>>from around the globe. It's the Cube covering HP. Discover Virtual Experience Brought to you by HP >>Hello and welcome back to the Cube's coverage of HP Discover Virtual experience. This is the Cube. I'm John Furrier, your host. We're here in the Palo Alto studio with remote interviews. We have a great innovation story here with Disney and HBO. ET Al is tailor vice president of content innovation with studio lab Disney. And so men do suck. Sarkar, distinguished technologist, director of AI at HP. Thanks for coming on, Alice. Someone do. Thank you for taking the time. >>It's great to be here. Hi, >>I love this story. I think it's the innovation story, and I think it's going to be one that will experience in our life going forward. That is media, video and experiences and this innovation in AI. It's a lot to do with the collaboration between Disney Studio Labs Alice that you're running and it's super super important and fun as well. Very relevant. Cool. So first, before we get started, Alice, >>take a minute >>to explain a little about yourself and how Studio Lab came about. Yeah, >>McGuinness Studio lab is just over in its second year of operation. It was an idea that was had by our CTO. I'm going to say, three years ago and at the time, just previously before that I had a start up company that came through the Disney accelerators. So I was already inside the building and, um, the team there said Felicity on the said, You know, we need to start up an innovation lab that will investigate storytelling through emerging technology, and that's basically being the majority of my background. So I said Yes on then. Since then, we'll be going a team. We opened the lab in May of 2018 and here we are in the middle of Pandemic. But it has grown like crazy. Its just a wonderful place to be and to operate. And we've been doing some amazing projects with some amazing partners, >>and it's not unusual that an entrepreneur has this kind of role to think outside the box. We'll get some of that talk about your experiences, and I wonder how you got into this position because you came in as an entrepreneur. You're doing some creative things. Tell us that story real quick. >>Yeah, Okay, well, so as you could sell on British. My actual background started. My whole career started in technology in the mid nineties. A Xai started as a training video editor but then switched very quickly and 95 building websites. And from there on, it was Internet all the way. But I've always focused on storytelling. And, you know, much of my background is working for broadcasters and media and content creators. So those five years of the BBC in there already department and, um actually out here is VP of digital media for them and then Channel four as well. And throughout the whole process, I was always interested in how to tell stories with new technology and the new mediums as they emerged. So yep, flights side story and doing a startup which was actually in toys and video games, but again, big digital storytelling environments for Children. And then I came round. Robin, if you like into Disney and here we are still looking at how to you make films and episodic content. Even Mawr. You name it faster, better, more exciting. Using the best and greatest in emerging tech as we find it, >>and the lab that you're doing is it's an accelerant, almost four new technologies. Your job is to what? Look out over the horizon next 10 years or so to figure out what's next. It's >>not a structure. I think you have >>some rain to be creative and experiment >>Well, yeah, I mean, in fact. So it's a studio live at the studios. We'll Disney has eight studios at the moment, and what we do is we look at actually the whole breath of storytelling. So right from the moment when a creative has an idea through to how our guests and fans might be receiving the end product out in the world and we segregate those that that whole breadth from into three categories i d. Eight. When you know the process of generating the idea and building it, make how we make it where we make it, what we make it with on that experience, how we experience it out in the world. So we have a whole SNU of projects. The studio level so works with some of the best technology companies in the world, and we call those are innovation partners on. We sign these partnerships really to bring what we like to call superpowers to the system we like to think. But the combination of those companies and what comes out of these projects is going to give our filmmakers superpowers, but also that combinatorial effect of Disney. You know, in this case, for instance, working with HP like produces something that Disney couldn't necessarily do on its own or the HP. He couldn't necessarily do it on his own, either. So, yeah, it's a huge remit, and we tend to look, we don't look quite so far out. Generally speaking as 10 years, it's more like three to now. We don't do day to day operational work, but we try to pick something up a couple of years before it's going to be operationally ready and really investigated then and get a bit of a head start. >>Well, it's great. Have HBs partner and And having that bench of technology software people is just a nice power source for you as well. Someone to talk about the relation HP relationship with Disney because, um, you got a lot of deep technical from the lab standpoint to resilient technology. How are you involved? What's your role? You guys sitting around you riff and put a white board together and say, Hey, we're gonna solve these big problems. Here is the future of consumption. That is the future of video. What goes on? Tell us your the relationship between you guys. >>Yeah, it's a good question at HP. We don't really make the service, but what we also do is we work quite a lot on optimizing some of the artificial intelligence solutions and algorithms on the DP use and scale it across servers. So So you don't have this opportunity came up from Disney, where this thing came up with a very innovative solution where they were solving the video quality problem. As as, you know, there are a lot of blemishes and in the video that can come up and didn't want to fix all of them. And they have great algorithm. But what happens is, but with better guards comes a huge amount of computational complexity, which needs a little bit of heterogeneous compute input in parallel processing and in sequential processing. So we thought that it's a perfect on, and it's a combination off the skill sets to make this video quality software execute at speed switch needed for production. Disney. >>So it's good to have a data center whenever you need it to. You guys have a great technology. We hear a lot more from the execs at HB on our reporting else. Want to get your thoughts? We're covering some of this new edge technologies. We're talking about new experiences. I gave a talk at Sundance a few years ago, called The New Creative Class, and it's really about this next wave of art and filmmakers who are using the tools of the trade, which is a cell phone and and really set of Asti studios and use the technology. Can you give us some examples of how Studio Lab collaborates with filmmakers and execs to push the push, the art and technology of storytelling to be fresh? Because the sign of the times, our instagram, tic tac, this is just very elementary. The quality and the storytelling is pretty basic dopamine in, but you can almost imagine the range of quality that's going to come so access to more people, certainly more equipment, cameras, etcetera. What's next? How do you guys see? What's