're doing, managing a family and helping to drive these experiences at Facebook that we all expect are just going to happen? >> Yeah, two things come to mind. It does take a village. You do need people around you. You know, I'm grateful for my husband. You talked about managing a family, I did the very Indian thing and my parents live with us, and they help take care of the kids. >> Right! (laughs) >> (laughs) My kids are young, six and four, and I definitely needed help over the last few years. It takes mentors, it takes other people that you look up to, who've gone through all of those same challenges and can, you know, advise you to sort of continue working in the field. I remember when my kid was born when he was six months old, I was considering quitting. And my husband's like, to be a good role model for your children, you need to continue working. Like, just being a mother is not enough. And so, you know, so that's one. You know, the village that you build around you your supporters, your mentors who keep encouraging you. Sheryl Sandberg said this to me in my second month at Facebook. She said that women drop out of technical fields, they become managers, they become sort of administrative more, in their nature of their work, and her advice was, "Don't do that, Don't stop the technical". And I think that's the other thing I'd say to a lot of women. Technical stuff is hard, but you know, keeping up with that and keeping sort of on top of it actually does help you in the long run. And it's definitely helped me in my career at Facebook. >> I think one of the things, and Hannah and I and Tracy talked about this in the open, and I think you'll agree with us, is the whole saying of you can't be what you can't see, and I like to way, "Well, you can be what you can see". That visibility, the great thing that WiDS did, of having you on the stage as a speaker this morning so people can understand, everyone, like I said, everyone knows Meta, >> Yeah. >> everyone uses Facebook. And so it's important to bring that connection, >> Yeah. >> of how data is driving the experiences, the fact that it's User First, but we need to be able to see women in positions, >> Yes. >> like you, especially with Sheryl stepping down moving on to something else, or people that are like YouTube influencers, that have no idea that the head of YouTube for a very long time, Susan Wojcicki is a woman. >> (laughs) Yes. Who pioneered streaming, and I mean how often do you are you on YouTube every day? >> Yep, every day. >> But we have to be able to see and and raise the profile of these women and learn from them and be inspired, >> Absolutely. >> to keep going and going. I like what I do, I'm making a difference here. >> Yeah, yeah, absolutely. >> And I can be the, the sponsor or the mentor for somebody down the road. >> Absolutely. >> Yeah, and then referring back to what we talked in the beginning, show that data science is so diverse and it doesn't mean if you're like in IT, you're like sitting in your dark room, >> Right. (laughs) >> coding all day, but you know, >> (laughs) Right! >> to show the different facets of this job and >> Right! >> make this appealing to women, >> Yeah. for sure. >> And I said this in my keynote too, you know, one of the things that helped me most is complimenting the data and the techniques and the algorithms with how you work with people, and you know, empathy and alignment building and leadership, strategic thinking. And I think honestly, I think women do a lot of this stuff really well. We know how to work with people and so, you know, I've seen this at Meta for sure, like, you know, all of these skills soft skills, as we call them, go a long way, and like, you know, doing the right things and having a lasting impact. And like I said, women are going to rule the world, you know, in our lifetimes. (laughs) >> Oh, I can't, I can't wait to see that happen. There's some interesting female candidates that are already throwing their hats in the ring for the next presidential election. >> Yes. >> So we'll have to see where that goes. But some of the things that are so interesting to me, here we are in California and Palo Alto, technically Stanford is its own zip code, I believe. And we're in California, we're freaking out because we've gotten so much rain, it's absolutely unprecedented. We need it, we had a massive drought, an extreme drought, technically, for many years. I've got friends that live up in Tahoe, I've been getting pictures this morning of windows that are >> (laughs) that are covered? >> Yes, actually, yes. (Gayatree laughs) That, where windows like second-story windows are covered in snow. >> Yeah. >> Climate change. >> Climate change. >> There's so much that data science is doing to power and power our understanding of climate change whether it's that, or police violence. >> Yeah. (all talk together) >> We had talk today on that it was amazing. >> Yes. So I want more people to know what data science is really facilitating, that impacts all of us, whether you're in a technical role or not. >> And data wins arguments. >> Yes, I love that! >> I said this is my slide today, like, you know, there's always going to be doubters and naysayers and I mean, but there's hard evidence, there's hard data like, yeah. In all of these fields, I mean the data that climate change, the data science that we have done in the environmental and climate change areas and medical, and you know, medicine professions just so much, so much more opportunity, and like, how much we can learn more about the world. >> Yeah. >> Yeah, it's a pretty exciting time to be a data scientist. >> I feel like, we're just scratching the surface. >> Yeah. >> With the potential and the global impact that we can make with data science. Gayatree, it's been so great having you on theCUBE, thank you. >> Right, >> Thank you so much, Gayatree. >> So much, I love, >> Thank you. >> I'm going to take Data WiD's arguments into my personal life. (Gayatree laughs) I was actually just, just a quick anecdote, funny story. I was listening to the radio this morning and there was a commercial from an insurance company and I guess the joke is, it's an argument between two spouses, and the the voiceover comes in and says, "Let's watch a replay". I'm like, if only they, then they got the data that helped the woman win the argument. (laughs) >> (laughs) I will warn you it doesn't always help with arguments I have with my husband. (laughs) >> Okay, I'm going to keep it in the middle of my mind. >> Yes! >> Gayatree, thank you so much. >> Thank you so much, >> for sharing, >> Thank you both for the opportunity. >> And being a great female that we can look up to, we really appreciate your insights >> Oh, likewise. >> and your time. >> Thank you. >> All right, for our guest, for Hannah Freitag, I'm Lisa Martin, live at Stanford University covering "Women in Data Science '23". Stick around, our next guest joins us in just a minute. (upbeat music) I have been in the software and technology industry for over 12 years now, so I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCUBE. (upbeat music) Being a host on theCUBE has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, "Hey, I'm really interested in this. I love talking with customers, gimme a shot, let me come into the studio and do an interview and see if we can work together". I think where I really impact theCUBE is being a female in technology. We interview a lot of females in tech, we do a lot of women in technology events and one of the things I.

(low-key music) >> The cube's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (uplifting electronic music) (crowd chattering in background) >> Welcome back to Spain, everybody. My name's Dave Vellante. I'm here with Dave Nicholson. We are live at the Fira in Barcelona, covering MWC23 day four. We've been talking about, you know, 5G all week. We're going to talk about it some more. Jillian Kaplan is here. She's the head of Global Telecom Thought Leadership at Dell Technologies, and we're pleased to have Meg Knauth, who's the Vice President for Digital Platform Engineering at T-Mobile. Ladies, welcome to theCUBE. Thanks for coming on. >> Thanks for having us. >> Yeah, thank you. >> All right, Meg, can you explain 5G and edge to folks that may not be familiar with it? Give us the 101 on 5G and edge. >> Sure, I'd be happy to. So, at T-Mobile, we want businesses to be able to focus on their business outcomes and not have to stress about network technology. So we're here to handle the networking behind the scenes for you to achieve your business goals. The main way to think about 5G is speed, reduced latency, and heightened security. And you can apply that to so many different business goals and objectives. You know, some of the use cases that get touted out the most are in the retail manufacturing sectors with sensors and with control of inventory and things of that nature. But it can be applied to pretty much any industry because who doesn't need more (chuckles) more speed and lower latency. >> Yeah. And reliability, right? >> Exactly. >> I mean, that's what you're going to have there. So it's not like it's necessarily going to- you know, you think about 5G and these private networks, right? I mean, it's not going to, oh, maybe it is going to eat into, there's a Venn there, I know, but it's not going to going to replace wireless, right? I mean, it's new use cases. >> Yeah. >> Maybe you could talk about that a little bit. >> Yeah, they definitely coexist, right? And Meg touched a little bit on like all the use cases that are coming to be, but as we look at 5G, it's really the- we call it like the Enterprise G, right? It's where the enterprise is going to be able to see changes in their business and the way that they do things. And for them, it's going to be about reducing costs and heightening ROI, and safety too, right? Like being able to automate manufacturing facilities where you don't have workers, like, you know, getting hit by various pieces of equipment and you can take them out of harm's way and put robots in their place. And having them really work in an autonomous situation is going to be super, super key. And 5G is just the, it's the backbone of all future technologies if you look at it. We have to have a network like that in order to build things like AI and ML, and we talk about VR and the Metaverse. You have to have a super reliable network that can handle the amount of devices that we're putting out today, right? So, extremely important. >> From T-Mobile's perspective, I mean we hear a lot about, oh, we spent a lot on CapEx, we know that. You know, trillion and a half over the next seven years, going into 5G infrastructure. We heard in the early keynotes at MWC, we heard the call to you know, tax the over the top vendors. We heard the OTT, Netflix shot back, they said, "Why don't you help us pay for the content that we're creating?" But, okay, so I get that, but telcos have a great business. Where's T-Mobile stand on future revenue opportunities? Are you looking to get more data and monetize that data? Are you looking to do things like partner with Dell to do, you know, 5G networks? Where are the opportunities for T-Mobile? >> I think it's more, as Jillian said, it's the opportunities for each business and it's unique to those businesses. So we're not in it just for ourselves. We're in it to help others achieve their business goals and to do more with all of the new capabilities that this network provides. >> Yeah, man, I like that answer because again, listening to some of the CEOs of the large telcos, it's like, hmm, what's in it for me as the customer or the business? I didn't hear enough of that. And at least in the early keynotes, I'm hearing it more, you know, as the show goes on. But I don't know, Dave, what do you think about what you've heard at the event? >> Well, I'm curious from T-Mobile's perspective, you know when a consumer thinks about 5G, we think of voice, text, and data. And if we think about the 5G network that you already have in place, I'm curious, if you can share this kind of information, what percentage of that's being utilized now? How much is available for the, you know, for the Enterprise G that we're talking about, and maybe, you know, in five years in the future, do you have like a projected mix of consumer use versus all of these back office, call them processes that a consumer's not aware of, but you know the factory floor being connected via 5G, that frontiers that emerges, where are we now and what are you looking towards? Does that make sense? Kind of the mixed question? >> Hand over the business plan! (all laugh) >> Yeah! Yeah, yeah, yeah. >> Yeah, I- >> I want numbers Meg, numbers! >> Wow. (Dave and Dave laugh) I'm probably actually not the right person to speak to that. But as you know, T-Mobile has the largest 5G network in North America, and we just say, bring it, right? Let's talk- >> So you got room, you got room for Jillian's stuff? >> Yeah, let's solve >> Well, we can build so many >> business problems together. >> private 5G networks, right? Like I would say like the opportunities are... There's not a limit, right? Because as we build out these private networks, right? We're not on a public network when we're talking about like connecting these massive factories or connecting like a retail store to you and your house to be able to basically continue to try on the clothes remotely, something like that. It's limitless and what we can build- >> So they're related, but they're not necessarily mutually exclusive in the sense that what you are doing in the factory example is going to interfere with my ability to get my data through T-mobile. >> No, no, I- >> These are separated. >> Yeah. Yeah. >> Okay. >> As we build out these private networks and these private facilities, and there are so many applications in the consumer space that haven't even been realized yet. Like, when we think about 4G, when 4G launched, there were no applications that needed 4G to run on our cell phones, right? But then the engineers got to work, right? And we ended up with Uber and Instagram stories and all these applications that require 4G to launch. And that's what's going to happen with 5G too, it's like, as the network continues to get built, in the consumer space as well as the enterprise space, there's going to be new applications realized on this is all the stuff that we can do with this amazing network and look how many more devices and look how much faster it is, and the lower latency and the higher bandwidth, and you know, what we can really build. And I think what we're seeing at this show compared to last year is this stuff actually in practice. There was a lot of talk last year, like about, oh, this is what we can build, but now we're building it. And I think that's really key to show that companies like T-Mobile can help the enterprise in this space with cooperation, right? Like, we're not just talking about it now, we're actually putting it into practice. >> So how does it work? If I put in a private network, what are you doing? You slice out a piece of the network and charge me for it and then I get that as part of my private network. How does it actually work for the customer? >> You want to take that one? >> So I was going to say, yeah, you can do a network slice. You can actually physically build a private network, right? It depends, there's so many different ways to engineer it. So I think you can do it either way, basically. >> We just, we don't want it to be scary, right? >> Yep. >> So it starts with having a conversation about the business challenges that you're facing and then backing it into the technology and letting the technology power those solutions. But we don't want it to be scary for people because there's so much buzz around 5G, around edge, and it can be overwhelming and you can feel like you need a PhD in engineering to have a conversation. And we just want to kind of simplify things and talk in your language, not in our language. We'll figure out the tech behind the scenes. Just tell us what problems we can solve together. >> And so many non-technical companies are having to transform, right? Like retail, like manufacturing, that haven't had to be tech companies before. But together with T-Mobile and Dell, we can help enable that and make it not scary like Meg said. >> Right, so you come into my factory, I say, okay, look around. I got all these people there, and they're making hoses and they're physically putting 'em together. And we go and we have to take a physical measurement as to, you know, is it right? And because if we don't do that, then we have to rework it. Okay, now that's a problem. Okay, can you help me digitize that business? I need a network to do that. I'm going to put in some robots to do that. This is, I mean, I'm making this up but this has got to be a common use case, right? >> Yeah. >> So how do you simplify that for the business owner? >> So we start with what we can provide, and then in some cases you need additional solution providers. You might need a robotics company, you might need a sensor company. But we have those contacts to bring that together for you so that you don't have to be the expert in all those things. >> And what do I do with all the data that I'm collecting? Because, you know, I'm not really a data expert. Maybe, you know, I'm good at putting hoses together, but what's the data layer look like here? (all laughing) >> It's a hose business! >> I know! >> Great business. >> Back to the hoses again. >> There's a lot of different things you can do with it, right? You can collect it in a database, you can send it up to a cloud, you can, you know, use an edge device. It depends how we build the network. >> Dave V.: Can you guys help me do that? Can you guys- >> Sure, yeah. >> Help me figure that out. Should I put it into cloud? Should I use this database or that data? What kind of skills do I need? >> And it depends on the size of the network, right? And the size of the business. Like, you know, there's very simple. You don't have to be a massive manufacturer in order to install this stuff. >> No, I'm asking small business questions. >> Yeah. >> Right, I might not have this giant IT team. I might not have somebody who knows how to do ETL and PBA. >> Exactly. And we can talk to you too about what data matters, right? And we can, together, talk about what data might be the most valuable to you. We can talk to you about how we use data. But again, simplifying it down and making it personal to your business. >> Your point about scary is interesting, because no one has mentioned that until you did in four days. Three? Four days. Somebody says, let's do a private 5G network. That sounds like you're offering, you know, it's like, "Hey, you know what we should do Dave? We'll build you a cruise ship." It's like, I don't need a cruise ship, I just want to go bass fishing. >> Right, right, right. >> But in fact, these things are scalable in the sense that it can be scaled down from the trillions of dollars of infrastructure investment. >> Yeah. >> Yeah. It needs to be focused on your outcome, right? And not on the tech. >> When I was at the Dell booth I saw this little private network, it was about this big. I'm like, how much is that? I want one of those. (all laugh) >> I'm not the right person to talk about that! >> The little black one? >> Yes. >> I wanted one of those, too! >> I saw it, it had a little case to carry it around. I'm like, that could fit in my business. >> Just take it with you. >> theCUBE could use that! (all laugh) >> Anything that could go in a pelican case, I want. >> It's true. Like, it's so incredibly important, like you said, to focus on outcomes, right? Not just tech for the sake of tech. What's the problem? Let's solve the problem together. And then you're getting the outcome you want. You'll know what data you need. If you know what the problem is, you're like, okay this is the data I need to know if this problem is solved or not. >> So it sounds like 2022 was the year of talking about it. 2023, I'm inferring is the year of seeing it. >> Yep. >> And 2024 is going to be the year of doing it? >> I think we're doing it now. >> We're doing it now. >> Yeah. >> Okay. >> Yeah, yeah. We're definitely doing it now. >> All right. >> I see a lot of this stuff being put into place and a lot more innovation and a lot more working together. And Meg mentioned working with other partners. No one's going to do this alone. You've got to like, you know, Dell especially, we're focused on open and making sure that, you know, we have the right software partners. We're bringing in smaller players, right? Like ISVs too, as well as like the big software guys. Incredibly, incredibly important. The sensor companies, whatever we need you've got to be able to solve your customer's issue, which in this case, we're looking to help the enterprise together to transform their space. And Dell knows a little bit about the enterprise, so. >> So if we are there in 2023, then I assume 2024 will be the year that each of your companies sets up a dedicated vertical to address the hose manufacturing market. (Meg laughing) >> Oh, the hose manufacturing market. >> Further segmentation is usually a hallmark of the maturity of an industry. >> I got a lead for you. >> Yeah, there you go. >> And that's one thing we've done at Dell, too. We've built like this use case directory to help the service providers understand what, not just say like, oh, you can help manufacturers. Yeah, but how, what are the use cases to do that? And we worked with a research firm to figure out, like, you know these are the most mature, these are the best ROIs. Like to really help hone in on exactly what we can deploy for 5G and edge solutions that make the most sense, not only for service providers, right, but also for the enterprises. >> Where do you guys want to see this partnership go? Give us the vision. >> To infinity and beyond. To 5G! (Meg laughing) To 5G and beyond. >> I love it. >> It's continuation. I love that we're partnering together. It's incredibly important to the future of the business. >> Good deal. >> To bring the strengths of both together. And like Jillian said, other partners in the ecosystem, it has to be approached from a partnership perspective, but focused on outcomes. >> Jillian: Yep. >> To 5G and beyond. I love it. >> To 5G and beyond. >> Folks, thanks for coming on theCUBE. >> Thanks for having us. >> Appreciate your insights. >> Thank you. >> All right. Dave Vellante for Dave Nicholson, keep it right there. You're watching theCUBE. Go to silliconANGLE.com. John Furrier is banging out all the news. theCUBE.net has all the videos. We're live at the Fira in Barcelona, MWC23. We'll be right back. (uplifting electronic music)

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. >> All right, welcome back to the Fira in Barcelona. This is Dave Vellante with Dave Nicholson. Day 4 of coverage MWC 23. We've been talking all week about the disaggregation of the telco networks, how telcos need to increase revenue how they're not going to let the over the top providers do it again. They want to charge Netflix, right? And Netflix is punching back. There maybe are better ways to do revenue acceleration. We're going to talk to that topic with Dave Trigg who's the Global Vice President of Telecom systems business at Dell Technologies. And Ken Burns, who's a global telecom partner, sales lead. Guys, good to see you. >> Good to see you. Great to be here. >> Dave, you heard my, you're welcome. You heard my intro. It's got to be better ways to, for the telcos to make money. How can they accelerate revenue beyond taxing Netflix? >> Yeah, well, well first of all, sort of the promise of 5G, and a lot of people talk about 5G as the enterprise G. Right? So the promise of 5G is to really help drive revenue enterprise use cases. And so, it's sort of the promise of the next generation of technology, but it's not easy to figure out how we monetize that. And so we think Dell has a pretty significant role to play. It's a CEO conversation for every telco and how they accelerate. And so it's an area we're investing heavily into three different areas for telcos. One is the IT space. Dell's done that forever. 90% of the companies leaning in on that. The other places network, network's more about cost takeout. And the third area where we're investing in is working with what we call their line of businesses, but it's really their business units, right? How can we sit down with them and really understand what services do they take to market? Where do they go? So, we're making significant investments. So one way they can do it is working with Dell and and we're making big investments 'cause in most Geos we have a fairly significant sales force. We've brought in an industry leader to help us put it together. And we're getting very focused on this space and, you know, looking forward to talking more about it. >> So Ken, you know, the space inside and out, we just had at AT&T on... >> Dave Trigg: Yep. >> And they were saying we have to be hypersensitive because of our platinum brand to the use of personal information. >> Ken: Yeah. >> So we're not going to go there yet. We're not going to go directly monetize, but yet I'm thinking well, Netflix knows what I'm watching and they're making recommendations and they're, and and that's how they make money. And so the, the telcos are, are shy about doing that for right reasons, but they want to make better offers. They want to put, put forth better bundles. You know, they don't, they don't want to spend all their time trying to figure that out and not being able to change when they need to change. So, so what is the answer? If they're not going to go toward that direct monetization of data? >> Ken: Yeah. >> How do they get there? >> So I, I joined Dell in- at the end of June and brought on, as David said, to, to build and lead this what we call the line of business strategy, right? And ultimately what it is is tying together Dell technology solutions and the best of breed of what the telecoms bring to bear to solve the business outcomes of our joint customers. And there's a few jewels inside of Dell. One of it is that we have 35,000 sellers out there all touching enterprise business customers. And we have a really good understanding of what those customer needs are and you know what their outcomes needs to be. The other jewel is we have a really good understanding of how to solve those business outcomes. Dell is an open company. We work with thousands of integrators, and we have a really good insight in terms of how to solve those business outcomes, right? And so in my conversations with the telecom companies when you talk about, you know combining the best assets of Dell with their capabilities and we're all talking to the same customers, right? And if we're giving them the same story on these solutions solving business outcomes it's a beautiful thing. It's a time to market. >> What's an example of a, of a, of a situation where you'll partner with telcos that's going to drive revenue for, for both of you and value for the customer? >> Yeah, great question. So we've been laser focused on four key areas, cyber, well, let me start off with connected laptops, cyber, private mobility, and edge. Right? Now, the last two are a little bit squishy, but I'll I'll get to that in a bit, right? Because ultimately I feel like with this 5G market, we could actually make the market. And the way that we've been positioning this is almost, almost on a journey for IOT. When we talk about laptops, right? Dell is the, is the number one company in the world to sell business laptops. Well, if we start selling connected laptops the telcos are starting to say, well, you know what? If all of those laptops get connected to my network, that's a ton of 5G activations, right? We have the used cases on why having a connected workforce makes sense, right? So we're sharing that with the telcos to not simply sell a laptop, but to sell the company on why it makes sense to have that connected workforce. >> Dave Vellante: Why does it make sense? It could change the end customer. >> Ken: Yeah. So, you know, I'm probably not the best to answer that one right? But, but ultimately, you know Dell is selling millions and millions of laptops out there. And, and again, the Verizon's, the AT&T's, the T-mobile's, they're seeing the opportunity that, you know, connecting those laptops, give those the 5G activations right? But Dave, you know, the way that we've been positioning this is it's not simply a laptop could be really a Trojan horse into this IOT journey. Because ultimately, if you sell a thousand laptops to an enterprise company and you're connecting a thousand of their employees, you're connecting people, right? And we can give the analytics around that, what they're using it for, you know, making sure that the security, the bios, all of that is up to date. So now that you're connecting their people you could open up the conversation to why don't we we connect your place and, you know, allowing the telecom companies to come in and educate customers and the Dell sales force on why a private 5G mobility network makes sense to connecting places. That's a great opportunity. When you connect the place, the next part of that journey is connecting things in that place. Robotics, sensors, et cetera, right? And, and so really, so we're on the journey of people, places, things. >> So they got the cyber angle angle in there, Dave. That, that's clear benefit. If you, you know, if you got all these bespoke laptops and they're all at different levels you're going to get, you know, you're going to get hacked anyway. >> Ken: That's right. >> You're going to get hacked worse. >> Yeah. I'm curious, as you go to market, do you see significant differences? You don't have to name any names, but I imagine that there are behemoths that could be laggards because essentially they feel like they're the toll booth and all they have to do is collect, keep collecting the tolls. Whereas some of the smaller, more nimble, more agile entities that you might deal with might be more receptive to this message. That seems to be the sort of way the circle of life are. Are you seeing that? Are you seeing the big ones? Are you seeing the, you know, the aircraft carriers realizing that we got to turn into the wind guys and if we don't start turning into the wind now we're going to be in trouble. >> So this conference has been absolutely fantastic allowing us to speak with, you know, probably 30 plus telecom operators around this strategy, right? And all of the big guys, they've invested hundreds of billions of dollars in their 5G network and they haven't really seen the ROI. So when we're coming into them with a story about how Dell can help monetize their 5G network I got to tell you they're pretty excited >> Dave Nicholson: So they're receptive? >> Oh my God. They are very receptive >> So that's the big question, right? I mean is, who's, is anybody ever going to make any money off of 5G? And Ken, you were saying that private mobility and edge are a little fuzzy but I think from a strategy standpoint I mean that is a potential gold mine. >> Yeah, but it, for, for lot of the telcos and most telcos it's a pretty significant shift in mentality, right? Cause they are used to selling sim cards to some degree and how many sim cards are they selling and how many, what other used cases? And really to get to the point where they understand the use case, 'cause to get into the enterprise to really get into what can they do to help power a enterprise business more wholly. They've got to understand the use case. They got to understand the more complete solution. You know, Dell's been doing that for years. And that's where we can bring our Salesforce, our capabilities, our understanding of the customer. 'cause even your original question around AT&T and trying to understand the data, that's just really a how do you get better understanding of your customer, right? >> Right. Absolutely. >> And, and combined we're better together 'cause we bring a more complete picture of understanding our customers and then how can we help them understand what the edge is. Cause nobody's ever bought an Edge, right? They're buying an Edge to get a business outcome. You know, back in the day, nobody ever bought a data lake, right? Like, you know, they're buying an outcome. They want to use, use that data lake or they want to use the edge to deliver something. They want to use 5G. And 5G has very real capabilities. It's got intrinsic security, which, you know a lot of the wifi doesn't. It's got guaranteed on time, you know, for areas where you can't lose connectivity: autonomous vehicles, et cetera. So it's got very real capabilities that helps deliver that outcome. But you got to be able to translate that into the en- enterprise language to help them solve a problem. And that's where we think we need the help of the telcos. I think the telcos we can help them as well and, and really go drive that outcome. >> So Dell's bringing its go to market expertise and its technology. The telcos obviously have the the connectivity piece and what they do. There's no overlap in terms of the... >> Yeah. >> The, the equipment and the software that you're selling. I mean, they're going to, they're going to take your equipment and create new networks. Beautiful. And, and it's interesting you, like, you think about how Dell has transformed prior to EMC, Dell was, you know, PC maker with a subpar enterprise business, right? Kind of a wannabe enterprise business. Sorry Dell, it's the truth. And then EMC was largely, you know, a company sold storage boxes, but you owned VMware and then brought those two together. Now all of a sudden you had Dell powerhouse leader and Michael Dell, you had VMware incredibly strategic and important and it got EMC with amazing go to market. All of a sudden this Dell, Dell technologies became incredibly attractive to CIOs, C-level executives, board level. And you've come out of that transition VMware's now a separate company, right? And now, but now you have these relationships and you got the shops to be able to go into these edge locations at companies And actually go partner with the telcos. And you got a very compelling value proposition. >> Well, it's been interesting as in, in this show, again most telcos think of Dell as a server provider, you know? Important, but not overly strategic in their journey. But as we've started to invest in this business we've started to invest in things like automation. We've brought together things in our Infra Blocks and then we help them develop revenue. We're not only helping 'em take costs out of their network we're not helping 'em take risk out of deploying that network. We're helping them accelerate the deployment of that network. And then we're helping 'em drive revenue. We are having, you know, they're starting to see us in a new light. Not done yet, but, you know, you can start to see, one, how they're looking at Dell and two, and then how we can go to market. And you know, a big part of that is helping 'em drive and generate revenue. >> Yeah. Well, as, as a, as a former EMC person myself, >> Yeah? >> I will assert that that strategic DNA was injected into Dell by the acquisition of, of EMC. And I'm sticking... >> I won't say that. Okay I'll believe you on that. >> I'm sticking with the story. And it makes sense when you think about moving up market, that's the natural thing. What's, what's what's nearly impossible is to say, we sell semi-trucks but we want to get into the personal pickup truck market. That's that, that doesn't work. Going the other way works. >> Dave Trigg: Yeah. >> Now, now back to the conversation that you had with, with, with AT&T. I'm not buying this whole, no offense to AT&T, but I'm not buying this whole story that, you know, oh we're concerned about our branded customer data. That sounds like someone who's a little bit too comfortable with their existing revenue stream. If I'm out there, I want to be out partnering with folks who are truly aggressive about, about coming up with the next cool thing. You guys are talking about being connected in a laptop. Someone would say, well I got wifi. No, no, no. I'm thinking I want to sim in my laptop cause I don't want to screw around with wifi. Okay, fine. If I know I'm going to be somewhere with excellent wifi connectivity, great. But most of the time it's not excellent. >> That's right. >> So the idea that I could maybe hit F2 and have it switch over to my sim and know that anywhere that I've got coverage, I have high speed connections. Just the convenience of that. >> Ken: Absolutely. >> I'd pay extra for that as an end user consumer. >> Absolutely. >> And I pay for the service. >> Like I tell you, if it interests AT&T I think it's more not, they ask, they're comfortable. They don't know how to monetize that data. Now, of course, AT&T has a media >> Dave Nicholson: Business necessity is the mother of invention. If they don't see the necessity then they're not going to think about it. >> It's a mentality shift. Yes, but, but when you start talking about private mobility and edge, there's there's no concern about personal information there. You're going in with basically a business transformation. Hey, your, your business is, is not, not digital. It's not automated. Now we're going to automate that and digitize that. It's like the, the Dell booth with the beer guys. >> Right. >> You saw that, right? >> I mean that's, I mean that's a simple application. Yeah, a perfect example of how you network and use this technology. >> I mean, how many non-digital businesses are that that need to go digital? >> Dave Nicholson: Like, hundred percent of them. >> Everyone. >> Dave Nicholson: Pretty much. >> Yeah. And this, and this jewel that we have inside of Dell our global industries group, right, where we're investing really heavily in terms of what is the manufacturing industry looking for retail, finance, et cetera. So we have a CTO that came in, that it would be the CTO of manufacturing that gives us a really good opportunity to go to at AT&T or to Verizon or any telco out there, right? To, to say, these are the outcomes. There's Dell technology already in place. How do we connect it to your network? How do we leverage your assets, your manager professional services to provide a richer experience? So it's, there's, you said before Dave, there's really no overlap between Dell and, and our telecom partners. >> You guys making some serious investments here. I mean I, I've been, I was been critical over the years of, hey, you can't just take an X86 block, put a name on it that says edge something and throw it over the fence because that's what you were doing. >> Dave Trigg: And we would agree. >> Yeah. Right. But, of course, but that's all you had at the time. And so you put some... >> We may not have agreed then, but we would agree. >> You bought, brought some people in, you know, like Ken, who really know the business. You brought people into the technical side and you can really see it happening. It's not going to happen overnight. You know, I mean, you know if I were an investor in Dell, I'd be like, okay when are you going to start making money at this business? I'd be like, be patient. You know, it's going to take some time but look at the TAM. >> Yep. >> You know, you guys do a good, good TAM. Tennis is a pro at this stuff. >> We've been at, we've been at this two, three years and we're just now coming with some real material products. You've seen our server line really start to get more purpose-built, really start to get in there as we've started to put out some software that allows for quicker automation, quicker deployments. We have some telcos that are using it to deploy at 10,000 locations. They're literally turning up thousands of locations a week. And so yeah, we're starting to put out some real capability. Got a long way to go. A lot of exciting things on the roadmap. But to your point, it doesn't, you know the ship doesn't turn overnight, you know. >> It could be a really meaningful portion of Dell's business. I'm, I'm excited for the day that Tom Sweet starts reporting on it. Here's our telco business. Yeah. The telco business. But that's not going to happen overnight. But you know, Dell's pretty good at things like ROI. And so you guys do a lot of planning a lot of TAM analysis, a lot of technical analysis, bringing the ecosystem together. That's what this business needs. I, I just don't, it's, it feels unstoppable. You know, you're at this show everybody recognizes the need to open up. Some telcos are moving faster than others. The ones that move faster are going to disrupt. They're going to probably make some mistakes, you know but they're going to get there first. >> Well we've, we've seen the disruptors are making some mistakes and are kind of re- they're already at the phase where they're reevaluating, you know, their approach. Which is great. You know, you, you learn and adjust. You know, you run into a wall, you, you make a turn. And the interesting thing, one of the biggest learnings I've taken out of the show is talking to a bunch of the telcos that are a little bit more of the laggards. They're like, Nope, we, we don't believe in open. We don't think we can do it. We don't have the skillset. They're maybe in a geo that it's hard to find the skillset. As they've been talking to us, and we've been talking about, there's almost a glimmer of hope. They're not convinced yet, but they're like, well wait, maybe we can do this. Maybe open, you know, does give us choice. Maybe it can help us accelerate revenue. So it's been interesting to see a little bit of the, just a little bit, but a little bit of that shift. >> We all remember at 2010, 2011, you talked to banks and financial services companies about, the heck, the Cloud is happening, the Cloud's going to take over the world. We're never going to go into the Cloud. Now they're the biggest, you know Capital One's launching Cloud businesses, Western Union, I mean, they're all in the cloud, right? I mean, it's the same thing's going to happen here. Might, it might take a different pattern. Maybe it takes a little longer, but it's, it's it's a fate are completely >> I was in high school then, so I don't remember all that. >> Sorry, Dave. >> Wow, that was a low blow, like you know? >> But, but the, but the one thing that is for sure there's money to be made convincing people to get off of the backs of the dinosaurs they're riding. >> Dave Vellante: That's right. >> And also, the other thing that's a certainty is that it's not easy. And because it's not easy, there's opportunity there. So I know, I know it's, it, it, it, it, it all sounds great to talk about the the wonderful vision of the future, but I know how hard the the road is that you have to go down to get people, especially if you're comfortable with the revenue stream, if you're comfortable running the plumbing. If you're so comfortable that you can get up on stage and say, I want more money from you to pump your con- your content across my network. I love the Netflix retort, right Dave? >> Yeah, totally Dave. And, but the, the other thing is, telco's a great business. It's, they got monopolies that print money. So... >> Dave Nicholson: It's rational. It's rational. I understand. >> There's less of an incentive to move but what's going to be the incentive is guys like Dish Network coming in saying, we're going to, we're going to disrupt, we're going to build new apps. >> That's right. >> Yeah. >> Well and it's, you know, revenue acceleration, the board level, the CEO level know that they have to, you know, do things different. But to your point, it's just hard, and there's so much gravity there. There's hundreds of years literally of gravity of how they've operated their business. To your point, a lot of them, you know, lot- most of 'em were regulated and most Geos around the world at one point, right? They were government owned or government regulated entities. It's, it's a big ship to turn and it's really hard. We're not claiming we can help them turn the ship overnight but we think we can help evolve them. We think we can go along with the journey and we do think we are better together. >> IT the network and the line of business. Love the strategy. Guys, thanks so much for coming in theCUBE. >> Thank you so much. >> Thank you. >> All right, for Dave, Nicholson, Dave Vellante here, John Furrier is in our Palo Alto studio banging out all the news, keep it right there. TheCUBE's coverage of MWC 23. We'll be right back.