some examples can you share? >>It's an amazing question. I mean, we're working on films and episodic. It's rather than very short form content, obviously, but you're absolutely right. There's a lot of consumer grade technology that is entering the production pipeline in many ways and in many areas, whether it's phones or iPads first using certain bits of software. One of the things that we're building at the moment is the ability Teoh generate vertical metric models, capturing with consumer drones or even iPhones, and then use it getting that data into a three D model as soon as possible. There's a really big theme. What we want to do is like make the process more efficient so that our creatives and the folks working on productions aren't having to slog through something that's slow and tedious. They want to get to the story, telling the art in the act of storytelling as much as possible. And so waiting for a model to render or waiting for their QC process toe finished is what we want to kind of get rid of so they can really get to the meat of the problem much, much faster and just going back to what Mandy was saying about the AI project here I mean, it was about finding the dead pixels on screen when we do our finished prints, which would you believe we do with humans? Humans at their best historically have been the best of finding dead pixels. But what a job I have to do at the end of the process to go through quality control and then have to go and manually find the little dead pixels in each frame of our print. Right? Nobody actually wants to be doing that job. So the algorithm goes and looks follows automatically. And then HP came in and spread that whole process up by nine X. So now it actually runs fast enough to be used on our final prince. >>You know, it's interesting in the tech trend for the past 10 15 years that I've been covering cloud technology. Even in the early days, it was kind of on the fringe them because mainstream. But all the trends were more agility, faster taking with ah, heavy lifting so that the focus on the job at hand when it's creative writing software. This is kind of a success formula, and you're kind of applying it to film and creation, which is still like software is kind of same thing, almost so you know, when you see these new technologies that love to get both your reactions of this. One of the big misses that people kind of miss is the best stuff is often misunderstood until it's understood. And we're kind of seeing that now. A covert our ones. From a way, I could have seen this. No, no one predicted. So what's >>an >>example of something that people might be misunderstanding that super relevant, that that might become super important very quickly? Any thoughts? >>That's great. Well, I can give an example of something that has come and gone and then coming, potentially gone. Except it hasn't it's VR. So it came, you know, whenever it was 20 years ago, and then 10 years ago, and everybody was saying VR is going to change the world And then it reappeared again six years ago again, everybody said it's going to change the world, and in terms of film production, it really has. But that's slightly gone unnoticed, I think, because out in the market everyone is expecting VR to have being a huge consumer success, and I suspect it still will be one day a huge consumer success. But meanwhile, in the background, we're using VR on a daily basis in film production. Virtual production is one of the biggest, um, emerging processes that is happening If you've seen anything to do with, um Jungle Book Line King Man DeLorean. Anything the industrial like magic work on. You're really looking at a lot of virtual production techniques that have ended up on screen, and it is now a technology that we can't do without. I'm gonna have to think two seconds for something that's emerging. Ai and Ml is a huge area, obviously were scratching it. I don't think anyone is going to say that it's going to come and go in this one. This is huge, but we're only just beginning to see where and how we can apply Ai and Ml and you did you wanna jump in on that one? So >>let me take it from the technology standpoint, I think it was also very cool trends. Now what happens is that your ML spaces people have come up with creative ideas. But one of the biggest challenge is how do you take those ideas for commercial, use it on and make and make it work at the speed, as Alice was mentioning, It makes it feasible in production. So accelerating your ML on making it in a form which is visible is super important. And the other aspect of it is just the first video quality that it was mentioning. That picture is one types, and I know the business is working on certain other video qualities to fix the blemishes. But there's a whole variety of these vanishes on with human operators. It's kind of impossible to scale up the production on to find all these different artifacts like, you know, especially now. As you can see, the video is disseminated in your forms in your ipads from like, you know, in that streaming. So this is a problem of scale on do stuff. This is also like, you know, a lot of compute on a very like I said, a lot of collaboration with complimentary skill sets that make it real. >>I was talking with a friend who was an early Apple employees, now retired good friend, and we're talking about all the Dev ops agile go fast scale up, and he made a comment I want to get your reaction to, he said. You know what we're missing is craft and software. You speak crafts game. So when you have speed, you lose craft, and we see that certainly with cloud and agility and then iterating. Then you get to a good product over time. But I think one of the things that's interesting and you guys are kind of teasing out is you can kind of get craft with the help from some of these technologies where you can kind of build crafting into it. Alice, what's your reaction to that? >>One of our favorite anecdotes from The Lion King is so Jon Favreau, the director, built out the virtual production system himself, Teoh with his team to make the film, and it allowed for a smaller production team acting on a smaller footprint. What they didn't do was shortened the time to make the film. What the whole system enabled was more content created within that same amount of time, so effectively John had more takes and more material to make his final film with, and that that's what we want people to have. We want them to have to know ever to have to say I missed my perfect shot because of I don't know what you know. We run out of time so we couldn't get the perfect shot. That's it. That's a terrible thing. We never want that to happen. So where technology can help gather as much material is possible in the most efficient way. Basically, at the end of the day, for our for our creatives, that means more ability to tell a story. >>So someone do. This is an example of the pixel innovation. The Video QC. It's really a burden if you have to go get it and chase it. You can automate that respect from the tech trends. Will automation action in there? >>Yeah, absolutely. And as Chris was mentioning, If you can bridge the gap between imagination and realization, then you have solved the problem that the people who are creative can think on implement something in a very short time, gone back for like, you know, some of these I'm just coming. >>Well, it's a very impressed that I'm looking forward to coming down and visiting studio labs when the world gets back to work. You guys are in the heart of Burbank and all the action and the Euro