(gentle music) >> Narrator: "TheCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hello everybody, welcome back to day four of "theCUBE's" coverage of MWC '23. We're here live at the Fira in Barcelona. Wall-to-wall coverage, John Furrier is in our Palo Alto studio, banging out all the news. Really, the whole week we've been talking about the disaggregation of the telco network, the new opportunities in telco. We're really excited to have AT&T and Snowflake here. Dave Whittington is the AVP, at the Chief Data Office at AT&T. Roddy Tranum is the Assistant Vice President, for Channel Performance Data and Tools at AT&T. And Phil Kippen, the Global Head Of Industry-Telecom at Snowflake, Snowflake's new telecom business. Snowflake just announced earnings last night. Typical Scarpelli, they beat earnings, very conservative guidance, stocks down today, but we like Snowflake long term, they're on that path to 10 billion. Guys, welcome to "theCUBE." Thanks so much >> Phil: Thank you. >> for coming on. >> Dave and Roddy: Thanks Dave. >> Dave, let's start with you. The data culture inside of telco, We've had this, we've been talking all week about this monolithic system. Super reliable. You guys did a great job during the pandemic. Everything shifting to landlines. We didn't even notice, you guys didn't miss a beat. Saved us. But the data culture's changing inside telco. Explain that. >> Well, absolutely. So, first of all IoT and edge processing is bringing forth new and exciting opportunities all the time. So, we're bridging the world between a lot of the OSS stuff that we can do with edge processing. But bringing that back, and now we're talking about working, and I would say traditionally, we talk data warehouse. Data warehouse and big data are now becoming a single mesh, all right? And the use cases and the way you can use those, especially I'm taking that edge data and bringing it back over, now I'm running AI and ML models on it, and I'm pushing back to the edge, and I'm combining that with my relational data. So that mesh there is making all the difference. We're getting new use cases that we can do with that. And it's just, and the volume of data is immense. >> Now, I love ChatGPT, but I'm hoping your data models are more accurate than ChatGPT. I never know. Sometimes it's really good, sometimes it's really bad. But enterprise, you got to be clean with your AI, don't you? >> Not only you have to be clean, you have to monitor it for bias and be ethical about it. We're really good about that. First of all with AT&T, our brand is Platinum. We take care of that. So, we may not be as cutting-edge risk takers as others, but when we go to market with an AI or an ML or a product, it's solid. >> Well hey, as telcos go, you guys are leaning into the Cloud. So I mean, that's a good starting point. Roddy, explain your role. You got an interesting title, Channel Performance Data and Tools, what's that all about? >> So literally anything with our consumer, retail, concenters' channels, all of our channels, from a data perspective and metrics perspective, what it takes to run reps, agents, all the way to leadership levels, scorecards, how you rank in the business, how you're driving the business, from sales, service, customer experience, all that data infrastructure with our great partners on the CDO side, as well as Snowflake, that comes from my team. >> And that's traditionally been done in a, I don't mean the pejorative, but we're talking about legacy, monolithic, sort of data warehouse technologies. >> Absolutely. >> We have a love-hate relationship with them. It's what we had. It's what we used, right? And now that's evolving. And you guys are leaning into the Cloud. >> Dramatic evolution. And what Snowflake's enabled for us is impeccable. We've talked about having, people have dreamed of one data warehouse for the longest time and everything in one system. Really, this is the only way that becomes a reality. The more you get in Snowflake, we can have golden source data, and instead of duplicating that 50 times across AT&T, it's in one place, we just share it, everybody leverages it, and now it's not duplicated, and the process efficiency is just incredible. >> But it really hinges on that separation of storage and compute. And we talk about the monolithic warehouse, and one of the nightmares I've lived with, is having a monolithic warehouse. And let's just go with some of my primary, traditional customers, sales, marketing and finance. They are leveraging BSS OSS data all the time. For me to coordinate a deployment, I have to make sure that each one of these units can take an outage, if it's going to be a long deployment. With the separation of storage, compute, they own their own compute cluster. So I can move faster for these people. 'Cause if finance, I can implement his code without impacting finance or marketing. This brings in CI/CD to more reality. It brings us faster to market with more features. So if he wants to implement a new comp plan for the field reps, or we're reacting to the marketplace, where one of our competitors has done something, we can do that in days, versus waiting weeks or months. >> And we've reported on this a lot. This is the brilliance of Snowflake's founders, that whole separation >> Yep. >> from compute and data. I like Dave, that you're starting with sort of the business flexibility, 'cause there's a cost element of this too. You can dial down, you can turn off compute, and then of course the whole world said, "Hey, that's a good idea." And a VC started throwing money at Amazon, but Redshift said, "Oh, we can do that too, sort of, can't turn off the compute." But I want to ask you Phil, so, >> Sure. >> it looks from my vantage point, like you're taking your Data Cloud message which was originally separate compute from storage simplification, now data sharing, automated governance, security, ultimately the marketplace. >> Phil: Right. >> Taking that same model, break down the silos into telecom, right? It's that same, >> Mm-hmm. >> sorry to use the term playbook, Frank Slootman tells me he doesn't use playbooks, but he's not a pattern matcher, but he's a situational CEO, he says. But the situation in telco calls for that type of strategy. So explain what you guys are doing in telco. >> I think there's, so, what we're launching, we launched last week, and it really was three components, right? So we had our platform as you mentioned, >> Dave: Mm-hmm. >> and that platform is being utilized by a number of different companies today. We also are adding, for telecom very specifically, we're adding capabilities in marketplace, so that service providers can not only use some of the data and apps that are in marketplace, but as well service providers can go and sell applications or sell data that they had built. And then as well, we're adding our ecosystem, it's telecom-specific. So, we're bringing partners in, technology partners, and consulting and services partners, that are very much focused on telecoms and what they do internally, but also helping them monetize new services. >> Okay, so it's not just sort of generic Snowflake into telco? You have specific value there. >> We're purposing the platform specifically for- >> Are you a telco guy? >> I am. You are, okay. >> Total telco guy absolutely. >> So there you go. You see that Snowflake is actually an interesting organizational structure, 'cause you're going after verticals, which is kind of rare for a company of your sort of inventory, I'll say, >> Absolutely. >> I don't mean that as a negative. (Dave laughs) So Dave, take us through the data journey at AT&T. It's a long history. You don't have to go back to the 1800s, but- (Dave laughs) >> Thank you for pointing out, we're a 149-year-old company. So, Jesse James was one of the original customers, (Dave laughs) and we have no longer got his data. So, I'll go back. I've been 17 years singular AT&T, and I've watched it through the whole journey of, where the monolithics were growing, when the consolidation of small, wireless carriers, and we went through that boom. And then we've gone through mergers and acquisitions. But, Hadoop came out, and it was going to solve all world hunger. And we had all the aspects of, we're going to monetize and do AI and ML, and some of the things we learned with Hadoop was, we had this monolithic warehouse, we had this file-based-structured Hadoop, but we really didn't know how to bring this all together. And we were bringing items over to the relational, and we were taking the relational and bringing it over to the warehouse, and trying to, and it was a struggle. Let's just go there. And I don't think we were the only company to struggle with that, but we learned a lot. And so now as tech is finally emerging, with the cloud, companies like Snowflake, and others that can handle that, where we can create, we were discussing earlier, but it becomes more of a conducive mesh that's interoperable. So now we're able to simplify that environment. And the cloud is a big thing on that. 'Cause you could not do this on-prem with on-prem technologies. It would be just too cost prohibitive, and too heavy of lifting, going back and forth, and managing the data. The simplicity the cloud brings with a smaller set of tools, and I'll say in the data space specifically, really allows us, maybe not a single instance of data for all use cases, but a greatly reduced ecosystem. And when you simplify your ecosystem, you simplify speed to market and data management. >> So I'm going to ask you, I know it's kind of internal organizational plumbing, but it'll inform my next question. So, Dave, you're with the Chief Data Office, and Roddy, you're kind of, you all serve in the business, but you're really serving the, you're closer to those guys, they're banging on your door for- >> Absolutely. I try to keep the 130,000 users who may or may not have issues sometimes with our data and metrics, away from Dave. And he just gets a call from me. >> And he only calls when he has a problem. He's never wished me happy birthday. (Dave and Phil laugh) >> So the reason I asked that is because, you describe Dave, some of the Hadoop days, and again love-hate with that, but we had hyper-specialized roles. We still do. You've got data engineers, data scientists, data analysts, and you've got this sort of this pipeline, and it had to be this sequential pipeline. I know Snowflake and others have come to simplify that. My question to you is, how is that those roles, how are those roles changing? How is data getting closer to the business? Everybody talks about democratizing business. Are you doing that? What's a real use example? >> From our perspective, those roles, a lot of those roles on my team for years, because we're all about efficiency, >> Dave: Mm-hmm. >> we cut across those areas, and always have cut across those areas. So now we're into a space where things have been simplified, data processes and copying, we've gone from 40 data processes down to five steps now. We've gone from five steps to one step. We've gone from days, now take hours, hours to minutes, minutes to seconds. Literally we're seeing that time in and time out with Snowflake. So these resources that have spent all their time on data engineering and moving data around, are now freed up more on what they have skills for and always have, the data analytics area of the business, and driving the business forward, and new metrics and new analysis. That's some of the great operational value that we've seen here. As this simplification happens, it frees up brain power. >> So, you're pumping data from the OSS, the BSS, the OKRs everywhere >> Everywhere. >> into Snowflake? >> Scheduling systems, you name it. If you can think of what drives our retail and centers and online, all that data, scheduling system, chat data, call center data, call detail data, all of that enters into this common infrastructure to manage the business on a day in and day out basis. >> How are the roles and the skill sets changing? 'Cause you're doing a lot less ETL, you're doing a lot less moving of data around. There were guys that were probably really good at that. I used to joke in the, when I was in the storage world, like if your job is bandaging lungs, you need to look for a new job, right? So, and they did and people move on. So, are you able to sort of redeploy those assets, and those people, those human resources? >> These folks are highly skilled. And we were talking about earlier, SQL hasn't gone away. Relational databases are not going away. And that's one thing that's made this migration excellent, they're just transitioning their skills. Experts in legacy systems are now rapidly becoming experts on the Snowflake side. And it has not been that hard a transition. There are certainly nuances, things that don't operate as well in the cloud environment that we have to learn and optimize. But we're making that transition. >> Dave: So just, >> Please. >> within the Chief Data Office we have a couple of missions, and Roddy is a great partner and an example of how it works. We try to bring the data for democratization, so that we have one interface, now hopefully know we just have a logical connection back to these Snowflake instances that we connect. But we're providing that governance and cleansing, and if there's a business rule at the enterprise level, we provide it. But the goal at CDO is to make sure that business units like Roddy or marketing or finance, that they can come to a platform that's reliable, robust, and self-service. I don't want to be in his way. So I feel like I'm providing a sub-level of platform, that he can come to and anybody can come to, and utilize, that they're not having to go back and undo what's in Salesforce, or ServiceNow, or in our billers. So, I'm sort of that layer. And then making sure that that ecosystem is robust enough for him to use. >> And that self-service infrastructure is predominantly through the Azure Cloud, correct? >> Dave: Absolutely. >> And you work on other clouds, but it's predominantly through Azure? >> We're predominantly in Azure, yeah. >> Dave: That's the first-party citizen? >> Yeah. >> Okay, I like to think in terms sometimes of data products, and I know you've mentioned upfront, you're Gold standard or Platinum standard, you're very careful about personal information. >> Dave: Yeah. >> So you're not trying to sell, I'm an AT&T customer, you're not trying to sell my data, and make money off of my data. So the value prop and the business case for Snowflake is it's simpler. You do things faster, you're in the cloud, lower cost, et cetera. But I presume you're also in the business, AT&T, of making offers and creating packages for customers. I look at those as data products, 'cause it's not a, I mean, yeah, there's a physical phone, but there's data products behind it. So- >> It ultimately is, but not everybody always sees it that way. Data reporting often can be an afterthought. And we're making it more on the forefront now. >> Yeah, so I like to think in terms of data products, I mean even if the financial services business, it's a data business. So, if we can think about that sort of metaphor, do you see yourselves as data product builders? Do you have that, do you think about building products in that regard? >> Within the Chief Data Office, we have a data product team, >> Mm-hmm. >> and by the way, I wouldn't be disingenuous if I said, oh, we're very mature in this, but no, it's where we're going, and it's somewhat of a journey, but I've got a peer, and their whole job is to go from, especially as we migrate from cloud, if Roddy or some other group was using tables three, four and five and joining them together, it's like, "Well look, this is an offer for data product, so let's combine these and put it up in the cloud, and here's the offer data set product, or here's the opportunity data product," and it's a journey. We're on the way, but we have dedicated staff and time to do this. >> I think one of the hardest parts about that is the organizational aspects of it. Like who owns the data now, right? It used to be owned by the techies, and increasingly the business lines want to have access, you're providing self-service. So there's a discussion about, "Okay, what is a data product? Who's responsible for that data product? Is it in my P&L or your P&L? Somebody's got to sign up for that number." So, it sounds like those discussions are taking place. >> They are. And, we feel like we're more the, and CDO at least, we feel more, we're like the guardians, and the shepherds, but not the owners. I mean, we have a role in it all, but he owns his metrics. >> Yeah, and even from our perspective, we see ourselves as an enabler of making whatever AT&T wants to make happen in terms of the key products and officers' trade-in offers, trade-in programs, all that requires this data infrastructure, and managing reps and agents, and what they do from a channel performance perspective. We still ourselves see ourselves as key enablers of that. And we've got to be flexible, and respond quickly to the business. >> I always had empathy for the data engineer, and he or she had to service all these different lines of business with no business context. >> Yeah. >> Like the business knows good data from bad data, and then they just pound that poor individual, and they're like, "Okay, I'm doing my best. It's just ones and zeros to me." So, it sounds like that's, you're on that path. >> Yeah absolutely, and I think, we do have refined, getting more and more refined owners of, since Snowflake enables these golden source data, everybody sees me and my organization, channel performance data, go to Roddy's team, we have a great team, and we go to Dave in terms of making it all happen from a data infrastructure perspective. So we, do have a lot more refined, "This is where you go for the golden source, this is where it is, this is who owns it. If you want to launch this product and services, and you want to manage reps with it, that's the place you-" >> It's a strong story. So Chief Data Office doesn't own the data per se, but it's your responsibility to provide the self-service infrastructure, and make sure it's governed properly, and in as automated way as possible. >> Well, yeah, absolutely. And let me tell you more, everybody talks about single version of the truth, one instance of the data, but there's context to that, that we are taking, trying to take advantage of that as we do data products is, what's the use case here? So we may have an entity of Roddy as a prospective customer, and we may have a entity of Roddy as a customer, high-value customer over here, which may have a different set of mix of data and all, but as a data product, we can then create those for those specific use cases. Still point to the same data, but build it in different constructs. One for marketing, one for sales, one for finance. By the way, that's where your data engineers are struggling. >> Yeah, yeah, of course. So how do I serve all these folks, and really have the context-common story in telco, >> Absolutely. >> or are these guys ahead of the curve a little bit? Or where would you put them? >> I think they're definitely moving a lot faster than the industry is generally. I think the enabling technologies, like for instance, having that single copy of data that everybody sees, a single pane of glass, right, that's definitely something that everybody wants to get to. Not many people are there. I think, what AT&T's doing, is most definitely a little bit further ahead than the industry generally. And I think the successes that are coming out of that, and the learning experiences are starting to generate momentum within AT&T. So I think, it's not just about the product, and having a product now that gives you a single copy of data. It's about the experiences, right? And now, how the teams are getting trained, domains like network engineering for instance. They typically haven't been a part of data discussions, because they've got a lot of data, but they're focused on the infrastructure. >> Mm. >> So, by going ahead and deploying this platform, for platform's purpose, right, and the business value, that's one thing, but also to start bringing, getting that experience, and bringing new experience in to help other groups that traditionally hadn't been data-centric, that's also a huge step ahead, right? So you need to enable those groups. >> A big complaint of course we hear at MWC from carriers is, "The over-the-top guys are killing us. They're riding on our networks, et cetera, et cetera. They have all the data, they have all the client relationships." Do you see your client relationships changing as a result of sort of your data culture evolving? >> Yes, I'm not sure I can- >> It's a loaded question, I know. >> Yeah, and then I, so, we want to start embedding as much into our network on the proprietary value that we have, so we can start getting into that OTT play, us as any other carrier, we have distinct advantages of what we can do at the edge, and we just need to start exploiting those. But you know, 'cause whether it's location or whatnot, so we got to eat into that. Historically, the network is where we make our money in, and we stack the services on top of it. It used to be *69. >> Dave: Yeah. >> If anybody remembers that. >> Dave: Yeah, of course. (Dave laughs) >> But you know, it was stacked on top of our network. Then we stack another product on top of it. It'll be in the edge where we start providing distinct values to other partners as we- >> I mean, it's a great business that you're in. I mean, if they're really good at connectivity. >> Dave: Yeah. >> And so, it sounds like it's still to be determined >> Dave: Yeah. >> where you can go with this. You have to be super careful with private and for personal information. >> Dave: Yep. >> Yeah, but the opportunities are enormous. >> There's a lot. >> Yeah, particularly at the edge, looking at, private networks are just an amazing opportunity. Factories and name it, hospital, remote hospitals, remote locations. I mean- >> Dave: Connected cars. >> Connected cars are really interesting, right? I mean, if you start communicating car to car, and actually drive that, (Dave laughs) I mean that's, now we're getting to visit Xen Fault Tolerance people. This is it. >> Dave: That's not, let's hold the traffic. >> Doesn't scare me as much as we actually learn. (all laugh) >> So how's the show been for you guys? >> Dave: Awesome. >> What're your big takeaways from- >> Tremendous experience. I mean, someone who doesn't go outside the United States much, I'm a homebody. The whole experience, the whole trip, city, Mobile World Congress, the technologies that are out here, it's been a blast. >> Anything, top two things you learned, advice you'd give to others, your colleagues out in general? >> In general, we talked a lot about technologies today, and we talked a lot about data, but I'm going to tell you what, the accelerator that you cannot change, is the relationship that we have. So when the tech and the business can work together toward a common goal, and it's a partnership, you get things done. So, I don't know how many CDOs or CIOs or CEOs are out there, but this connection is what accelerates and makes it work. >> And that is our audience Dave. I mean, it's all about that alignment. So guys, I really appreciate you coming in and sharing your story in "theCUBE." Great stuff. >> Thank you. >> Thanks a lot. >> All right, thanks everybody. Thank you for watching. I'll be right back with Dave Nicholson. Day four SiliconANGLE's coverage of MWC '23. You're watching "theCUBE." (gentle music)

>> And I'm here at Dell's Presence at MWC with vice president of marketing for telecom and Edge Computing, Aaron Chaisson. Aaron, how's it going? >> Doing great. How's it going today, Dave? >> It's going pretty well. Pretty excited about what you've got going here and I'm looking forward to getting the tour. You ready to take a closer look? >> Ready to do it. Let's go take a look! For us in the telecom ecosystem, it's really all about how we bring together the different players that are innovating across the industry to drive value for our CSP customers. So, it starts really, for us, at the ecosystem layer, bringing partners, bringing telecommunication providers, bringing (stutters) a bunch of different technologies together to innovate together to drive new value. So Paul, take us a little bit through what we're doing to- to develop and bring in these partnerships and develop our ecosystem. >> Uh, sure. Thank you Aaron. Uh, you know, one of the things that we've been focusing on, you know, Dell is really working with many players in the open telecom ecosystem. Network equipment providers, independent software vendors, and the communication service providers. And, you know, through our lines of business or open telecom ecosystem labs, what we want to do is bring 'em together into a community with the goal of really being able to accelerate open innovation and, uh, open solutions into the market. And that's what this community is really about, is being able to, you know, have those communications, develop those collaborations whether it's through, you know, sharing information online, having webinars dedicated to sharing Dell information, whether it's our next generation hardware portfolio we announced here at the show, our use case directory, our- how we're dealing with new service opportunities, but as well as the community to share, too, which I think is an exciting way for us to be able to, you know- what is the knowledge thing? As well as activities at other events that we have coming up. So really the key thing I think about, the- the open telecom ecosystem community, it's collaboration and accelerating the open industry forward. >> So- So Aaron, if I'm hearing this correctly you're saying that you can't just say, "Hey, we're open", and throw a bunch of parts in a box and have it work? >> No, we've got to work together to integrate these pieces to be able to deliver value, and, you know, we opened up a- (stutters) in our open ecosystem labs, we started a- a self-certification process a couple of months back. We've already had 13 partners go through that, we've got 16 more in the pipeline. Everything you see in this entire booth has been innovated and worked with partnerships from Intel to Microsoft to, uh, to (stutters) Wind River and Red Hat and others. You go all the way around the booth, everything here has partnerships at its core. And why don't we go to the next section here where we're going to be showing how we're pulling that all together in our open ecosystems labs to drive that innovation? >> So Aaron, you talked about the kinds of validation and testing that goes on, so that you can prove out an open stack to deliver the same kinds of reliability and performance and availability that we expect from a wireless network. But in the opens- in the open world, uh, what are we looking at here? >> Yeah absolutely. So one of the- one of the challenges to a very big, broad open ecosystem is the complexity of integrating, deploying, and managing these, especially at telecom scale. You're not talking about thousands of servers in one site, you're talking about one server in thousands of sites. So how do you deploy that predictable stack and then also manage that at scale? I'm going to show you two places where we're talkin' about that. So, this is actually representing an area that we've been innovating in recently around creating an integrated infrastructure and virtualization stack for the telecom industry. We've been doing this for years in IT with VxBlocks and VxRails and others. Here what you see is we got, uh, Dell hardware infrastructure, we've got, uh, an open platform for virtualization providers, in this case we've created an infrastructure block for Red Hat to be able to supply an infrastructure for core operations and Packet Cores for telecoms. On the other side of this, you can actually see what we're doing with Wind River to drive innovation around RAN and being able to simplify RAN- vRAN and O-RAN deployments. >> What does that virtualization look like? Are we talking about, uh, traditional virtual machines with OSs, or is this containerized cloud native? What does it look like? >> Yeah, it's actually both, so it can support, uh, virtual, uh-uh, software as well as containerized software, so we leverage the (indistinct) distributions for these to be able to deploy, you know, cloud native applications, be able to modernize how they're deploying these applications across the telecom network. So in this case with Red Hat, uh, (stutters) leveraging OpenShift in order to support containerized apps in your Packet Core environments. >> So what are- what are some of the kinds of things that you can do once you have infrastructure like this deployed? >> Yeah, I mean by- by partnering broadly across the ecosystem with VMware, with Red Hat, uh, with- with Wind River and with others, it gives them the ability to be able to deploy the right virtualization software in their network for the types of applications they're deploying. They might want to use Red Hat in their core, they may want to use Wind River in their RAM, they may want to use, uh, Microsoft or VMware for their- for their Edge workloads, and we allow them to be able to deploy all those, but centrally manage those with a common user interface and a common set of APIs. >> Okay, well I'm dying to understand the link between this and the Lego city that the viewers can't see, yet, but it's behind me. Let's take a look. >> So let's take a look at the Lego city that shows how we not deploy just one of these, but dozens or hundreds of these at scale across a cityscape. >> So Aaron, I know we're not in Copenhagen. What's all the Lego about? >> Yeah, so the Lego city here is to show- and, uh, really there's multiple points of Presence across an entire Metro area that we want to be able to manage if we're a telecom provider. We just talked about one infrastructure block. What if I wanted to deploy dozens of these across the city to be able to manage my network, to be able to manage, uh, uh- to be able to deploy private mobility potentially out into a customer enterprise environment, and be able to manage all of these, uh, very simply and easily from a common interface? >> So it's interesting. Now I think I understand why you are VP of marketing for both telecom and Edge. Just heard- just heard a lot about Edge and I can imagine a lot of internet of things, things, hooked up at that Edge. >> Yeah, so why don't we actually go over to another area? We're actually going to show you how one small microbrewery (stutters) in one of our cities nearby, uh, (stutters) my hometown in Massachusetts is actually using this technology to go from more of an analyzed- analog world to digitizing their business to be able to brew better beer. >> So Aaron, you bring me to a brewery. What do we have- what do we have going on here? >> Yeah, so, actually (stutters) about- about a year ago or so, I- I was able to get my team to come together finally after COVID to be able to meet each other and have a nice team event. One of those nights, we went out to dinner at a- at a brewery called "Exhibit 'A'" in Massachusetts, and they actually gave us a tour of their facilities and showed us how they actually go through the process of brewing beer. What we saw as we were going through it, interestingly, was that everything was analog. They literally had people with pen and paper walking around checking time and temperature and the process of brewing the beer, and they weren't asking for help, but we actually saw an opportunity where what we're doing to help businesses digitize what they're doing in their manufacturing floor can actually help them optimize how they build whatever product they're building, in this case it was beer. >> Hey Warren, good to meet you! What do we have goin' on? >> Yeah, it's all right. So yeah, basically what we did is we took some of their assets in the, uh, brewery that were completely manually monitored. People were literally walking around the floor with clipboards, writing down values. And we censorized the asset, in this case fermentation tanks and we measured the, uh, pressure and the temperature, which in fermentation are very key to monitor those, because if they get out of range the entire batch of beer can go bad or you don't get the consistency from batch to batch if you don't tightly monitor those. So we censorized the fermentation tank, brought that into an industrial I/O network, and then brought that into a Dell gateway which is connected 5G up to the cloud, which then that data comes to a tablet or a phone, which they, rather than being out on the floor and monitor it, can look at this data remotely at any time. >> So I'm not sure the exact date, the first time we have evidence of beer being brewed by humanity... >> Yep. >> But I know it's thousands of years ago. So it's taken that long to get to the point where someone had to come along, namely Dell, to actually digitally transform the beer business. Is this sort of proof that if you can digitally transform this, you can digitally transform anything? >> Absolutely. You name it, anything that's being manufactured, sold, uh, uh, taken care of, (stutters) any business out there that's looking to be able to be modernize and deliver better service to their customers can benefit from technologies like this. >> So we've taken a look at the ecosystem, the way that you validate architectures, we've seen an example of that kind of open architecture. Now we've seen a real world use case. Do you want to take a look a little deeper under the covers and see what's powering all of this? >> We just this week announced a new line of servers that power Edge and RAN use cases, and I want to introduce Mike to kind of take us through what we've been working on and really what the power of what this providing. >> Hey Mike, welcome to theCube. >> Oh, glad- glad to be here. So, what I'd really like to talk about are the three new XR series servers that we just announced last week and we're showing here at Mobile World Congress. They are all short depth, ruggedized, uh, very environmentally tolerant, and able to withstand, you know, high temperatures, high humidities, and really be deployed to places where traditional data center servers just can't handle, you know, due to one fact or another, whether it's depth or the temperature. And so, the first one I'd like to show you is the XR7620. This is, uh, 450 millimeters deep, it's designed for, uh, high levels of acceleration so it can support up to 2-300 watt, uh, GPUs. But what I really want to show you over here, especially for Mobile World Congress, is our new XR8000. The XR8000 is based on Intel's latest Sapphire Rapids technology, and this is- happens to be one of the first, uh, EE boost processors that is out, and basically what it is (stutters) an embedded accelerator that makes, uh, the- the processing of vRAN loads very, uh, very efficient. And so they're actually projecting a, uh, 3x improvement, uh, of processing per watt over the previous generation of processors. This particular unit is also sledded. It's very much like, uh, today's traditional baseband unit, so it's something that is designed for low TCO and easy maintenance in the field. This is the frew. When anything fails, you'll pull one out, you pop a new one in, it comes back into service, and the- the, uh, you know, your radio is- is, uh, minimally disrupted. >> Yeah, would you describe this as quantitative and qualitative in terms of the kinds of performance gains that these underlying units are delivering to us? I mean, this really kind of changes the game, doesn't it? It's not just about more, is it about different also in terms of what we can do? >> Well we are (stutters) to his point, we are able to bring in new accelerator technologies. Not only are we doing it with the Intel, uh, uh, uh, of the vRAN boost technologies, but also (stutters) we can bring it, too, but there's another booth here where we're actually working with our own accelerator cards and other accelerator cards from our partners across the industry to be able to deliver the price and performance capabilities required by a vRAN or an O-RAN deployment in the network. So it's not- it's not just the chip technology, it's the integration and the innovation we're doing with others, as well as, of course, the unique power cooling capabilities that Dell provides in our servers that really makes these the most efficient way of being able to power a network. >> Any final thoughts recapping the whole picture here? >> Yeah, I mean I would just say if anybody's, uh, i- is still here in Mobile World Congress, wants to come and learn what we're doing, I only showed you a small section of the demos we've got here. We've got 13 demos across on 8th floor here. Uh, for those of you who want to talk to us (stutters) and have meetings with us, we've got 13 meeting rooms back there, over 500 costumer partner meetings this week, we've got some whisper suites for those of you who want to come and talk to us but we're innovating on going forward. So, you know, there's a lot that we're doing, we're really excited, there's a ton of passion at this event, and, uh, we're really excited about where the industry is going and our role in it. >> 'Preciate the tour, Aaron. Thanks Mike. >> Mike: Thank you! >> Well, for theCube... Again, Dave Nicholson here. Thanks for joining us on this tour of Dell's Presence here at MWC 2023.

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (bright upbeat music plays) >> Welcome back to Barcelona, everybody. You're watching theCUBE's coverage of MWC '23, my name is Dave Vellante. Just left a meeting with the CEO of Cisco, Chuck Robbins, to meet with Jeetu Patel, who's our Executive Vice President and General Manager of security and collaboration at Cisco. Good to see you. >> You never leave a meeting with Chuck Robbins to meet with Jeetu Patel. >> Well, I did. >> That's a bad idea. >> Walked right out. I said, hey, I got an interview to do, right? So, and I'm excited about this. Thanks so much for coming on. >> Thank you for having me. It's a pleasure. >> So, I mean you run such an important part of the business. I mean, obviously the collaboration business but also security. So many changes going on in the security market. Maybe we could start there. I mean, there hasn't been a ton of security talk here Jeetu, because I think it's almost assumed. It was 45 minutes into the keynote yesterday before anybody even mentioned security. >> Huh. >> Right? And so, but it's the most important topic in the enterprise IT world. And obviously is important here. So why is it you think that it's not the first topic that people mention. >> You know, it's a complicated subject area and it's intimidating. And actually that's one of the things that the industry screwed up on. Where we need to simplify security so it actually gets to be relatable for every person on the planet. But, if you think about what's happening in security, it's not just important for business it's critical infrastructure that if you had a breach, you know lives are cost now. Because hospitals could go down, your water supply could go down, your electricity could go down. And so it's one of these things that we have to take pretty seriously. And, it's 51% of all breaches happen because of negligence, not because of malicious intent. >> It's that low. Interesting. I always- >> Someone else told me the same thing, that they though it'd be higher, yeah. >> I always say bad user behavior is going to trump good security every time. >> Every single time. >> You can't beat it. But, you know, it's funny- >> Jeetu: Every single time. >> Back, the earlier part of last decade, you could see that security was becoming a board level issue. It became, it was on the agenda every quarter. And, I remember doing some research at the time, and I asked, I was interviewing Robert Gates, former Defense Secretary, and I asked him, yeah, but we're getting attacked but don't we have the best offense? Can't we have the best technology? He said, yeah but we have so much critical infrastructure the risks to United States are higher. So we have to be careful about how we use security as an offensive weapon, you know? And now you're seeing the future of war involves security and what's going on in Ukraine. It's a whole different ballgame. >> It is, and the scales always tip towards the adversary, not towards the defender, because you have to be right every single time. They have to be right once. >> Yeah. And, to the other point, about bad user behavior. It's going now beyond the board level, to it's everybody's responsibility. >> That's right. >> And everybody's sort of aware of it, everybody's been hacked. And, that's where it being such a complicated topic is problematic. >> It is, and it's actually, what got us this far will not get us to where we need to get to if we don't simplify security radically. You know? The experience has to be almost invisible. And what used to be the case was sophistication had to get to a certain level, for efficacy to go up. But now, that sophistication has turned to complexity. And there's an inverse relationship between complexity and efficacy. So the simpler you make security, the more effective it gets. And so I'll give you an example. We have this great kind of innovation we've done around passwordless, right? Everyone hates passwords. You shouldn't have passwords in 2023. But, when you get to passwordless security, not only do you reduce a whole lot of friction for the user, you actually make the system safer. And that's what you need to do, is you have to make it simpler while making it more effective. And, I think that's what the future is going to hold. >> Yeah, and CISOs tell me that they're, you know zero trust before the pandemic was like, yeah, yeah zero trust. And now it's like a mandate. >> Yeah. >> Every CISO you talk to says, yes we're implementing a zero trust architecture. And a big part of that is that, if they can confirm zero trust, they can get to market a lot faster with revenue generating or critical projects. And many projects as we know are being pushed back, >> Yeah. >> you know? 'Cause of the macro. But, projects that drive revenue and value they want to accelerate, and a zero trust confirmation allows people to rubber stamp it and go faster. >> And the whole concept of zero trust is least privileged access, right? But what we want to make sure that we get to is continuous assessment of least privileged access, not just a one time at login. >> Dave: 'Cause things change so frequently. >> So, for example, if you happen to be someone that's logged into the system and now you start doing some anomalous behavior that doesn't sound like Dave, we want to be able to intercept, not just do it at the time that you're authenticating Dave to come in. >> So you guys got a good business. I mentioned the macro before. >> Yeah. >> The big theme is consolidating redundant vendors. So a company with a portfolio like Cisco's obviously has an advantage there. You know, you guys had great earnings. Palo Alto is another company that can consolidate. Tom Gillis, great pickup. Guy's amazing, you know? >> Love Tom. >> Great respect. Just had a little webinar session with him, where he was geeking out with the analyst and so- >> Yeah, yeah. >> Learned a lot there. Now you guys have some news, at the event event with Mercedes? >> We do. >> Take us through that, and I want to get your take on hybrid work and what's happening there. But what's going on with Mercedes? >> Yeah so look, it all actually stems from the hybrid work story, which is the future is going to be hybrid, people are going to work in mixed mode. Sometimes you'll be in the office, sometimes at home, sometimes somewhere in the middle. One of the places that people are working more and more from is their cars. And connected cars are getting to be a reality. And in fact, cars sometimes become an extension of your home office. And many a times I have found myself in a parking lot, because I didn't have enough time to get home and I was in a parking lot taking a conference call. And so we've made that section easier, because we have now partnered with Mercedes. And they aren't the first partner, but they're a very important partner where we are going to have Webex available, through the connected car, natively in Mercedes. >> Ah, okay. So I could take a call, I can do it all the time. I find good service, pull over, got to take the meeting. >> Yeah. >> I don't want to be driving. I got to concentrate. >> That's right. >> You know, or sometimes, I'll have the picture on and it's not good. >> That's right. >> Okay, so it'll be through the console, and all through the internet? >> It'll be through the console. And many people ask me like, how's safety going to work over that? Because you don't want to do video calls while you're driving. Exactly right. So when you're driving, the video automatically turns off. And you'll have audio going on, just like a conference call. But the moment you stop and put it in park, you can have video turned on. >> Now, of course the whole hybrid work trend, we, seems like a long time ago but it doesn't, you know? And it's really changed the security dynamic as well, didn't it? >> It has, it has. >> I mean, immediately you had to go protect new endpoints. And those changes, I felt at the time, were permanent. And I think it's still the case, but there's an equilibrium now happening. People as they come back to the office, you see a number of companies are mandating back to work. Maybe the central offices, or the headquarters, were underfunded. So what's going on out there in terms of that balance? >> Well firstly, there's no unanimous consensus on the way that the future is going to be, except that it's going to be hybrid. And the reason I say that is some companies mandate two days a week, some companies mandate five days a week, some companies don't mandate at all. Some companies are completely remote. But whatever way you go, you want to make sure that regardless of where you're working from, people can have an inclusive experience. You know? And, when they have that experience, you want to be able to work from a managed device or an unmanaged device, from a corporate network or from a Starbucks, from on the road or stationary. And whenever you do any of those things, we want to make sure that security is always handled, and you don't have to worry about that. And so the way that we say it is the company that created the VPN, which is Cisco, is the one that's going to kill it. Because what we'll do is we'll make it simple enough so that you don't, you as a user, never have to worry about what connection you're going to use to dial in to what app. You will have one, seamless way to dial into any application, public application, private application, or directly to the internet. >> Yeah, I got a love, hate with my VPN. I mean, it's protecting me, but it's in the way a lot. >> It's going to be simple as ever. >> Do you have kids? >> I do, I have a 12 year old daughter. >> Okay, so not quite high school age yet. She will be shortly. >> No, but she's already, I'm not looking forward to high school days, because she has a very, very strong sense of debate and she wins 90% of the arguments. >> So when my kids were that age, I've got four kids, but the local high school banned Wikipedia, they can't use Wikipedia for research. Many colleges, I presume high schools as well, they're banning Chat GPT, can't use it. Now at the same time, I saw recently on Medium a Wharton school professor said he's mandating Chat GPT to teach his students how to prompt in progressively more sophisticated prompts, because the future is interacting with machines. You know, they say in five years we're all going to be interacting in some way, shape, or form with AI. Maybe we already are. What's the intersection between AI and security? >> So a couple very, very consequential things. So firstly on Chat GPT, the next generation skill is going to be to learn how to go out and have the right questions to ask, which is the prompt revolution that we see going on right