little incubates really kind of R and D meets commercial commercially. Really cool. But I have to ask you, with covert 19 going on, how are you guys handling? The situation certainly impacted people coming to work. How is your team? Have been impacted. And how are you guys continuing the mission? >>Well, yeah, The lab itself is obviously a physical place on the lot. It's in the old animation building, but it's also this program of innovation that we have with our partners. To be honest, we didn't slow down at all. The team carried on the next day from home, and in fact, we have expanded even because new projects came rolling in as folks who were stuck at home suddenly had needs. So we had editors needing to work work remotely. You know, you name it folks with that home connections, wondering if we had some five G phones hanging around that kind of thing. And so everything really expanded a bit. We are hoping to get back into physical co location as soon as possible, not least to be able to shoot movies again. But I think that there will be an element of this remote working that's baked in forever from here on in not least, cause it was just around. This kind of what this has done has accelerated things like the beginning of cloud adoption properly in the beginning of remote teller work and remote telepresence and then also ideas coming out of that. So ah, you know, again, the other day I heard holograms coming up. Like, Can we have holograms yet? So we don't do it That's going to cover out again. Yeah, but you know what? The team have all been amazing, would. But we'll miss each other. You know, there's something about real life that can't be replaced by technology >>has been a great leader in in accumulating. All HP employees work from removed and in the process. But we're also discovered is we have also, you know, maybe so. We discovered innovative ways where we can still work together. Like so we increase the volume of our virtual collaborations on. I worked with Erica from Disney is a tremendous facilitator and the technologists of mining one. You have this close collaboration going. Andi almost missed nothing, but yes, if you would like to, you know, on the field each other on to be in close proximity. Look at each app in each other's eyes are probably that's only missing thing, but rest off it, You know, we created an environment perfect, clever and work pretty well. And actually, at this point in the process, we also discovered a lot of things which can be done in remote, considering the community of Silicon Valley. >>You know, the final question I want to get your thoughts on is your favorite technology that you're excited about. But someone doing you know, we're talking amongst us nerds and geeks here in Silicon Valley around you know what virtualization server virtualization has done? An HP knows a lot about server virtualization. You're in the server business that created cloud because with virtualization, you could create one server and great many servers. But I think this covert 19 and future beyond it virtualization of life, Immersion of digital is going to bring and change a lot of things. You guys highlight a few of them. Um, this virtualization of life society experiences playing work. It's not just work. It's experiences so Internet of things devices how I'm consuming how I'm producing. It's really going to have an impact. I'd love to get your both of your thoughts on this kind of virtualization of life because certainly impact studio lab, because you think about these things. Alice and HP has to invent that the tech to get scaling up. So final question. What do think about virtualization of life and what technologies do you see that you're excited about to help make our lives better? >>Well, goodness, may, I think we're only beginning to understand the impact that things like video conferencing has on folks. You know, I don't know whether you've seen all of the articles flying around about how it's a lot more work to do video conferencing that you don't have the same subtle cues as you have in real life. And again, you know, virtual technologies like we are on day similar and not going to solve that immediately. So what we'll have to happen is that humans themselves will adapt to the systems. I think, though fundamentally we're about to enter a radical period. We basically have already a radical period of innovation because as folks understand what's at their fingertips. And then what's missing? We're going to see all sorts of startups and new ideas come rushing out as people understand this new paradigm and what they could do to solve for the new pains that come out of it. I mean, just from my perspective, I have back to back nine hours of BTC a day. And by the end of the day, I could barely walk Way gonna do about that. I think we're gonna see holograms like that. We're gonna see home exercise equipment combined. You know, really good ones. Like you've seen politicians shares going crazy. There's tons of that. So I'm just really excited at the kind of three years or so. I think that we're going to see of radical innovation, the likes of which we have always usually being held back by, um other reasons, maybe not enough money or not enough permission. Whereas now people are like we have to fix this problem. >>Well, you've got a great job. I want to come to quit. My job income joined studio left. Sounds like it's a playground of fun. There great stuff. Someone do close us out here. What? Are you excited about as we virtualized you're in the in the labs, creating new technology. You're distinct, technologist and director of AI. When you're on the cutting edge, you're riding the wave two. What's your take on this? >>Virtually? Yeah, you know the experience. What it has done is it has pushed the age to the home. So now if you really see home is one of the principal connectivity to the outside world restaurants. Professional goes on and on with that, What I also offers is like a better experience. Right now. We're all gather about Zoom being able to do a video conferencing. But as this was pointing out there is that here in that we are now consider combining the augmented reality and and the way that we do your conference and all the other innovations that we could begin in the East so that the interactions becomes much more really. And that is like, you know, I'd say that the world is moving to >>l Cool. Thank you very much for that comment and insight really enjoyed. Congratulations on studio lab. You've got a great mission and very cool and very relevant. And someone do. Thank you very much for sharing the insights on HP's role in that. Appreciate it. Thank you very much. Okay, this is the Cube. Virtual covering HP Discover virtual experience. I'm John Furrow, your host of the Cube. Stay tuned for more coverage from HP Discover experience after this break. >>Yeah, yeah, yeah, yeah, yeah.

From around the globe. It's theCUBE covering HPE's Discover Virtual Experience. Brought to you by HPE. >> Hello and welcome back to the CUBE's coverage of HPE discover Virtual Experience. This is theCUBE, I'm John Furrier, your host, we're here in the Palo Alto Studio for the remote interviews. We have a great innovation story here with Disney and HPE, Alice Taylor, Vice President of Content Innovation with studioLAB at Disney. And Soumyendu Sarkar, distinguished technologist director of AI at HPE. Thanks for coming on Alice. Samandiyu thank you for taking the time. >> No worries. Great to be here. Hi. >> Hi >> I love this story. I think it's an innovation story and I think it's going to be one that we'll experience in our life going forward, and that is media, video, and its experiences and these innovation about AI, It's a lot to do with the collaboration between Disney studioLAB, Alice, that you're running, and it's super, super important and fun as well and very relevant and Cool. So first before we get started, Alice, take a minute to explain a little about yourself and how StudioLAB came about. >> Oh my goodness. StudioLAB is just in its second year of operation. It was an idea that was had by our CTO. I'm going to say three years ago, And at the time, just previously before that, I had a startup company that came through the Disney accelerator. So I was already inside the building and the team there said well, the CTO there, and the boss said, you know, we need to start up an innovation lab that will investigate storytelling through emerging technology. And that's basically being the majority of my background. So I said, yes. And then since then we've been growing a team. We opened the lab in may of 2018 and here we are, in the middle of a pandemic, but it has grown like crazy. It's just a wonderful place to be and to operate. And we've been doing some amazing projects with some amazing partners, >> And it's not unusual that an entrepreneur has this kind of role to think outside the box. We'll get at some of that. Talk about your experience as an entrepreneur, how you got into this position, because you came in as an entrepreneur, you're doing some creative things. Tell us that story real quick. >> Yeah. Okay. Well, so as you can tell, I'm British. My actual background started, my whole career started in technology in the mid 90s. As I started as a trainee video editor, but then switched very quickly in 95 to building websites and from there on, and it was internet all the way. But I've always focused on storytelling and I, you know, much of my background is working for broadcasters and media and content creators. So I was five years at the BBC in their R and D department. And I'm actually out here as VP of digital media for them, and then Channel 4 as well. And throughout the whole process, I was always interested in how to tell stories with new technology and the new mediums as they emerged. So yeah, slight side story and doing a startup, which was actually in toys and video games, but again, big digital storytelling environments for children. And then I came round Robin, if you like into Disney and here we are still looking at how to make films and episodic content, even more, you name it faster, better, more exciting, using the best and greatest in emerging tech as we find it. >> The lab that you're doing, it's an accelerant almost for new technologies. Your job is to what? look out over the horizon next 10 years or so to figure out? >> Yeah >> what's next. It's not a structured thing. You have some reign to be creative and experiment? >> Well, yeah, I mean, the studioLAB, at the studios, well, Disney has eight studios at the moment, And what we do is we look at actually the whole breadth of storytelling. So right from the moment when a creative has an idea through to how our guests and fans might be receiving the end product out in the world, and we segregate that whole breadth into three categories; Ideate, when, you know, the process of generating the idea and building it, Make, how we make it, where we make it, what we make it with and then Experience. How we experience it out in the world. So we have a whole slew of projects, the studio level works with some of the best technology companies in the world. And we call those our innovation partners and we sign these partnerships really to bring what we like to call Superpowers to the system. We like to think that the combination of those companies and what comes out of these projects is going to give our filmmakers superpowers, but also that combinatorial effect of Disney, you know, in this case, for instance, working with HPE, like producing something that Disney couldn't necessarily do on its own or the HBE couldn't necessarily do on his own either. So yeah, it's a huge remit and we don't look quite so far out, generally speaking as 10 years, it's more like three to now. We don't do day to day operational work, but we try to pick something up a couple of years before it's going to be operationally ready and really investigate it then and get a bit of a headstart. >> Well, it's great to have HPE as partner and having that bench of technology, software, and people, and it's just a nice power source for you as well. >> Exactly So Soumyendu talk about HPE relationship with Disney, because you got a lot of deep technical from the lab standpoint to resilient technology. How are you involved? What's your role, you guys sitting around you riffing and put a whiteboard together and say, Hey, we're going to solve these big problems? ... Here's the future of consumption, here's the future of video... What goes on? Tell us the relationship between you guys. >> Yeah, it's a good question. At HPE We can not only make the servers, but what we also do is we work quite a lot on optimizing some of the Artificial Intelligence solutions and algorithms on the GPUs and scale it across Servers. So this opportunity came up from Disney where Disney came up with a very innovative solution where they were solving the video quality problem. As you know, there are a lot of blemishes in the Video that can come up and Disney wanted to fix all of them. And they came up with great algorithm, but what happens is, like with great algorithm comes a huge amount of computational complexity which needs quite a bit of heterogeneous input in both in Parallel Processing and in Sequential Processing. So we thought that it's a perfect, I'd say combination of two skillsets to make this video quality software execute at speeds which are needed for production in Disney. >> So it's good to have a data center whenever you need it to, you guys have some great technology. We'll hear a lot more from the Execs at HPE. On our reporting Alice, we want to to get your thoughts. We're covering some of those new edge technologies, we're talking about new experiences. I gave a talk at Sundance a few years ago called the new creative class, and it's really about this next wave of art and filmmakers who are using the tools of the trade, which is a cellphone, you know, really easy to set up a studios and use the technology. Can you give us some examples of how the studioLAB collaborates with filmmakers and the Execs to push the art and technology of storytelling to be fresh, Because the sign of the times, are Instagram and Tik Tok, this is just very elementary, the quality and the storytelling is pretty basic dopamine driven, but you can almost imagine that the range of quality that's going to come, so access to more people, certainly more equipment and cameras, et cetera. What's next? How do you guys see And what some examples can you share? >> Oh, that's an amazing question. I mean, where working on Films and Episodics rather than very short form content , Obviously. But you're absolutely right. There's a lot of consumer grade technology that is entering the production pipeline in many ways and in many areas, whether it's phones or iPads, using certain bits of software. One of the things that we're building at the moment is the ability to generate photometrical models, capturing with consumer drones or even iPhones, and then getting that data into a 3-D model as soon as possible. There's a really big theme of what we want to do. It's like make the process