now. But if you think about what's happening in security, and there's a few areas which are, firstly 3,500 hundred vendors in this space. On average, most companies have 50 to 70 vendors in security. Not a single vendor owns more than 10% of the market. You take out a couple vendors, no one owns more than 5%. Highly fractured market. That's a problem. Because it's untenable for companies to go out and manage 70 policy engines. And going out and making sure that there's no contention. So as you move forward, one of the things that Chat GPT will be really good for is it's fundamentally going to change user experiences, for how software gets built. Because rather than it being point and click, it's going to be I'm going to provide an instruction and it's going to tell me what to do in natural language. Imagine Dave, when you joined a company if someone said, hey give Dave all the permissions that he needs as a direct report to Chuck. And instantly you would get all of the permissions. And it would actually show up in a screen that says, do you approve? And if you hit approve, you're done. The interfaces of the future will get more natural language kind of dominated. The other area that you'll see is the sophistication of attacks and the surface area of attacks is increasing quite exponentially. And we no longer can handle this with human scale. You have to handle it in machine scale. So detecting breaches, making sure that you can effectively and quickly respond in real time to the breaches, and remediate those breaches, is all going to happen through AI and machine learning. >> So, I agree. I mean, just like Amazon turned the data center into an API, I think we're now going to be interfacing with technology through human language. >> That's right. >> I mean I think it's a really interesting point you're making. Now, from a security standpoint as well, I mean, the state of the art today in my email is be careful, this person's outside your organization. I'm like, yeah I know. So it's a good warning sign, but it's really not automated in any way. So two part question. One is, can AI help? You know, with the phishing, obviously it can, but the bad guys have AI too. >> Yeah. >> And they're probably going to be smarter than I am about using it. >> Yeah, and by the way, Talos is our kind of threat detection and response >> Yes. >> kind of engine. And, they had a great kind of piece that came out recently where they talked about this, where Chat GPT, there is going to be more sophistication of the folks that are the bad actors, the adversaries in using Chat GPT to have more sophisticated phishing attacks. But today it's not something that is fundamentally something that we can't handle just yet. But you still need to do the basic hygiene. That's more important. Over time, what you will see is attacks will get more bespoke. And in order, they'll get more sophisticated. And, you will need to have better mechanisms to know that this was actually not a human being writing that to you, but it was actually a machine pretending to be a human being writing something to you. And that you'll have to be more clever about it. >> Oh interesting. >> And so, you will see attacks get more bespoke and we'll have to get smarter and smarter about it. >> The other thing I wanted to ask you before we close is you're right on. I mean you take the top security vendors and they got a single digit market share. And it's like it's untenable for organizations, just far too many tools. We have a partner at ETR, they do quarterly survey research and one of the things they do is survey emerging technology companies. And when we look at in the security sector just the number of emerging technology companies that are focused on cybersecurity is as many as there are out there already. And so, there's got to be consolidation. Maybe that's through M & A. I mean, what do you think happens? Are company's going to go out of business? There's going to be a lot of M & A? You've seen a lot of companies go private. You know, the big PE companies are sucking up all these security companies and may be ready to spit 'em out and go back public. How do you see the landscape? You guys are obviously an inquisitive company. What are your thoughts on that? >> I think there will be a little bit of everything. But the biggest change that you'll see is a shift that's going to happen with an integrated platform, rather than point solution vendors. So what's going to happen is the market's going to consolidate towards very few, less than a half a dozen, integrated platforms. We believe Cisco is going to be one. Microsoft will be one. There'll be others over there. But these, this platform will essentially be able to provide a unified kind of policy engine across a multitude of different services to protect multiple different entities within the organization. And, what we found is that platform will also be something that'll provide, through APIs, the ability for third parties to be able to get their technology incorporated in, and their telemetry ingested. So we certainly intend to do that. We don't believe, we are not arrogant enough to think that every single new innovation will be built by us. When there's someone else who has built that, we want to make sure that we can ingest that telemetry as well, because the real enemy is not the competitor. The real enemy is the adversary. And we all have to get together, so that we can keep humanity safe. >> Do you think there's been enough collaboration in the industry? I mean- >> Jeetu: Not nearly enough. >> We've seen companies, security companies try to monetize private data before, instead of maybe sharing it with competitors. And so I think the industry can do better there. >> Well I think the industry can do better. And we have this concept called the security poverty line. And the security poverty line is the companies that fall below the security poverty line don't have either the influence or the resources or the know how to keep themselves safe. And when they go unsafe, everyone else that communicates with them also gets that exposure. So it is in our collective interest for all of us to make sure that we come together. And, even if Palo Alto might be a competitor of ours, we want to make sure that we invite them to say, let's make sure that we can actually exchange telemetry between our companies. And we'll continue to do that with as many companies that are out there, because actually that's better for the market, that's better for the world. >> The enemy of the enemy is my friend, kind of thing. >> That's right. >> Now, as it relates to, because you're right. I mean I, I see companies coming up, oh, we do IOT security. I'm like, okay, but what about cloud security? Do you that too? Oh no, that's somebody else. But, so that's another stove pipe. >> That's a huge, huge advantage of coming with someone like Cisco. Because we actually have the entire spectrum, and the broadest portfolio in the industry of anyone else. From the user, to the device, to the network, to the applications, we provide the entire end-to-end story for security, which then has the least amount of cracks that you can actually go out and penetrate through. The biggest challenges that happen in security is you've got way too many policy engines with way too much contention between the policies from these different systems. And eventually there's a collision course. Whereas with us, you've actually got a broad portfolio that operates as one platform. >> We were talking about the cloud guys earlier. You mentioned Microsoft. They're obviously a big competitor in the security space. >> Jeetu: But also a great partner. >> So that's right. To my opinion, the cloud has been awesome as a first line of defense if you will. But the shared responsibility model it's different for each cloud, right? So, do you feel that those guys are working together or will work together to actually improve? 'Cause I don't see that yet. >> Yeah so if you think about, this is where we feel like we have a structural advantage in this, because what does a company like Cisco become in the future? I think as the world goes multicloud and hybrid cloud, what'll end up happening is there needs to be a way, today all the CSPs provide everything from storage to computer network, to security, in their own stack. If we can abstract networking and security above them, so that we can acquire and steer any and all traffic with our service providers and steer it to any of those CSPs, and make sure that the security policy transcends those clouds, you would actually be able to have the public cloud economics without the public cloud lock-in. >> That's what we call super cloud Jeetu. It's securing the super cloud. >> Yeah. >> Hey, thanks so much for coming to theCUBE. >> Thank you for having me. >> Really appreciate you coming on our editorial program. >> Such a pleasure. >> All right, great to see you again. >> Cheers. >> All right, keep it right there. Dave Vellante with David Nicholson and Lisa Martin. We'll be back, right after this short break from MWC '23 live, in the Fira, in Barcelona. (bright music resumes) (music fades out)

>> theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hey everyone, guys and gals, good to see you. It's theCUBE live in Barcelona at MWC23. Lisa Martin here with Dave Vellante on day one of four days of wall to wall CUBE coverage. Dave, today is ecosystem day. We've had some great conversations about why the open ecosystem is so important and some of the key players in it. >> Well and I'm in search of disruptors, so I'm looking for, okay, who are the network operators that are going to actually lean into the future and drive it and challenge the existing incumbents. We'll talk about that today. >> And we're going to be talking about that next. We've got one of our alumni back with us. Satish Iyer is here, the Vice President of Emerging Services at Dell. Great to have you back on the program. >> Thank you. >> Richard Leitao is with us as well, the Vice President of National Development at DISH Network. Welcome. >> Pleasure to be here. >> So, lots of, this is day one, the theme is velocity. I feel like the day has gone by so quickly. But Dell and DISH have partnered together on a multi-year initiative to build your nationwide cloud-native 5G network that's going to cover a lot of the US. Talk a little bit about that partnership, we'll get both of your perspectives. Richard, we'll start with you. >> Sure. So thank you again for having me. So DISH had the opportunity of, of going through this experience, of innovating once more. For the ones that know DISH, DISH is a company that was founded in 1980 by an innovator, a disruptor. Of course, in the course of the next 40 years, we had the opportunities of even disrupting ourselves. We launched our first satellite TV service. We then launched the first streaming, video streaming platform, disrupting our own satellite business. And since 2008, we have been acquiring Spectrum and, you know, Spectrum, the most valuable asset of a wireless operator. We felt that this was the right opportunity, having 5G , having O-RAN, and we decided to go full in in a greenfield project building national network, 5G O-RAN cloud-based network, one of a kind network in in the US and, and most of all, using O-RAN, it's very important to us, what, what it can bring and it can bring to DISH but to the entire ecosystem of, of this sector in the US. >> Satish, talk a little bit about the partnership from Dell's perspective and some of the unique advantages that Dell is delivering to DISH. >> Oh absolutely. Again, like Richard was saying, I mean the telecom network is being desegregated as we speak. You know, companies like DISH and everybody else is looking at what are the best-in-class technologies we can bring to the table. I would like to say that, you know, the cloud is coming to the telco world, right? A lot of us have seen the tremendous transformation in the cloud world in the last few years. Now, you know, DISH is a big enterprise company. As you know, you know, we are pretty strong within the cloud space and enterprise space. So what we try to work with DISH is Dell, is to bring to DISH is, you know, that notion of cloud scale and the cloud ecosystem into telecom, right? By means best-in-class infrastructure products, best-in-class software products, to allow somebody like DISH to innovate and incre, you know, basically expand and build their O-RAN network. So it's absolutely important for us as we build and get into the telecom space to work with somebody like DISH who's also disrupting as a carrier in that space. >> So it's early days for Open RAN but you've decided, "okay, we're all in". >> Yeah. >> Right? So (chuckling) you burn the bridge, as they say, "go for it". (Lisa chuckles) So when you talk to most people, they say, "okay, it's, it's, it's, it's immature." It's got to be able to get to the levels of, of the, the the hardened stack reliability. But of course it brings the advantage of flexibility and speed. Are you optimizing for one or the other right now? How are you dealing with that balance? >> Well, it, it's, it's not mature in the sense that most of operators that think about it, they have a legacy network. And in order to go full in on the O-RAN side, they need to scrap a lot of things that they have and honestly, they don't want, and it doesn't make sense. So being a greenfield operator, give us that advantage. Give us the advantage and, and desegregation, it's all about chip sets, boxes and software and the chip sets part and what I like the most in desegregation is the time of innovation. The time that we can use new chip sets coming into the market, the size of the boxes that we are using. Obviously our footprint onsite is much smaller than traditional carriers or proprietary systems. So all of that Dell has been critical in supporting us. Supporting us having the best chip sets, having the smallest footprint and, you know, the software, the cycle of innovation is much faster than in proprietary systems. So ma-, it's maturing. I'm glad to say that probably two years ago here O-RAN was more like a, a pilot type of technology. It is not, we are live, we are live for more than 30 million customers in the US and, you know, the performance levels are very similar to traditional networks. >> So you don't just buy a nationwide cloud-native 5G network out of the box, you got to- >> No, you don't. >> You got to build it. So I'm curious as to what Dell's role is in that, in that build out. >> Right? >> How and how, I'm really curious how to, how you would grade Dell but we'll get there. >> Yeah, I mean, look, yes, you don't. So I think the, the, the first and foremost is again, as, as we, Dell, comes into the telco space, one of the things we have to look at is to understand what makes Dell better in the enterprise space, right? It is the best-in-class infrastructure. It is the software ties together. As you talk about desegregated networks, it's important to understand lot of these piece parts have to still be touched together, right? So I think the integration and integration aspects becomes really key which is really Dell is very good at. So one of the things we are working really closely with DISH Tech, you know Richard was alluding to, is bringing all, not just bringing all the software and hardware assets together, but how do you continuously innovate and keep fixing things faster, right? So in the old days, traditional ways, you have a software stack, it takes you 18 months, 20 months to actually get an upgrade done. Here we have continuously CI/CD pipelines where if you want to a change done within, within a week's or within a few days, where we can actually go and test and make sure these things work. So I think a lot of the best enterprise software practices, cloud practices, combined with whatever needs for telco, actually is what makes it very unique. >> I, I saw that this started out as an FCC compliance initiative that turned into a partnership, obviously a very successful one. Richard, talk about what DISH saw in Dell that really made it the right choice, knowing you have choices, you have options. >> You know, we saw the capability to execute, but we also saw the capability to innovate. From an execution level, at the end of the day, like we were talking, we started the project in the middle of COVID, and we had the first mandate to cover 20% of the US population by June, 2022. And now we have a second one, 70% of US population by June 2023. At the beginning of the project, it was all about availability of materials, logistics, how to distribute, how to transport material. So Dell has a world-class supply chain, we felt that working with Dell through all these challenges made things easier. So from an execution perspective, whenever you need to build a network and you, you are building thousands of sites, you need to have materials, you need to distribute them and you need to install them. Dell helped us across the board. Our expectations obviously will change. We have a network, we want to cooperate with Dell in many other areas. We want to, you know, leverage on Dell ability to reach the enterprise market, to have private 5G offers. So hopefully this collaboration will endure in time and, and, you know, will change and evolve in time. >> And it's a big bet. I mean, it's not like a single, it's not like a little transaction that you guys are doing. I feel like, you know Michael Dell and Eric Carlson had dinner and they said, "okay, we're going to, we're going to partner up and this is going to be a multi-decade partnership. You had to be transparent, "Hey, we're new at this, even though we're really good at enterprise tech and so you're going to, obviously if you take a chance on us, here's what we promise you." >> Absolutely. >> And vice versa, you guys had to say, "all right, hey, we're willing to roll the dice because we're trying to change the world." So what was that dynamic like? I mean, how did, I'm curious as to this has to be a lot of different levels, engineering, senior management, board level discussions. >> You know, we felt a huge buy-in from Dell on the Open RAN concept. >> Right. >> Yeah, okay. >> And, you know, edge computing and, and the ability to get us the best product and evolve the best product, Intel is is critical in all these offerings. Intel has a great relationship with Dell. Dell helped us. Dell sponsored the DISH program and some of these suppliers, So it was definitely good to have their support and the buy-in on the O-RAN concept. We felt it from day one and we felt secure on that. >> Yeah, I mean, I, to add to that, I mean, you know DISH was very instrumental in driving, dictating and executing to our roadmap, right? They're one of the key, I mean, since they are out there and they're really turning in a way, it's important that a customer who's actually at the out front of innovation, helps us drive our own roadmap. So to Richard's point, a lot of our product roadmaps, in terms of what have you built and all that, was based on what DISH thinks as going to be market-based requirements. They also helped us a lot in the integration aspects. Like I said, one of the things about open desegregation of these networks is there is a lot of integration because, you know, there is, it's not a one, one monolithic pipe smokestack anymore. You are picking up best-in-class pieces, bits and pieces and tying it together. And it's important to understand when you tie it together things will go wrong, right? So there is a lot of learnings from an integration standpoint. Supportability, deployment, one of the things Richard talked about was supply chain, you know. Other Dell's ability to, lot of these deployments, a lot of these configs in the factory, right, in the second part. So especially a lot of these partnerships started during COVID time and as you all know, you know what we went through two years ago. So we had to make sure that lot of these things are done in one place and a factory, and not done in the field because we couldn't do a lot of these things. So there's a lot of, lot of experimentation, lot of, lot, lot of innovation on that. >> So it's 2030, what's this look like? What's the vision if we can work backwards from there? Well, a, a great network coverage to the entire country, bringing new services to enterprises, to verticals, bringing value add to customers and, you know, technology cycles, they are lasting much less than they were. I cannot even say what will happen in three years. 2030, I mean, I know, I know somebody has a vision for 2030. That's another thing. (everyone laughs) >> A lot of it is "build it and they will come", right? >> Yeah. >> I mean it really is right? You put that network in place and then innovation happens on top. That's the best thing. >> Yeah. And look and and I think the biggest people think about Open RAN in terms of cost, which, you know, you, you have some things in cost that you appreciate in Open RAN. The footprint, the the possibility to diversify suppliers and and have more competition. But for me, Open RAN is about innovation and cycles of innovation. I used to work for Nokia, I used to work for Alcatel. I knew from the generation of an idea to an execution and having a feature delivered to a certain customer, it, it took months. We want innovation to take weeks. We are innovating at the speed, speed of the cloud. We are cooperating with new players, players on the cloud and, and we expect things to happen much faster than they traditionally happen on the telecom sector. >> Move fast and break things. >> Well, we also expect that speed- >> Break and fix. (everyone laughs) >> Yeah, thank you for that. >> But speaking of speed, your customers expect that, right? They expect the service to be up 24/7. They expect to be able to access whatever content they want, whenever they want from wherever they are. So comment, Richard, in our last few minutes here of, of how the, the Dell partnership is helping DISH to really deliver the excellent customer experience that your customers just expect that you're going to deliver. >> Well by setting up the system, number one, we are leveraging on a number of services. And I mentioned the supply chain, but in reality Dell made much more than that for our 20% milestone and is supporting our 70% milestone by installing, testing, verifying most of our data center equipment. We found that this offering from Dell was really addressing some of our needs because, you know, we, we believe they know a lot in this area and they, they can provide the best advice and the best speed to market in, in terms of having this equipment. Because we are working on a time clock, we need to have this done as soon as possible. You know for the future, I hope that they can help us in driving more services. I hope they can bring all the infrastructure that we need to offer to our customers. And, you know, we keep committed to O-RAN. O-RAN is really important. We are not compromising that. And I think the future is bright for both of us. >> Yeah, and Dell learns from the experience. >> Exactly. >> Absolutely. >> There's got to be a catalyst for expanding your roadmap and vision in telecom. >> Yeah, I mean, like you said, I mean, you asked a 2030 question and I think that, you know, know six, seven years from now I think people should look at what DISH and Dell and say they were the trailblazers of make, bringing Open RAN to the market and making 5G a reality. I mean, you talk about 5G, but every 5G is on a different stages. I do think that this combination, this partnership has the best chance to be the first ones to actually have a truly Open RAN network to be successful in commercial. >> Awesome guys. Trailblazers, Dell and DISH. Well, we look forward to watching this story unfold. Thank you- >> Thank you. >> for joining Dave and me on the program today talking about what you're doing together. We appreciate it. >> Thanks for having us. >> Our pleasure. >> Thank you, bye. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Barcelona at MWC23. We'll be back after a short break, so we'll see you soon.