more efficient so that our creatives and the folks working on productions, aren't having to slog through something that's and tedious. They want to get to the storytelling and the art and the act of storytelling as much as possible. And so waiting for a model to render or waiting for the QC process to finish is what we want to kind of get rid of. So they can really get to the meat of the problem much, much faster. And just going back to what Soumyendu was saying about the AI project here, I mean, it was about finding the dead pixels on the screen when we do all finished prints, which would you believe we do with humans? Humans are the best, or historically have been the best at finding dead pixels, but what a job to have to do at the end of the process. To go through quality control and then have to go and manually find the little dead pixels in each frame of our print, right? Nobody actually wants to be doing that job. So the algorithm goes and looks for those automatically. And then HPE came in and sped that whole process up by 9X. So now it actually runs fast enough to be used on our final prints. >> You know, it's interesting in the tech trend for the past 10, 15 years that I've been covering cloud technology even in the early days, it was kind of on the fringe and then become mainstream. But all the trends were more agility, faster, take away that heavy lifting so that the focus on the job at hand, whether its creative or writing software. This is kind of a a success formula, and you're kind of applying it to film and creation, which is still, like software, it's kind of the same thing almost. >> Yeah >> So you know, when you see these new technologies, I'd love to get both of your reactions to this. One of the big misses, that people kind of miss is the best stuff is often misunderstood until it's understood. >> Yes >> And we're kind of seeing that now with Covid and everyone's like no way I could've seen this. No, no one predicted it. So what's an example of something that people might be misunderstanding. That's super relevant, that might become super important very quickly. Any thoughts? >> Gosh, that's a great one. Well, I can give an example of something that has come and gone and then come and potentially gone, except it hasn't. You'll see. It's VR. So it came whenever it was, 20 years ago and then 10 years ago, and everybody was saying VR is going to change the world. And then it reappeared again, six years ago. And again, everybody said it was going to change the world. And in terms of film production, it really has. But that's slightly gone unnoticed. I think, because out in the market, everyone is expecting VR to have been a huge consumer success. And I suspect it still will be one day a huge consumer success. But meanwhile, in the background, We are using VR on a daily basis in film production, Virtual production is one of the biggest emerging processes that is happening. If you've seen anything to do with Jungle Book, Lion King , the Mandalorian, anything that industrial light and magic work on, you're really looking at a lot of virtual production techniques that have ended up on the screen. And it is now a technology that we can't do without. I'm going to have to think two seconds for something that's emerging. AI and ML is a huge area. Obviously, we're scratching it. I don't think anyone is going to to say that it's going to come and go this one. This is huge, but we're already just beginning to see where and how we can apply AI and ML. >> Yeah. >> So Soumyendu, did you want to jump in on that one? >> Yeah, Let me take it from the technology standpoint. I think Alice sort of puts out some very cool trends. Now what happens in tHE AI and ML spaces, people can come up with creative ideas, but one of the biggest challenges is how do you take those ideas for commercial usage and make it work at a speed, as Alice was mentioning, makes it feasible in production. So accelerating AI/ML and making it in a form, which is usable is super important. And the other aspect of it is, just see, for instance, video quality, that Alice was mentioning. Dead pixel is one type, And I know that Disney is working on certain other video qualities to fix the blemishes, but there is a whole variety of these blemishes and with human operators, Its kind of impossible to scale up the production and to find all these different artifacts, and especially now, as you can see, the video is disseminated in your phones, in your iPads. Like, you know, in just streaming. So this is a problem of scale and to solve this is also like, you know, a lot of computers, and I'd say a lot of collaboration with complementary skillsets that make AI real. >> I was talking with a friend who was an early Apple employee. He's now retired, good friend. And we were talking about, you know, all the dev apps, agile, go fast, scale up. And he made a comment. I want to get your reaction to it. He said, "you know, what we're missing is craft." And software used to be a craft game. So when you have speed, you lose craft. And we see that certainly with cloud and agility and then iterate, and then you get to a good product over time. But I think one of the things that's interesting and you guys are kind of teasing out is you can kind of get craft with the help from some of these technologies, where, you can kind of build crafting into it. >> Yap Alice, what's your reaction to that? >> One of our favorite anecdotes from the lion King is, so Jon Favreau the director, built out the virtual production system with his team to make the film. And it allowed for a smaller production team acting on a smaller footprint. What they didn't do was shorten the time to make the film, what the whole system enabled was more content created within that same amount of time. So effectively Jon had more tapes and more material to make his final film with. And that's what we want people to have. We want them to not ever to have to say, Oh, I missed my perfect shot because of, I don't know what, you know, we ran out of time, so we couldn't get the perfect shot. That's it, that's a terrible thing. We never want that to happen. So where technology can help gather as much material as possible in the most efficient way, basically at the end of the day for our our creatives, that means more ability to tell a story. >> So Soumyendu, this is an example of the pixel innovation, the video QC, it's really a burden if you have to go get it and chase it, you can automate that. That's back to some of the tech trends. A lot of automation action in there. >> Yeah, absolutely. And as Alice was mentioning, if you can bridge the gap between imagination and realization then you have solved the problem. That way, the people who are creative can think and implement something in a very short time. And that's fair, like, you know, some of these scientists come in >> Well, I also very impressed and I'm looking forward to coming down and visiting studio labs when the world gets back to work, >> Alright. >> You guys are in the part of Burbank and all the action. I know you're a little sort of incubate. It's really kind of R and D meet commercially. Commercial is really cool. But I have to ask you what the COVID-19 going on, how are you guys handling the situation? Certainly impacted people coming to work. >> Yeah >> How has your team in been impacted and how are you guys continuing the mission? >> Well, the lab itself is obviously a physical place on the lot. It's in the old animation building. But there's also this program of innovation that we have with our partners. To be honest, we didn't slow down at all the team carried on the next day from home. And in fact, we have expanded even, because new projects came rolling in as folks who were stuck at home suddenly had needs. So we had editors needing to work remotely, you name it, folks with bad home connections, wondering if we had some 5G phones hanging around, that kind of thing. And so everything really expanded a bit. We are hoping to get back into physical co-location as soon as possible, not least to be able to shoot movies again. But I think that there will be an element of this remote working that's baked in forever from here on then. Not least, coz it was just a round, this kind of, what this has done is accelerated things like the beginning of cloud adoption properly, in the beginning of remote teleworking and remote telepresence, and then also ideas coming out of that. So you know, again, the other day I heard Holograms coming up, like, can we have holograms yet? >> Yeah, we can do that, we've done that, Lets do it. Bring that back. >> And so it's that kind of thing. Exactly, that's going to come around again. Yeah. But you know what? The team have all been amazing. But we'll miss each other, you know, there's something about real life that can't be replaced by technology. >> Well, You know, we were talking earlier on theCUBE last week about, the future got pulled to the present, not the present accelerated the future. Which exposes some of these things that are really important and you mentioned it. So I have to ask you Alice, as you guys got more work, obviously it makes sense. What have you learnt as adapting and leading your team through this change? Any learnings you can share with folks? >> Well, yes, that's a good one. But mainly resilience. It's been a nonstop and quite relentless and the news out there is extraordinary. So we're also trying to balance a very full pipeline of work with understanding that people are struggling to balance their lives as well at home, You know, kids, pets, BLM, like you name it, everything is affecting everybody. So resilience and empathy is really top of my mind at the moment as we try to continue to succeed, but making sure that everybody stays healthy and sane. >> Yeah. And in great news, you got a partner here with HPE, the innovation doesn't stop there. You still have to partner. How do you keep up with these technologies and the importance of partners, comments, and Soumyendu your comment as well. >> Yeah. So HPE has been a great leader in accommodating all HPE employees to work from remote and in the process, what we also discovered is, we humans are innovative. So we discover the innovative ways where we can still work together. So we increased the volume of our virtual collaborations, and I have worked with Erica from Disney, who is a tremendous facilitator and a technologist of mine, to have this close collaboration going, and we almost missed nothing. But yes, we would like to, you know the feel each other to be in close proximity, look at each other's eyes. Probably that's the only missing thing, a crest of it, You know, we created an environment where we can collaborate and work pretty well. And to Alice's point in the process, we also discovered a lot of things which can be done in remote considering the community of Silicon Valley. >> You know, I'd love. The final question I want to get your thoughts on is your favorite technologies that you're excited about. But some Soumyendu, you know, we were talking amongst us nerds and geeks here in Silicon Valley around, you know, what Virtualization... Server Virtualization has done. And HPE knows a lot about server virtualization. You're in the server business, that created cloud, because with virtualization, you could create one server and great many servers, but I think this COVID-19 and the future beyond it, virtualization of life, an immersion of digital is going to bring and change a lot of things. You guys highlighted a few of them. This virtualization of life, society, experiences, play, work. It's not just work it's experiences. So Internet of Things, devices, how I'm consuming, how I'm producing, it's really going to have an impact. I'd love to get your, both of your thoughts on this kind of "virtualization of life" because it certainly impacts studioLAB, because you think about these things, Alice, and HP has to invent the tech to get scaling up. So final question. What do you think about virtualization of life and what technologies do you see that you're excited about to help make our lives better? >> Wow. Goodness, me. I think we're only beginning to understand the impact that things like video conferencing has on folks. You know, I don't know whether you've seen all of the articles flying around about how it's a lot more work to do, video conferencing, that you don't have the same subtle cue as you have in real life. And again, you know, virtual technologies like VR and similar, are not going to solve that immediately. So what will have to happen is that humans themselves will adapt to the systems. I think though, fundamentally we're about to enter a radical period. We basically have already a radical period of innovation because as folks understand what's at their fingertips and then what's missing, we're going to see all sorts of startups and new ideas come rushing out. As people understand this new paradigm and what they can do to solve, for the new pains that come out of it. I mean, just from my perspective, I have back-to-back nine hours of etc a day. And by the end of the day, I can barely walk. What are we going to do about that? I think we're going to see, >> Holograms, I like that Idea. >> right, we're going to see home exercise equipment combined with like, you know, really good ones. Like you've seen pellets on the shares going crazy. There's going to be tons of that. So I'm just really excited at the kind of three years or so. I think that we're going to see of radical innovation, the likes of which we have always usually been held back by other reasons, maybe not enough money or not enough permission. Whereas now people are like, we have to fix this problem. >> Well, you got a great job. I want to come, just quit my job and come join studio lab, sounds like that's a playground of fun. They have great stuff. >> Ton of fun. >> Soumyendu, close this out here. What are you excited about as we virtualize. You're in the labs, creating new technology, you're a distinguished technologist and director of AI. I Wean, you're on the cutting edge. You're riding the wave too. What's your take on this virtual center? >> I think, you know the COVID experience, what it has done is it has pushed the edge to the home. So now, if you really see a home is one of the principle connectivity to the outside world, as far as professionalism goes. And with that, what AI also offers is like a better experience. Right now we are all Gaga about zoom being able to do a video conferencing, but as Alice was pointing out, there is that ER, and the VR. Now consider combining the augmented reality. And the way that we do review a conference and all the other AI innovations that we can bring in so that the interactions becomes much more real. And that is like, you know, I'd say, where the world is moving. >> I can't let this go. I have to go one more step in because you guys brought that up. Alice, you mentioned the fatigue and all these things. And if you think about just the younger generations, we have to invest in our communities and our young people. I mean, think about all the kids who have to