(light music) >> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Spain everyone. Lisa Martin here with theCUBE Dave Vellante, my co-host for the next four days. We're live in Barcelona, covering MWC23. This is only day one, but I'll tell you the theme of this conference this year is velocity. And I don't know about you Dave, but this day is flying by already. This is ecosystem day. We're going to have a great discussion on the ecosystem next. >> Well we're seeing the disaggregation of the hardened telco stack, and that necessitates an ecosystem open- we're going to talk about Open RAN, we've been talking about even leading up to the show. It's a critical technology enabler and it's compulsory to have an ecosystem to support that. >> Absolutely compulsory. We've got two guests here joining us, Gautam Bhagra, Vice President partnerships at Dell, and Scott Walker, Vice President of global Telco ecosystem at Wind River. Guys, welcome to the program. >> Nice to be here. >> Thanks For having us. >> Thanks for having us. >> So you've got some news, this is day one of the conference, there's some news, Gautam, and let's start with you, unpack it. >> Yeah, well there's a lot of news, as you know, on Dell World. One of the things we are very excited to announce today is the launch of the Open Telecom Ecosystems Community. I think Dave, as you mentioned, getting into an Open RAN world is a challenge. And we know some of the challenges that our customers face. To help solve for those challenges, Dell wants to work with like-minded partners and customers to build innovative solutions, and join go-to-market. So we are launching that today. Wind River is one of our flagship partners for that, and I'm excited to be here to talk about that as well. >> Can you guys talk a little bit about the partnership, maybe a little bit about Wind River so the audience gets that context? >> Sure, absolutely, and the theme of the show, Velocity, is what this partnership is all about. We create velocity for operators if they want to adopt Open RAN, right? We simplify it. Wind River as a company has been around for 40 years. We were part of Intel at one point, and now we're independent, owned by a company called Aptiv. And with that we get another round of investment to help continue our acceleration into this market. So, the Dell partnership is about, like I said, velocity, accelerating the adoption. When we talk to operators, they have told us there are many roadblocks that they face, right? Like systems integration, operating at scale. 'Cause when you buy a traditional radio access network solution from a single supplier, it's very easy. It's works, it's been tested. When you break these components apart and disaggregate 'em, as we talked about David, it creates integration points and support issues, right? And what Dell and Wind River have done together is created a cloud infrastructure solution that could host a variety of RAN workloads, and essentially create a two layer cake. What we're, overall, what we're trying to do is create a traditional RAN experience, with the innovation agility and flexibility of Open RAN. And that's really what this partnership does. >> So these work, this workload innovation is interesting to me because you've got now developers, you know, the, you know, what's the telco developer look like, you know, is to be defined, right? I mean it's like this white sheet of paper that can create all this innovation. And to do that, you've got to have, as I said earlier, an ecosystem. But you've got now, I'm interested in your Open RAN agenda and how you see that sort of maturity model taking place. 'Cause today, you got disruptors that are going to lean right in say "Hey, yeah, that's great." The traditional carriers, they have to have a, you know, they have to migrate, they have to have a hybrid world. We know that takes time. So what's that look like in the marketplace today? >> Yeah, so I mean, I can start, right? So from a Dell's perspective, what we see in the market is yes, there is a drive towards, everyone understands the benefits of being open, right? There's the agility piece, the innovation piece. That's a no-brainer. The question is how do we get there? And I think that's where partnerships become critical to get there, right? So we've been working with partners like Wind River to build solutions that make it easier for customers to start adopting some of the foundational elements of an open network. The, one of the purposes in the agenda of building this community is to bring like-minded developers, like you said like we want those guys to come and work with the customers to create new solutions, and come up with something creative, which no one's even thought about, that accelerates your option even quicker, right? So that's exactly what we want to do as well. And that's one of the reasons why we launched the community. >> Yeah, and what we find with a lot of carriers, they are used to buying, like I said, traditional RAN solutions which are provided from a single provider like Erickson or Nokia and others, right? And we break this apart, and you cloudify that network infrastructure, there's usually a skills gap we see at the operator level, right? And so from a developer standpoint, they struggle with having the expertise in order to execute on that. Wind River helps them, working with companies like Dell, simplify that bottom portion of the stack, the infrastructure stack. So, and we lifecycle manage it, we test- we're continually testing it, and integrating it, so that the operator doesn't have to do that. In addition to that, wind River also has a history and legacy of working with different RAN vendors, both disruptors like Mavenir and Parallel Wireless, as well as traditional RAN providers like Samsung, Erickson, and others soon to be announced. So what we're doing on the northbound side is making it easy by integrating that, and on the southbound side with Dell, so that again, instead of four or five solutions that you need to put together, it's simply two. >> And you think about today how we- how you consume telco services are like there's these fixed blocks of services that you can buy, that has to change. It's more like the, the app stores. It's got to be an open marketplace, and that's where the innovation's going to come in, you know, from the developers, you know, top down maybe. I don't know, how do you see that maturity model evolving? People want to know how long it's going to take. So many questions, when will Open RAN be as reliable. Does it even have to be? You know, so many interesting dynamics going on. >> Yeah, and I think that's something we at Dell are also trying to find out, right? So we have been doing a lot of good work here to help our customers move in that direction. The work with Dish is an example of that. But I think we do understand the challenges as well in terms of getting, adopting the technologies, and adopting the innovation that's being driven by Open. So one of the agendas that we have as a company this year is to work with the community to drive this a lot further, right? We want to have customers adopt the technology more broadly with the tier one, tier two telcos globally. And our sales organizations are going to be working together with Wind Rivers to figure out who's the right set of customers to have these conversations with, so we can drop, drive, start driving this agenda a lot quicker than what we've seen historically. >> And where are you having those customer conversations? Is that at the operator level, is it higher, is it both? >> Well, all operators are deploying 5G in preparation for 6G, right? And we're all looking for those killer use cases which will drive top line revenue and not just make it a TCO discussion. And that starts at a very basic level today by doing things like integrating with Juniper, for their cloud router. So instead of at the far edge cell site, having a separate device that's doing the routing function, right? We take that and we cloudify that application, run it on the same server that's hosting the RAN applications, so you eliminate a device and reduce TCO. Now with Aptiv, which is primarily known as an automotive company, we're having lots of conversations, including with Dell and Intel and others about vehicle to vehicle communication, vehicle to anything communication. And although that's a little bit futuristic, there are shorter term use cases that, like, vehicle to vehicle accident avoidance, which are going to be much nearer term than autonomous driving, for example, which will help drive traffic and new revenue streams for operators. >> So, oh, that's, wow. So many other things (Scott laughs) that's just opened up there too. But I want to come back to, sort of, the Open RAN adoption. And I think you're right, there's a lot of questions that that still have to be determined. But my question is this, based on your knowledge so far does it have to be as hardened and reliable, obviously has to be low latency as existing networks, or can flexibility, like the cloud when it first came out, wasn't better than enterprise IT, it was just more flexible and faster, and you could rent it. And, is there a similar dynamic here where it doesn't have to replicate the hardened stack, it can bring in new benefits that drive adoption, what are your thoughts on that? >> Well there's a couple of things on that, because Wind River, as you know, where our legacy and history is in embedded devices like F-15 fighter jets, right? Or the Mars Rover or the James Web telescope, all run Wind River software. So, we know about can't fail ultra reliable systems, and operators are not letting us off the hook whatsoever. It has to be as hardened and locked down, as secure as a traditional RAN environment. Otherwise they will (indistinct). >> That's table stakes. >> That's table stakes that gets us there. And when River, with our legacy and history, and having operator experience running live commercial networks with a disaggregated stack in the tens of thousands of nodes, understand what this is like because they're running live commercial traffic with live customers. So we can't fail, right? And with that, they want their cake and eat it too, right? Which is, I want ultra reliable, I want what I have today, but I want the agility and flexibility to onboard third party apps. Like for example, this JCNR, this Juniper Cloud-Native Router. You cannot do something as simple as that on a traditional RAN Appliance. In an open ecosystem you can take that workload and onboard it because it is an open ecosystem, and that's really one of the true benefits. >> So they want the mainframe, but they want (Scott laughs) the flexibility of the developer cloud, right? >> That's right. >> They want their, have their cake eat it too and not gain weight. (group laughs) >> Yeah I mean David, I come from the public cloud world. >> We all don't want to do that. >> I used to work with a public cloud company, and nine years ago, public cloud was in the same stage, where you would go to a bank, and they would be like, we don't trust the cloud. It's not secure, it's not safe. It was the digital natives that adopted it, and that that drove the industry forward, right? And that's where the enterprises that realized that they're losing business because of all these innovative new companies that came out. That's what I saw over the last nine years in the cloud space. I think in the telco space also, something similar might happen, right? So a lot of this, I mean a lot of the new age telcos are understanding the value, are looking to innovate are adopting the open technologies, but there's still some inertia and hesitancy, for the reasons as Scott mentioned, to go there so quickly. So we just have to work through and balance between both sides. >> Yeah, well with that said, if there's still some inertia, but there's a theme of velocity, how do you help organizations balance that so they trust evolving? >> Yeah, and I think this is where our solution, like infrastructure block, is a foundational pillar to make that happen, right? So if we can take away the concerns that the organizations have in terms of security, reliability from the fundamental elements that build their infrastructure, by working with partners like Wind River, but Dell takes the ownership end-to-end to make sure that service works and we have those telco grade SLAs, then the telcos can start focusing on what's next. The applications and the customer services on the top. >> Customer service customer experience. >> You know, that's an interesting point Gautam brings up, too, because support is an issue too. We all talk about when you break these things apart, it creates integration points that you need to manage, right? But there's also, so the support aspect of it. So imagine if you will, you had one vendor, you have an outage, you call that one vendor, one necktie to choke, right, for accountability for the network. Now you have four or five vendors that you have to work. You get a lot of finger pointing. So at least at the infrastructure layer, right? Dell takes first call support for both the hardware infrastructure and the Wind River cloud infrastructure for both. And we are training and spinning them up to support, but we're always behind them of course as well. >> Can you give us a favorite customer example of- that really articulates the value of the partnership and the technologies that it's delivering to customers? >> Well, Infra Block- >> (indistinct) >> Is quite new, and we do have our first customer which is LG U plus, which was announced yesterday. Out of Korea, small customer, but a very important one. Okay, and I think they saw the value of the integrated system. They don't have the (indistinct) expertise and they're leveraging Dell and Wind River in order to make that happen. But I always also say historically before this new offering was Vodafone, right? Vodafone is a leader in Europe in terms of Open RAN, been very- Yago and Paco have been very vocal about what they're doing in Open RAN, and Dell and Wind River have been there with them every step of the way. And that's what I would say, kind of, led up to where we are today. We learned from engagements like Vodafone and I think KDDI as well. And it got us where we are today and understanding what the operators need and what the impediments are. And this directly addresses that. >> Those are two very different examples. You were talking about TCO before. I mean, so the earlier example is, that's an example to me of a disruptor. They'll take some chances, you know, maybe not as focused on TCO, of course they're concerned about it. Vodafone I would think very concerned about TCO. But I'm inferring from your comments that you're trying to get the industry, you're trying to check the TCO box, get there. And then move on to higher levels of value monetization. The TCO is going to come down to how many humans it takes to run the network, is it not, is that- >> Well a lot of, okay- >> Or is it devices- >> So the big one now, particularly with Vodafone, is energy cost, right? >> Of course, greening the network. >> Two-thirds of the energy consumption in RAN is the the Radio Access Network. Okay, the OPEX, right? So any reductions, even if they're 5% or 10%, can save tens or hundreds of millions of dollars. So we do things creatively with Dell to understand if there's a lot of traffic at the cell site and if it's not, we will change the C state or P state of the server, which basically spins it down, so it's not consuming power. But that's just at the infrastructure layer. Where this gets really powerful is working with the RAN vendors like Samsung and Ericson and others, and taking data from the traffic information there, applying algorithms to that in AI to shut it down and spin it back up as needed. 'Cause the idea is you don't want that thing powered up if there's no traffic on it. >> Well there's a sustainability, ESG, benefit to that, right? >> Yes. >> And, and it's very compute intensive. >> A hundred percent. >> Which is great for Dell. But at the same time, if you're not able to manage that power consumption, the whole thing fails. I mean it's, because there's going to be so much data, and such a intense requirement. So this is a huge issue. Okay, so Scott, you're saying that in the TCO equation, a big chunk is energy consumption? >> On the OPEX piece. Now there's also the CapEx, right? And Open RAN solutions are now, what we've heard from our customers today, are they're roughly at parity. 'Cause you can do things like repurpose servers after the useful life for a lower demand application which helps the TCO, right? Then you have situations like Juniper, where you can take, now software that runs on the same device, eliminating at a whole other device at the cell site. So we're not just taking a server and software point of view, we're taking a whole cell site point of view as it relates to both CapEx and OPEX. >> And then once that infrastructure it really gets adopted, that's when the innovation occurs. The ecosystem comes in. Developers now start to think of new applications that we haven't thought of yet. >> Gautam: Exactly. >> And that's where, that's going to force the traditional carriers to respond. They're responding, but they're doing so very carefully right now, it's understandable why. >> Yeah, and I think you're already seeing some news in the, I mean Nokia's announcement yesterday with the rebranding, et cetera. That's all positive momentum in my opinion, right? >> What'd you think of the logo? >> I love the logo. >> I liked it too. (group laughs) >> It was beautiful. >> I thought it was good. You had the connectivity down below, You need pipes, right? >> Exactly. >> But you had this sort of cool letters, and then the the pink horizon or pinkish, it was like (Scott laughs) endless opportunity. It was good, I thought it was well thought out. >> Exactly. >> Well, you pick up on an interesting point there, and what we're seeing, like advanced carriers like Dish, who has one of the true Open RAN networks, publishing APIs for programmers to build in their 5G network as part of the application. But we're also seeing the network equipment providers also enable carriers do that, 'cause carriers historically have not been advanced in that way. So there is a real recognition that in order for these networks to monetize new use cases, they need to be programmable, and they need to publish standard APIs, so you can access the 5G network capabilities through software. >> Yeah, and the problem from the carriers, there's not enough APIs that the carriers have produced yet. So that's where the ecosystem comes in, is going to >> A hundred percent >> I think there's eight APIs that are published out of the traditional carriers, which is, I mean there's got to be 8,000 for a marketplace. So that's where the open ecosystem really has the advantage. >> That's right. >> That's right. >> That's right. >> Yeah. >> So it all makes sense on paper, now you just, you got a lot of work to do. >> We got to deliver. Yeah, we launched it today. We got to get some like-minded partners and customers to come together. You'll start seeing results coming out of this hopefully soon, and we'll talk more about it over time. >> Dave: Great Awesome, thanks for sharing with us. >> Excellent. Guys, thank you for sharing, stopping by, sharing what's going on with Dell and Wind River, and why the opportunity's in it for customers and the technological evolution. We appreciate it, you'll have to come back, give us an update. >> Our pleasure, thanks for having us. (Group talks over each other) >> All right, thanks guys >> Appreciate it. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, Live from MWC23 in Barcelona. theCUBE is the leader in live tech coverage. (upbeat music)

>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great questions, Dave. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)

>> Welcome back to the program. My name is Dave Valante and in this session, we're going to explore one of the more interesting topics of the day. IoT for Smart Factories. And with me are, Todd Edmunds,the Global CTO of Smart Manufacturing Edge and Digital Twins at Dell Technologies. That is such a cool title. (chuckles) I want to be you. And Dr. Aditi Banerjee, who's the Vice President, General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thanks Dave. >> Thank you. Great to be here. >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, Smart Factories, IIoT. Can you briefly explain, what is Industry 4.0 all about and why is it important for the manufacturing industry? >> Yeah. Sure, Dave. You know, it's been around for quite a while and it's gone by multiple different names, as you said. Industry 4.0, Smart Manufacturing, Industrial IoT, Smart Factory. But it all really means the same thing, its really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So, being much more efficient, implementing really good sustainability initiatives. And so, we really look at that by saying, okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time? So it's really not- it's not new. It's been around for a long time. What's new is that manufacturers are looking at this, not as a one-of, two-of individual Use Case point of view but instead they're saying, we really need to look at this holistically, thinking about a strategic investment in how we do this. Not to just enable one or two Use Cases, but enable many many Use Cases across the spectrum. I mean, there's tons of them out there. There's Predictive maintenance and there's OEE, Overall Equipment Effectiveness and there's Computer Vision and all of these things are starting to percolate down to the factory floor, but it needs to be done in a little bit different way and really to really get those outcomes that they're looking for in Smart Factory or Industry 4.0 or however you want to call it. And truly transform, not just throw an Industry 4.0 Use Case out there but to do the digital transformation that's really necessary and to be able to stay relevant for the future. I heard it once said that you have three options. Either you digitally transform and stay relevant for the future or you don't and fade into history. Like, 52% of the companies that used to be on the Fortune 500 since 2000. Right? And so, really that's a key thing and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah. So, Aditi, it's like digital transformation is almost synonymous with business transformation. So, is there anything you'd add to what Todd just said? >> Absolutely. Though, I would really add that what really drives Industry 4.0 is the business transformation. What we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right? For example, improving the downtime or decreasing the maintenance cycle of the equipments or improving the quality of products, right? So, I think these are lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So, Aditi, I wonder if I could stay with you and maybe this is a bit esoteric but when I first first started researching IoT and Industrial IoT 4.0, et cetera, I felt, well, there could be some disruptions in the ecosystem. I kind of came to the conclusion that large manufacturing firms, Aerospace Defense companies the firms building out critical infrastructure actually had kind of an incumbent advantage and a great opportunity. Of course, then I saw on TV somebody now they're building homes with 3D printers. It like blows your mind. So that's pretty disruptive. But, so- But they got to continue, the incumbents have to continue to invest in the future. They're well-capitalized. They're pretty good businesses, very good businesses but there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will, or this transformation that we're talking about. So, my question is, how are your customers preparing for this new era? What are the key challenges that they're facing in the the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for Greenfield Factories, right? That is where the investments are going directly into building the factories with the new technologies, with the new connectivities, right? For the machines, for example, Industrial IoT having the right type of data platforms to drive computational analytics and outcomes, as well as looking at Edge versus Cloud type of technologies, right? Those are all getting built in the Greenfield Factories. However, for the Install-Based Factories, right? That is where our customers are looking at how do I modernize these factories? How do I connect the existing machine? And that is where some of the challenges come in on the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security because now you are connecting the factories to each other. So, cybersecurity becomes top of mind, right? So, there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way. So, perhaps they start with the innovation program and then they look at the business case and they scale it up, right? >> Todd, I'm glad you did brought up security, because if you think about the operations technology folks, historically they air-gaped the systems, that's how they created security. That's changed. The business came in and said, 'Hey, we got to connect. We got to make it intelligence.' So, that's got to be a big challenge as well. >> It absolutely is, Dave. And, you know, you can no longer just segment that because really to get all of those efficiencies that we talk about, that IoT and Industrial IoT and Industry 4.0 promise, you have to get data out of the factory but then you got to put data back in the factory. So, no longer is it just firewalling everything is really the answer. So, you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the Cloud and what that means. And does it mean a continuum of Cloud all the way down to the Edge, right down to the factory? It absolutely does. Because no one approach has the answer to everything. The more you go to the Cloud the broader the attack surface is. So, what we're seeing is a lot of our customers approaching this from kind of that hybrid right ones run anywhere on the factory floor down to the Edge. And one of the things we're seeing too, is to help distinguish between what is the Edge and bridge that gap between, like, Dave, you talked about IT and OT and also help what Aditi talked about is the Greenfield Plants versus the Brownfield Plants that they call it, that are the legacy ones and modernizing those. It's great to kind of start to delineate what does that mean? Where's the Edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about really two Edges in a manufacturing floor. We talk about an Industrial Edge that sits... or some people call it a Far Edge or a Thin Edge, sits way down on that plant, consists of industrial hardened devices that do that connectivity. The hard stuff about how do I connect to this obsolete legacy protocol and what do I do with it? And create that next generation of data that has context. And then we see another Edge evolving above that, which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself; that helps figure out where we're going to run this? Does it connect to the Cloud? Do we run Applications On-Prem? Because a lot of times that On-Prem Application it needs to be done. 'Cause that's the only way that it's going to work because of security requirements, because of latency requirements performance and a lot of times, cost. It's really helpful to build that Multiple-Edge strategy because then you kind of, you consolidate all of those resources, applications, infrastructure, hardware into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new Applications, new Use Cases and become the foundation for DXC'S expertise and Applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the the digital equivalent of building the Hoover Dam. I mean, its.. so yeah. How long does a typical project take? I know it varies, but what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're- you know, like I said at the beginning, this is not new. Smart Factory and Industry 4.0 is not new. It's been, it's people have been trying to implement the Holy Grail of Smart Factory for a long time. And what we're seeing is a switch, a little bit of a switch or quite a bit of a switch to where the enterprises and the IT folks are having a much bigger say and they have a lot to offer to be able to help that complexity. So, instead of deploying a computer here and a Gateway there and a Server there, I mean, you go walk into any manufacturing plant and you can see Servers sitting underneath someone's desk or a PC in a closet somewhere running a critical production application. So, we're seeing the enterprise have a much bigger say at the table, much louder voice at the table to say, we've been doing this enterprise all the time. We know how to really consolidate, bring Hyper-Converged Applications, Hyper-Converged Infrastructure to really accelerate these kind of applications. Really accelerate the outcomes that are needed to really drive that Smart Factory and start to bring that same capabilities down into the Mac on the factory floor. That way, if you do it once to make it easier to implement, you can repeat that. You can scale that. You can manage it much easily and you can then bring that all together because you have the security in one centralized location. So, we're seeing manufacturers that first Use Case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way when that- Think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that what you've done in that one factory and then set. Let's make that across all the factories including the factory that we're in, then across the globe. That makes it much, much easier. You really do the hard work once and then repeat. Almost like cookie cutter. >> Got it. Thank you. >> Aditi, what about the skillsets available to apply these to these projects? You got to have knowledge of digital, AI, Data, Integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean, definitely. Lot different types of skillsets are needed from a traditional manufacturing skillset, right? Of course, the basic knowledge of manufacturing is important. But the digital skillsets like IoT, having a skillset in in different Protocols for connecting the machines, right? That experience that comes with it. Data and Analytics, Security, Augmented Virtual Reality Programming. Again, looking at Robotics and the Digital Twin. So, the... It's a lot more connectivity software, data-driven skillsets that are needed to Smart Factory to life at scale. And, you know, lots of firms are recruiting these types of resources with these skill sets to accelerate their Smart Factory implementation, as well as consulting firms like DXC Technology and others. We recruit, we train our talent to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to Industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC to bring these to market? >> Yeah, Dell and DXC have a very strong partnership and we work very closely together to create solutions, to create strategies and how we are going to jointly help our clients, right? So, areas that we have worked closely together is Edge Compute, right? How that impacts the Smart Factory. So, we have worked pretty closely in that area. We're also looked at Vision Technologies. How do we use that at the Edge to improve the quality of products, right? So, we have several areas that we collaborate in and our approaches that we want to bring solutions to our client and as well as help them scale those solutions with the right infrastructure, the right talent and the right level of security. So, we bring a comprehensive solution to our clients. >> So, Todd, last question. Kind of similar but different, you know. Why Dell, DXC, pitch me? What's different about this partnership? Where are you confident that you're going to be to deliver the best value to customers? >> Absolutely. Great question. You know, there's no shortage of Bespoke Solutions that are out there. There's hundreds of people that can come in and do individual Use Cases and do these things and just, and that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously Bespoke Solutions upfront, together. The power of our scalable enterprise grade structured industry standard infrastructure, as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, 30,000 people across the globe that are really, really good at that scalable infrastructure to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's again, not just one individual solutions it's all of the solutions that not just drive Use Cases but drive outcomes with those solutions. >> Yeah, you're right. The partnership has gone, I mean I first encountered it back in, I think it was 2010. May of 2010. We had guys both on the, I think you were talking about converged infrastructure and I had a customer on, and it was actually the manufacturing customer. It was quite interesting. And back then it was how do we kind of replicate what's coming in the Cloud? And you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation and love to have you back. >> Thank you so much. It was a pleasure speaking with you. I agree. >> All right, keep it right there for more discussions that educate and inspire on "The Cube."

(electronic music) (graphics whooshing) (graphics tinkling) >> Telecommunications is well north of a trillion-dollar business globally, that provides critical services on which virtually everyone on the planet relies. Dramatic changes are occurring in the sector, and one of the most important dimensions of this change is the underlying infrastructure that powers global telecommunications networks. Telcos have been thawing out, if you will, they're frozen infrastructure, modernizing. They're opening up, they're disaggregating their infrastructure, separating, for example, the control plane from the data plane, and adopting open standards. Telco infrastructure is becoming software-defined. And leading telcos are adopting cloud native microservices to help make developers more productive, so they can respond more quickly to market changes. They're embracing technology consumption models, and selectively leveraging the cloud where it makes sense. And these changes are being driven by market forces, the root of which stem from customer demand. So from a customer's perspective, they want services, and they want them fast. Meaning, not only at high speeds, but also they want them now. Customers want the latest, the greatest, and they want these services to be reliable and stable with high quality of service levels. And they want them to be highly cost-effective. Hello and welcome to this preview of Mobile World Congress 2023. My name is Dave Vellante, and at this year's event, theCUBE has a major presence at the show made possible by Dell Technologies, and with me to unpack the trends in telco, and look ahead to MWC23 are Dennis Hoffman, he's the Senior Vice President and General Manager of Dell's telecom business, and Aaron Chaisson, who is the Vice President of Telecom and Edge Solutions Marketing at Dell Technologies, gentlemen, welcome, thanks so much for spending some time with me. >> Thank you, Dave. >> Thanks, glad to be here. >> So, Dennis, let's start with you. Telcos in recent history have been slow to deliver and to monetize new services, and a large part because their purpose-built infrastructure could been somewhat of a barrier to responding to all these market forces. In many ways, this is what makes telecoms, really this market so exciting. So from your perspective, where is the action in this space? >> Yeah, the action Dave is kind of all over the place, partly because it's an ecosystem play. I think it's been, as you point out, the disaggregation trend has been going on for a while. The opportunity's been clear, but it has taken a few years to get all of the vendors, and all of the components that make up a solution, as well as the operators themselves, to a point where we can start putting this stuff together, and actually achieving some of the promise. >> So Aaron, for those who might not be as familiar with Dell's a activities in this area, here we are just ahead of Mobile World Congress, it's the largest event for telecoms, what should people know about Dell? And what's the key message to this industry? >> Sure, yeah, I think everybody knows that there's a lot of innovation that's been happening in the industry of late. One of the major trends that we're seeing is that shift from more of a vertically-integrated technology stack, to more of a disaggregated set of solutions, and that trend has actually created a ton of innovation that's happening across the industry, or along technology vendors and providers, the telecoms themselves. And so, one of the things that Dell's really looking to do is, as Dennis talked about, is build out a really strong ecosystem of partners and vendors that we're working closely together to be able to collaborate on new technologies, new capabilities that are solving challenges that the networks are seeing today. Be able to create new solutions built on those in order to be able to bring new value to the industry. And then finally, we want to help both partners, as well as our CSP providers activate those changes, so that they can bring new solutions to market, to be able to serve their customers. And so, the key areas that we're really focusing on with our customers is, technologies to help modernize the network, to be able to capitalize on the value of open architectures, and bring price performance to what they're expecting, and availability that they're expecting today. And then also, partner with the lines of business to be able to take these new capabilities, produce new solutions, and then deliver new value to their customers. >> Great, thank you, Aaron. So Dennis, you and I, known you for a number of years. I've watched you, you're are a trend spotter. You're a strategic thinker. I love now the fact that you're running a business that you had to go out and analyze, and now you got to make it happen. So, how would you describe Dell's strategy in this market? >> Well, it's really two things. And I appreciate the comment, I'm not sure how much of a trend spotter I am, but I certainly enjoy, and I think I'm fascinated by what's going on in this industry right now. Our two main thrusts, Dave, are first round, trying to catalyze that ecosystem, be a force for pulling together a group of folks, vendors that have been flying in fairly loose formation for a couple of years, to deliver the kinds of solutions that move the needle forward, and produce the outcomes that our network operator customers can actually buy and consume, and deploy, and have them be supported. The other thing is, there's a couple of very key technology areas that need to be advanced here. This ends up being a much anticipated year in telecom. Because of the delivery of some open infrastructure solutions that have being developed for years. With the Intel Sapphire Rapids program coming to market, we've of course got some purpose-built solutions on top of that for telecommunications networks. Some expanded partnerships in the area of multi-cloud infrastructure. And so, I would say the second main thrust is, we've got to bring some intellectual property to the party. It's not just about pulling the ecosystem together. But those two things together really form the twin thrusts of our strategy. >> Okay, so as you point out, you obviously not going to go alone in this market, it's way too broad, there's so many routes to market, partnerships, obviously very, very important. So, can you share a little bit more about the ecosystem and partners, maybe give some examples of some of the key partners that you'd be highlighting or working with, maybe at Mobile World Congress, or other activities this year? >> Yeah, absolutely. As Aaron touched on, I'm a visual thinker. The way I think about this thing is a very, very vertical architecture is tipping sideways. It's becoming horizontal. And all of the layers of that horizontal architecture are really where the partnerships are at. So, let's start at the bottom, silicon. The silicon ecosystem is very much focused on this market. And producing very specific products to enable open, high performance telecom networks. That's both in the form of host processors, as well as accelerators. One layer up, of course, is the stuff that we're known for, subsystems, compute storage, the hardware infrastructure that forms the foundation for telco clouds. A layer above that, all of the cloud software layer, the virtualization and containerization software, and all of the usual suspects there, all of whom are very good partners of ours, and we're looking to expand that pretty broadly this year. And then at the top of the layer cake, all of the network functions, all of the VNF's and CNF's that were once kind of the top of proprietary stacks, that are now opening up and being delivered, as well-formed containers that can run on these clouds. So, we're focusing on all of those, if you will, product partnerships, and there is a services wrapper around all of it. The systems integration necessary to make these systems part of a carrier's network, which of course, has been running for a long time, and needs to be integrated with in a very specific way. And so, all of that, together kind of forms the ecosystem, all of those are partners, and we're really excited about being at the heart of it. >> Interesting, it's not like we've never seen this movie before, which is, it's sort of repeating itself in telco. Aaron, you heard my little intro up front about the need to modernize infrastructure, I wonder if I could touch on another major trend, which we're seeing is the cloud, and I'm talkin' about not only public, but private and hybrid cloud. The public cloud is an opportunity, but it's also a threat for telcos. Telcom providers are lookin' to the public cloud for specific use cases, you think about like bursting for an iPhone launch or whatever. But at the same time, these cloud vendors, they're sort of competing with telcos. They're providing local zones, for example, sometimes trying to do an end run on the telco connectivity services, so telecom companies, they have to find the right balance between what they own and what they rent. And I wonder if you could add some color as to what you see in the market and what Dell specifically is doing to support these trends. >> Yeah, and I think the most important thing is what we're seeing, as you said, is these aren't things that we haven't seen before. And I think that telecom is really going through their own set of cloud transformations, and so, one of the hot topics in the industry now is, what is telco cloud? And what does that look like going forward? And it's going to be, as you said, a combination of services that they offer, services that they leverage. But at the end of the day, it's going to help them modernize how they deliver telecommunication services to their customers, and then provide value added services on top of that. From a Dell perspective, we're really providing the technologies to provide the underpinnings to lay a foundation on which that network can be built, whether that's best of breed servers that are built in design for the telecom environments. Recently, we announced our Infer block program, in partnering with virtualization providers, to be able to provide engineered systems that dramatically simplify how our customers can deploy, manage, and lifecycle manage throughout day two operations, an entire cloud environment. And whether they're using Red Hat, whether they're using Wind River, or VMware, or other virtualization layers, they can deploy the right virtualization layer at the right part of their network to support the applications they're looking to drive. And Dell is looking to solve how they simplify and manage all of that, both from a hardware, as well as on management software perspective. So, this is really what Dell's doing to, again, partner with the broader technology community, to help make that telco cloud a reality. >> Aaron, let's stay here for a second, I'm interested in some of the use cases that you're going after with customers. You've got Edge infrastructure, remote work, 5G, where's security fit, what are the focus areas for Dell, and can we double click on that a little bit? >> Yeah, I mean, I think there's two main areas of telecommunication industry that we're talking to. One, we've really been talking about the sort of the network buyer, how do they modernize the core, the network Edge, the RAN capabilities to deliver traditional telecommunication services, and modernize that as they move into 5G and beyond. I think the other side of the business is, telecoms are really looking from a line of business perspective to figure out how do they monetize that network, and be able to deliver value added services to their enterprise customers on top of these new networks. So, you were just touching on a couple of things that are really critical. In the enterprise space, AI and IoT is driving a tremendous amount of innovation out there, and there's a need for being able to support and manage Edge compute at scale, be able to provide connectivity, like private mobility, and 4G and 5G, being able to support things like mobile workforces and client capabilities, to be able to access these devices that are around all of these Edge environments of the enterprises. And telecoms are seeing as that, as an opportunity for them to not only provide connectivity, but how do they extend their cloud out into these enterprise environments with compute, with connectivity, with client and connectivity resources, and even also provide protection for those environments as well. So, these are areas that Dell is historically very strong at. Being able to provide compute, be able to provide connectivity, and being able to provide data protection and client services, we are looking to work closely with lines of businesses to be able to develop solutions that they can bring to market in combination with us, to be able to serve their end user customers and their enterprises. So, those are really the two key areas, not only network buyer, but being able to enable the lines of business to go and capitalize on the services they're developing for their customers. >> I think that line of business aspect is key, I mean, the telcos have had to sit back and provide the plumbing, cost per bit goes down, data consumption going through the roof, all the over at the top guys have had the field day with the data, and the customer relationships, and now it's almost like the revenge (chuckles) of the telcos. Dennis, I wonder if we could talk about the future. What can we expect in the years ahead from Dell, if you break out the binoculars a little bit. >> Yeah, I think you hit it earlier. We've seen the movie before. This has happened in the IT data center. We went from proprietary vertical solutions to horizontal open systems. We went from client server to software-defined open hardware cloud native. And the trend is likely to be exactly that, in the telecom industry because that's what the operators want. They're not naive to what's happened in the IT data center, they all run very large data centers. And they're trying to get some of the scale economies. Some of the agility, the cost of ownership benefits for the reasons Aaron just discussed. It's clear as you point out, this industry's been really defined by the inability to stop investing, and the difficulty to monetize that investment. And I think now, everybody's looking at this 5G, and frankly, 5G plus 6G, and beyond, as the opportunity to really go get a chunk of that revenue, and Enterprise Edge is the target. >> And 5G is touching so many industries, and that kind of brings me, Aaron into Mobile World Congress. I mean, you look at the floor layout, it's amazing. You got Industry 4.0, you've got our traditional industry and telco colliding. There's public policy. So, give us a teaser to Mobile World Congress 23, what's on deck at the show from Dell? >> Yeah, we're really excited about Mobile World Congress. This, as you know, is a massive event for the industry every year. And it's really the event that the whole industry uses to kick off this coming year. So, we're going to be using this obviously to talk to our customers and our partners about what Dell's looking to do, and what we're innovating on right now, and what we're looking to partner with them around. In the front of the house, we're going to be doin', we're going to be highlighting 13 different solutions and demonstrations to be able to show our customers what we're doing today, and show them the use cases, and put into action, so they get to actually look and feel, and touch, and experience what it is that we're working around. Obviously, meetings are important, everybody knows Mobile World Congress is the place to get those meetings and kickoff for the year. So, we're going to have, we're lookin' at several hundred meetings, hundreds of meetings that we're going to be lookin' to have across the industry with our customers and partners in the broader community. And of course, we've also got technology that's going to be in a variety of different partner spaces as well. So, you can come and see us in hall three, but we're also going to have technologies, kind of spread all over the floor. And of course, there's always theCUBE. You're going to be able to see us live all four days, all day, every day. You're going to be hearing our executives, our partners, our customers, talk about what Dell is doing to innovate in the industry, and how we're looking to leverage the broader, open ecosystem to be able to transform the network, and what we're lookin' to do. So, in that space, we're going to be focusing on what we're doing from an ecosystem perspective, our infrastructure focus. We'll be talking about what we're doing to support telco cloud transformation. And then finally, as we talked about earlier, how are we helping the lines of business within our telecoms monetize the opportunity? So, these are all different things we're really excited to be focusing on, and look forward to the event next month. >> Yeah, it's going to be awesome in Barcelona at the FITA, as you say, Dell's big presence in hall three, Orange is in there, Deutsche Telecom, Intel's in hall three. VMware's there, Nokia, Vodafone, you got some great things to see there. Check that out, and of course, theCUBE, we are super excited to be collaborating with you, we got a great setup. We're in the walkway right between halls four and five, right across from the government of Catalonia, who are the host partners for the event, so there's going to be a ton of action there. Guys, can't wait to see you there, really appreciate your time today. >> Great, thanks. >> Alright, Mobile World Congress, theCUBE's coverage starts on February 27th right after the keynotes. So, first thing in the morning, east coast time, we'll be broadcasting is, Aaron said all week, Monday through Thursday in the show floor, check that out at thecube.net. siliconangle.com has all the written coverage, and go to dell.com, see what's happenin' there, have all the action from the event. Don't miss us, this is Dave Vellante, we'll see you there. (electronic music)

>> Welcome back to the program. My name is Dave Vellante and in this session we're going to explore one of the more interesting topics of the day. IoT for smart factories and with me are Todd Edmunds, the global CTO of Smart Manufacturing, Edge and Digital Twins, at Dell Technologies. That is such a cool title. (Todd laughs) I want to be you. And Dr. Aditi Banerjee, who's the Vice President General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thanks Dave. >> Thank you. Great to be here. >> Well- >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, smart factories, IIoT. Can you briefly explain, like, what is Industry 4.0 all about and why is it important for the manufacturing industry? >> Yeah, sure Dave. You know, it's been around for quite a while and it's got, it's gone by multiple different names. As you said, Industry 4.0, smart manufacturing, industrial IoT, smart factory. But it all really means the same thing. It's really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So being much more efficient. Implementing really good sustainability initiatives. And so we really look at that by saying, "Okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time"? So it's really not, it's not new. It's been around for a long time. What's new is that manufacturers are looking at this, not as a one-off, two off individual use case point of view, but instead they're saying, "We really need to look at this holistically, thinking about a strategic investment in how we do this." Not to just enable one or two use cases, but enable many, many use cases across the spectrum. I mean, there's tons of 'em out there. There's predictive maintenance and there's OEE, overall equipment effectiveness, and there's computer vision. And all of these things are starting to percolate down to the factory floor, but it needs to be done in a little bit different way. And really to to really get those outcomes that they're looking for in smart factory, or Industry 4.0, or however you want to call it. And truly transform. Not just throw an Industry 4.0 use case out there, but to do the digital transformation that's really necessary and to be able to stay relevant for the future. You know, I heard it once said that you have three options. Either you digitally transform and stay relevant for the future or you don't and fade into history like 52% of the companies that used to be on the Fortune 500 since 2000, right. And so really that's a key thing and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah, so Aditi, that's like digital transformation is almost synonymous with business transformation. So is there anything you'd add to what Todd just said? >> Absolutely, though, I would really add that what really drives Industry 4.0 is the business transformation. What we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right. For example, improving the downtime, you know, or decreasing the maintenance cycle of the equipments or improving the quality of products, right. So I think these are lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So Aditi, one, if I could stay with you and maybe this is a bit esoteric, but when I first started researching IoT and Industrial IoT 4.0, et cetera, I felt, you know, while there could be some disruptions in the ecosystem, I kind of came to the conclusion that large manufacturing firms, aerospace defense companies, the firms building out critical infrastructure, actually had kind of an incumbent advantage and a great opportunity. Of course, then I saw on TV, somebody now, they're building homes with 3D printers. It like blows your mind. So that's pretty disruptive. But. So, but they got to continue, the incumbents have to continue to invest in the future. They're well capitalized. They're pretty good businesses. Very good businesses. But there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will. Or there's transformation that we're talking about. So my question is how are your customers preparing for this new era? What are the key challenges that they're facing in the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for greenfield factories, right. That is where the investments are going directly into building the factories with the new technologies with the new connectivities, right, for the machines, for example. Industry IoT, Having the right type of data platforms to drive computational analytics and outcomes, as well as looking at edge versus cloud type of technologies, right. Those are all getting built in the greenfield factories. However, for the install-based factories, right, that is where our customers are looking at how do I modernize, right. These factories. How do I connect the existing machine? And that is where some of the challenges come in on, you know, the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security, right, because now you are connecting the factories to each other, right. So cybersecurity becomes top of mind, right. So there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way, right. So perhaps they start with the innovation program. And then they look at the business case and they scale it up, right. >> Todd, I'm glad Aditi brought up security because if you think about the operations technology, you know folks, historically they air gapped, you know, the systems. That's how they created security. That's changed. The business came in and said, "Hey, we got to connect. We got to make it intelligent." So that's got to be a big challenge as well. >> It absolutely is Dave. And, you know, you can no longer just segment that because really to get all of those efficiencies that we talk about, that IOT and industrial IoT and Industry 4.0 promise, you have to get data out of the factory but then you got to put data back in the factory. So no longer is it just firewalling everything is really the answer. So you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the cloud and what that means. And does it mean a continuum of cloud all the way down to the edge, right down to the factory? It absolutely does because no one approach has the answer to everything. The more you go to the cloud, the broader the attack surface is. So what we're seeing is a lot of our customers approaching this from, kind of, that hybrid, you know, write once, run anywhere on the factory floor down to the edge. And one of things we're seeing too is to help distinguish between what is the edge and that. And bridge that gap between, like Dave, you talked about IT and OT, and also help that what Aditi talked about is the greenfield plants versus the brownfield plants, that they call it, that are the legacy ones and modernizing those, is it's great to kind of start to delineate. What does that mean? Where's the edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about, really, two edges in a manufacturing floor. We talk about an industrial edge that sits, or some people call it a far edge or a thin edge, sits way down on that plant. Consists of industrial hardened devices that do that connectivity, the hard stuff, about how do I connect to this obsolete legacy protocol and what do I do with it? And create that next generation of data that has context. And then we see another edge evolving above that which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself that helps figure out where we're going to run this. Is... Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is needs to be done because that's the only way it's going to work. Because of security requirements. Because of latency requirements, performance, and a lot of times, cost. It's really helpful to build that multiple edge strategy because then you consolidate all of those resources, applications, infrastructure, hardware, into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new applications, new use cases, and become the foundation for DXC's expertise in applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the digital equivalent of building the Hoover Dam. I mean, it... So, yeah, how long does a typical project take? I know it varies, but what, you know, what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're, you know, like I said at the beginning, this is not new smart factory and Industry 4.0 is not new. It's been... It's people have been trying to implement the holy grail of smart factory for a long time. And what we're seeing is a switch, a little bit of a switch or quite a bit of a switch, to where the enterprise and the IT folks are having a much bigger say and have a lot to offer to be able to help that complexity. So instead of deploying a computer here and a gateway there and a server there. I mean, you go walk into any manufacturing plant and you can see servers sitting underneath someone's desk or a PC in a closet somewhere running a a critical production application. So we're seeing the enterprise have a much bigger say at the table. Much louder voice at the table to say, "We've been doing this enterprise all the time. We know how to really consolidate, bring hyper-converged applications, hyper-converged infrastructure, to really accelerate these kind of applications. Really accelerate the outcomes that are needed to really drive that smart factory." And start to bring that same capabilities down into the Mac on the factory floor. That way, if you do it once to make it easier to implement you can repeat that. You can scale that. You can manage it much easily. And you can then bring that all together because you have the security in one centralized location. So we're seeing manufacturers... Yeah, that first use case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way, when that... Think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that what you've done in that one factory and then set. Let's that, make that across all the factories including the factory that we're in, but across the globe. That makes it much, much easier. You really do the hard work once and then repeat almost like a cookie cutter. >> Got it, thank you. Aditi, what about the skillsets available to apply these to these projects? You got to have knowledge of digital, AI, data, integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean, definitely. Different types of skillsets are needed from a traditional manufacturing skillset, right. Of course, the basic knowledge of manufacturing is important. But the digital skillsets, like, you know, IoT. Having a skillset in different protocols for connecting the machines, right. That experience that comes with it. Data and analytics, security, augmented virtual reality, programming. You know, again, looking at robotics and the digital twin. So, you know, it's a lot more connectivity software data-driven skillsets that are needed to smart factory to life at scale. And, you know, lots of firms are, you know, recruiting these types of resources with these skillsets to, you know, accelerate their smart factory implementation as well as consulting firms like DXC technology and others. We recruit. We train our talent to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC, to bring these to market? >> Yeah, I... Dell and DXC have a very strong partnership, you know, and we work very closely together to create solutions, to create strategies, and how we are going to jointly help our clients, right. So. Areas that we have worked closely together is edge compute, right. How that impacts the smart factory. So we have worked pretty closely in that area. We're also looked at vision technologies, you know. How do we use that at the edge to improve the quality of products, right. So we have several areas that we collaborate in and our approach is that we want to bring solutions to our client and as well as help them scale those solutions with the right infrastructure, the right talent, and the right level of security. So we bring a comprehensive solution to our clients. >> So, Todd, last question. Kind of similar but different. You know, why Dell DXC? Pitch me. What's different about this partnership? You know, where are you confident that, you know, you're going to deliver the best value to customers? >> Absolutely, great question. You know, there's no shortage of bespoke solutions that are out there. There's hundreds of people that can come in and do individual use cases and do these things and just... And that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously bespoke solutions upfront, together. Right. The power of our scalables, enterprise grade, structured, you know, industry standard infrastructure as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, what, 30,000 people across the globe that are really, really good at that scalable infrastructure to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's, again, not just one individual solutions. It's all of the solutions that not just drive use cases but drive outcomes with those solutions. >> Yeah, you're right. The partnership has gone... I mean, I first encountered it back in, I think, it was 2010, May of 2010. We had you guys both on the queue... I think we were talking about converged infrastructure and I had a customer on, and it was actually manufacturing customer. Was quite interesting. And back then it was how do we kind of replicate what's coming in the cloud? And you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation. And love to have you back. >> Thank you so much. >> Absolutely. >> It was a pleasure speaking with you. >> I agree. >> All right, keep it right there for more discussions that educate and inspire on theCUBE.

(bright, upbeat music) >> Hello, brilliant humans and welcome to this special programming on theCUBE featuring Women of the Cloud, brought to you by AWS. My name is Savannah Peterson, and I am very excited to be joined by a brilliant woman both in supply chain as well as digital transformation. Please welcome Jeanette Barlow, VP of Product at Instacart. Jeanette, thank you so much for joining us from Boston today. How you doing? >> Thank you. I'm doing well, thank you. And thank you to the Amazon team for letting me join you. I'm excited to participate in this. I think it's such an important topic to learn all about how as women we're helping shape the future of business, supply chain, consumer experiences. So thank you very much. >> That's fantastic to have you and to be really celebrating women of the cloud properly. To start us off, how long, let's just, let's run with this. How long have you been a woman of the cloud? (Jeanette and Savannah laugh) >> Oh, probably since there, before there was a cloud, actually I have spent my entire career in enterprise technology and I spent nearly 25 years actually with IBM. And, you know, I remember when the internet really took off as far as a highly accessible thing and then the very beginnings of e-commerce where it was really the wild west and it was such a different experience than you get now. And I've been very fortunate throughout that journey to have a variety of roles from sales, marketing, communications. I eventually landed in product management and that's pretty much where I stayed. >> Savannah: At least for now. >> At least for now. >> Sounds like you're very curious. I can tell that you are a very curious person. Since you've been around for what I would consider a, an impressive period of time in an industry, especially when there were not a ton of women to reference or receive mentorship from, what was the initial catalyst or spark or inspiration for you to pursue a career in technology? >> I'll be really honest, getting out of college with college debt, money. (Savannah laughs) The best salary, I'm not going to sugarcoat that but once I landed there, it just was so amazing how technological advance advances were fundamentally changing the way businesses would work or how humans could get things done. And that whole, my whole career trajectory has been very much working at the forefront of new areas whether that be collaboration, software or supply chain which is, obviously we're all well aware, such a deep and important area and even low-code workflow automation before I came to Instacart. >> I love the transparency there. It's a indicator of a great leader and that level of authenticity. Were there any hurdles that you felt you had to overcome in the beginning or was the curiosity enough to power through the initial first few years that are always tough for anyone, no matter their gender or career? >> I think I was a very fortunate person. I do want to say that, sure, there are a lot of long hours and I often felt that I had to be more prepared, maybe than some of my colleagues that were men back, way back in the day. But I had the very good fortune of working for companies throughout my history that really believed in an equitable and respectful workplace. And I had wonderful mentors, both women and men, along the way who really were there to help develop talent. So I never felt that I had sort of a glass ceiling. I definitely felt that I had to to sit there and assert a point of view, at times. >> Savannah: Mm-Hm. >> But, I've seen this whole industry and space change and it's not just gender, but also racial backgrounds educational backgrounds, that neurodiversity I'm now seeing much greater respect for listening to that chorus of voices because we do get better, much better outcomes that way. >> Absolutely. I couldn't agree more and I'm happy to hear that you've been supported along your journey. I think the industry can definitely get a bad rap and there are a lot of people paving the way for us. I want to talk a little bit about supply chain because I don't know about you, but for me I don't think there were as many people talking about the industry and probably what you do, say four years ago, as are now. How did you find your way into supply chain and what is it about helping that be more efficient that excites you? >> Yes. There's nothing like a shortage of toilet paper to get people to. (Savannah laughs) Or to understand what supply chain means. And I, as tough as those times were, especially at the beginning of the pandemic and the uncertainty, it was so exciting for those of us in supply chain because suddenly people got what we did like- >> Savannah: Mm-Hm. >> And they were interested in hearing about it. So I really, I really have, we did enjoy that. I got exposed to that because ultimately I served as the Vice President of Product Management and Strategy for IBM, Sterling Supply Chain which was a very large brand within the IBM portfolio, serving over 10,000 clients worldwide, really focused on their omnichannel order management and their other supply chain processes around order to cash, procure to pay, logistics and things like that. And when you start to learn about the intricacies and that choreography needed across so many players in the value chain, it's an absolutely fascinating puzzle. And- >> Savannah: Yeah. >> Often the further away from the consumer experience you got, the more analog it became. And so the opportunity to start to digitize and transform that was really something that was very, very intriguing. And now here at Instacart, the opportunity to sort of parlay that into one of probably the most complex supply chains that there are, grocery, food just adds another level- >> Yeah. >> Of excitement intrigue to the work. >> I can only imagine there are, I'm just thinking about it right now. I'm not sure there are many supply chains, if any that touch as many lives as food does, as, I mean so is that what brought you, you joined Instacart relatively recently if I'm not mistaken, within the last year. Is that what brought you to them? Was the complexity of that global challenge? >> Absolutely. That was definitely the start of it, was so intriguing to me to see, to, the more I learned about Instacart when they approached me was also they're really changing an industry that's been very static for many, many years, right? And they're fundamentally reshaping that industry. One that's, as you said, is crucial to the everyday lives of pretty much everyone. And I was intrigued by that. But I was also intrigued by the breadth at which they're approaching this, not just the marketplace, but how we are helping retailers through our Instacart platform actually reach their consumers in ways that they like to shop whether it's online or in the store. We are also very, very committed to not just serving from a convenience standpoint, but actually improving access to healthy and nutritious food for as many people as might need that. So it just, core to the complexity of the problem the criticality of it, but also just frankly speaking to the core of who Instacart is as a company, I, it just felt like it was like a culmination of a lot of things to have this opportunity to work here. >> Sounds like a fantastic opportunity. I want to dive a little bit deeper into the technology side there. How is Instacart's technology helping grocers with varying levels of scale and geographical challenges and I'm sure a variety of other things and even a digital skillset. How are you helping them navigate their digital transformation? >> You know, this is probably one of the sectors that lags behind other retail sectors as far as digital transformation. And when the progress that's been made over the last four years is tremendous. And the road ahead is still before us is still a long way to go. I mean Instacart built the world's largest grocery marketplace, if you want to think about that. And so we have more than 10 years of experience in understanding the complexity of that. With, again a supply chain that is very, very complex. So last spring we announced the Instacart platform as a way of really putting a name to a lot of work we were already doing. And it's all about opening up the capability and the technology that we have to help grocers reach their customers directly as well as through our marketplace. So we help grocers like Publix, Wegmans, The Fresh Market just hundreds of grocers build out their own storefronts, their own mobile apps and that we are actually powering for them. We help them create some very unique fulfillment models that might serve customers or be new market opportunities. Certainly we have the traditional full service shop, but we also have virtual convenience that can enable delivery in minutes. And in certain geographies and demographics, that's, you know, really important. We are even going in the store with our connected stores technologies that we announced earlier this year, and that is everything from smart cards to scan and pay to wayfinding that it just, it's a lot of very interesting work we're doing and we're very, very fortunate to be able to partner with some of the best and brightest grocery retailers out there as well as retailers and other verticals as well. But grocery store is sort of our core. >> Yeah, I can only imagine some of the conversations that you have and the user behaviors that you get to learn about as people are on their food journey. You teased a little bit there about what's coming next. What else do you think is in our food future? >> Well, I think, you know, the pandemic pushed the grocery industry to get online to start to digitally transform itself, but we believe it's not an either or. There are virtually no one that's exclusively online and we know more and more there's no one that's exclusively you know, only in the store. We really expect to have that blend and I think as long as we're very, very savvy about understanding the, our retailers' needs as well as their customers' needs on how they can really traverse seamlessly between whether they're online or in store, how they can have an engaging experience that's consistent to the brand of the retailer. >> Savannah: Mm-Hm. >> How they can be rewarded for their loyalty. How they can be encouraged to try new things and just have a much more engaging experience with that grocer because food is a very emotional sort of buy, right? I mean, it's a very sensory rich. And so how- >> Sort of? I think you can go ahead and just make that claim. Just for a lot of people, yeah, yeah. We'll endorse that. >> You're right, yeah, it is. Right, we're passionate about our brand of this or that or we want to touch or smell or do things like that. So there's a tremendous amount of innovation you get online, like personalization and other things that you don't get when you get, you walk into the store, everybody's got the same end cap like I see the same end cap as you see and we might be very different. And then vice versa. I get a very much a sensory experience when I'm in the store, right? That I don't have, how do we blend that? And so there's some really interesting things that we're working on with our retail partners to embrace that omnichannel approach. So we create that flywheel of experience and innovation between the two. So I think you're going to see a lot more focus on an omnichannel experience that traverses between the on and the in, online and the in-store. >> Yeah, I, so I love this because you know, we, there's a continued debate around remote and in-person, working remote and in-person events, but it sounds like hybrid is here to stay when it comes to food and and how we eat, which is very exciting. Last question for you, Jeanette. What would you say to someone, a woman of any age who is looking at this video or maybe dreaming about a career in cloud technology? What's your moment of inspiration? >> You know, I think my best advice is all, you know, stay curious. Just be in love with not even just the technology for technology's sake, but what the technology can unlock as far as an experience and focus on building those experiences. Not only for your direct customer in my case, retailers, grocers, but for their customer. Trying to understand that. And I think if you can connect those dots, you know the cloud is the limit, let's put it that way. (Jeanette and Savannah laugh) >> I'll take it upon that. I love that. Jeanette Barlow, thank you so much for joining us. The team at Instacart is lucky to have you. And thank you to our audience for joining us for this special program on theCUBE featuring Women of the Cloud. My name is Savannah Peterson and I look forward to celebrating more brilliant women like Jeanette with you all soon. (upbeat, happy music)

>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great question, Steve. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, and it does get disconnected on a regular basis. In fact, I forget the exact number, but some several dozen locations get disconnected daily just by virtue of the fact that there's construction going on and things are happening in the real world. When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)