go back to school in September, in the fall, what their world's like. And you talk about, you know, we can handle video, but learners? So the transformation that's going to come down the path really fast is how do you create an experience for education and for learning and connecting. This is huge. Thoughts and reactions to that. So it's something that I've been thinking a lot about, but I'm sure a lot of other parents have as well. >> My take on that, kids, I've worked a lot with kids and kids media. And over the years, you often find that when a new media does come in, there's a lot of fear around it, but kids are plastic and incredibly good at adapting to new media and new technology and new ways of working. The other thing is, I think this generation of kids have really had to live through something, you know, and it's going to have, with luck, taught them some resilience. I think, if there's one thing that teachers can be focusing on, it is things like resilience and how to cope under very unusual and very unpredictable circumstances, which is never good for things like anxiety. But it's also the reality of the world, you know, be adaptive and learn, keep learning. These are great messages to give to kids. I think if anything, they are the ones who'll figure out how to socialize online successfully and healthily. So we're going to have to learn from them. >> Yeah. They're going to want to make it to be fun too. I mean, you have to make it entertaining. I mean, I find my personal experience, if it's boring, it ain't going to work. Thank you so much, Alice. Well, thank you very much for that comment and insight really enjoy. Congratulations on studioLAB, you got a great mission and very cool and very relevant. Soumyendu thank you very much for sharing the insights on HPE's role in that. I appreciate it. Thank you very much. >> Thanks. It's nice. >> Okay. >> Thanks John. >> This is theCUBE virtual covering HPE Discover Virtual Experience. I'm John Furrier, your host of theCUBE. Stay tuned for more coverage from HPE Discover Virtual Experience after this break.

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas. Lisa Martin with John Ferrier. You're watching the Cube live at Del Technologies World twenty nineteen. This is our second full day of Double Cube set coverage. We've got a couple of we're gonna really cool conversation coming up for you. We've got Robbie Pender County, one of our alumni on the cue back as VP product management server solutions. Robbie, Welcome back. >> Thank you, Lisa. Much appreciated. >> And you brought some Hollywood? Yes. Glenn Glenn ER, president of Sony Innovation Studios. Glenn and welcome to the Cube. >> Thank you very much. It's great to be here. >> So you are love this intersection of Hollywood and technology. But you're a filmmaker. >> Yeah. I have been filming movies for many years. Uh, I started off making motion pictures for many years. Executive produced him and over so production for them at one of our movie labels called Screen Gems, which is part of Sony Pictures. >> Wait a tremendous amount of evolution of the creative process being really fueled by technology and vice versa. Sony Innovation Studios is not quite one year old. This is a really exciting venture. Tell us about that and and what the the impetus was to start this company. >> You know that the genesis for it was based out of necessity because I looked at a nice Well, you know, I love making movies were doing it for a long time. And the challenge of making good pictures is resource is and you never get enough money believing not you never get enough money and never get enough time. That's everybody's issue, particularly time management. And I thought, Well, you know, we got a pretty good technology company behind us. What if we looked inward towards technology to help us find solutions? And so innovation studios is born out of that idea on what was exciting about it was to know that we had, uh, invited partners to the game right here with Del so that we could make movies and television shows and commercials and even enterprise solutions leaning into state of the art and cutting edge technology. >> And what some of the work prize and you guys envision coming out this mission you mentioned commercials. TV is it going to be like an artist's studio actor? Ackerson Ball is Take us through what this is going to look like. How does it get billed out? >> I lean into my career as a producer. To answer that one and say is going to enable that's one of the greatest things about being a producer is enabling stories, uh, inspiring ideas to be Greenland. That may not have been able to be done so before. And there's a key reason why we can't do that, because one of our key technologies is what we call the volumetric image acquisition. That's a lot of words. You probably say. What the heck is that? But a volumetric image acquisition is our ability to capture a real world, this analog world and digitize it, bring it into our servers using the power of Del and then live in that new environment, which is now a virtual sets. And that virtual set is made out of billions and trillions in quadrillions of points, much like the matter around us. And it's a difference because many people use pixels, which is interpretation of like worry, using points which is representative of the world around us, so it's a whole revolutionary way of looking at it. But what it allows us to do is actually film in it in a thirty K moving volume. >> It's like a monster green screen for the world. Been away >> in a way, your your your your action around it because you have peril X so these cameras could be photographing us. And for all you know, we may not be here. Could be at stage seven at Innovation Studios and not physically here, but you couldn't tell it. If >> this is like cloud computing, we talking check world, you don't the provisional these resource is you just get what you want. This is Hollywood looking at the artistry, enabling faster, more agile storytelling. You don't need to go set up a town and go get the permit. All the all the heavy lifting you're shooting in this new digital realm. >> That's right. Exactly. Now I love going on location on. There's a lot to celebrate about going on location, but we can always get to that location. Think of all the locations that we want to be in that air >> base off limits. Both space, the one I >> haven't been, uh, but but on said I've been I've walked on virtual moons and I've walked on set moons. But what if we did a volumetric image acquisition of someone set off the moon? Now we have that, and then we can walk around it. Or what if there's a great club, a nightclub? This says guys want you shoot here, but we have performances Monday night, Tuesday night, Wednesday night there. You know they have a job. What if we grab that image, acquired it, and then you could be there anytime you want. >> Robbie, we could go for an hour here. This is just a great comic. I >> completely agree with you. >> The Cube. You could. You could sponsor a cube in this new world. We could run the Q twenty four seven. That's absolutely >> right. And we don't even have >> to talk about the relationship with Dale because on Del Technologies, because you're enabling new capabilities. New kind of artistry was just totally cool. Want to get back to the second? But you guys were involved. What's your role? How do you get involved? Tell the story about your >> John. I mean, first and foremost one of things that didn't Glendon mention is he's actually got about fifty movies to his credit. So the guy actually knows this stuff, so which is absolutely fantastic. So we said, How do you go take average to the next level? So what else is better than trying to work something out, wherein we together between what Glenn and Esteem does at the Sony Innovation Labs for Studio Sorry. And as in Dead Technologies could do is to try and actually stretch the boundaries of our technology to a next tent that when he talks about kazillion bytes of data right one followed the harmony of our zeros way have to be able to process the data quickly. We have to be able to go out and do their rendering. We probably have to go out and do whatever is needed to make a high quality movie, and that, I think, in a way, is actually giving us an opportunity to go back and test the boundaries of their technology. They're building, which we believe this is the first of its kind in the media industry. If we can go learn together from this experience, we can actually go ahead and do other things in other industries. To maybe, and we were just talking about how we could also take this. He's got his labs here in Los Angeles, were thinking maybe one of the next things we do based on the learnings we get, we probably could take it to other parts of the world. And if we are successful, we might even take it to other industries. What if we could go do something to help in this field of medicine? >> It's just thinking that, right? Yes. >> Think about it. Lisa, John. I mean, it's phenomenal. I mean, this is something Michael always talks about is how do we as del technologies help in progress in the human kind? And if this is something that we can learn from, I think it's going to be phenomenal. >> I think I think that's so interesting. Not only is that a good angle for Del Technologies, the thing that strikes me is the access toe artist trees, voices, new voices that may be missed in the prop the vetting process the old way. But, you know, you got to know where we're going. No, in the Venture Capital way seen this with democratization of seed labs and incubators, where, if you can create access to the story, tells on the artists we're gonna have one more exposure to people might have missed. But also as things change, like whether it's Ray Ray beaming and streaming, we saw in the gaming side to pull a metric or volumetric things. You're gonna have a better canvas, more paint brushes on the creative side and more. Artist. Is that the mission to get AC, get those artists in there? Is it? Is that part of the core mission submission? Because you're going to be essentially incubating new opportunities really fast. >> It's, uh, it's very important to me. Personally. I know it speaks of the values of both Sony and L. I like to call it the democratization of storytelling. You know, I've been very blessed again, a Hollywood producer, and we maybe curate a certain kind of movie, a certain kind of experience. But there's so many voices around the world that need to be hurt, and there are so many stories that otherwise can't be enabled. Imagine a story that perhaps is a unique >> special voice but requires distance. It requires five disparate locations Perhaps it's in London, Piccadilly Circus and in Times Square. And perhaps it's overto Abu Dhabi on DH Libya somewhere because that's part of the story. We can now collapse geography and bring those locations to a central place and allow a story to be told that may not otherwise have been able to be created. And that's vital to the fabric of storytelling worldwide's >> going change the creative process to you don't have to have that waterfall kind of mentality like we don't talk about intact. You're totally distributed content, decentralized, potentially the creative process going change with all the tools and also the visual tools. >> That's right. It's >> almost becoming unlimited. >> You wanted to be unlimited. You want the human spirit to be unlimited. You want to be able to elevate people on. That's the great thing about what we're trying to achieve and will achieve. >> It is your right. I mean, it is interesting, you know, we were just talking about this, too. Uh, we're in, you know, as an example. Shock tank. Yes, right. I mean, they obviously did it. The filming and stuff, and then they don't have the access. Let's say to the right studio. But the fact is, they had all this done. Andi, you know, they had all the rendering they had captured. Already done. You could now go out and do your chute without having all the space you needed. >> That's right. In the case of Shark Tank, which shoots a Sony Pictures studios, they knew they had a real estate issue. The fact of the matter is, there's a limited amount of sound stages around the world. They needed to sound stages and only had access to one. So we went in and we did a volumetric image acquisition of their exit interview stage. They're set. And then when it came time to shoot the second half a season ten, one hundred contestants went into a virtual set and were filmed in that set. And the funny thing is, one of the guys in the truck you know how you have the camera trucks and, you know, off offstage, he leaned into the mike. Is that you guys, could you move that plant a couple inches to the left and somebody said, Uh, I don't think we can do it right now, he said, We're on a movie lot. You could move a plant. They said No, it's physically not there. We're on innovation studios goes Oh, that's right. It's virtual mind. >> So he was fooled. >> He was pulled. In a way, we're >> being hashing it out within a team. When we heard about some of the things you know Glenn and Team are doing is think about this. If you have to teach people when we are running short of doctors, right? Yeah, if you could. With this technology and the learnings that come from here, if you could go have an expert surgeon do surgery once you're captured, it would be nice. Just imagine, to take that learning, go to the new surgeons of the future and trained them and so they can get into the act without actually doing it. So my point and all this is this is where I think we can take technology, that next level where we can not only learn from one specific industry, but we could potentially put it to human good in terms of what we could to and not only preparing the next of doctors, but also take it to the next level. >> This was a great theme to Michael Dell put out there about these new kinds of use case is that the time is now to do before. Maybe you could get there technology, but maybe aspirational. Hey, let's do it. I could see that, Glenn, I want to ask you specifically. The time is now. This is all kind of coming together. Timing's pretty good. It's only gonna get better. It's gonna be good Tech, Tech mojo Coming for the creative side. Where were we before? Because I can almost imagine this is not a new vision for you. Probably seen it now that this house here now what was it like before for, um and compare contrast where you were a few years ago, maybe decades. Now what's different? Why? Why is this so important >> for me? There's a fundamental change in how we can create content and how we can tell stories. It used to be the two most expensive words in the movie TV industry were what if today that the most important words to me or what if Because what if we could collapse geography? What if we could empower a new story? Technology is at a place where, if we can dream it. Chances are we can make it a reality. We're changing the dynamics of how we may content. He used to be lights, action camera. I think it's now lights, action, compute power action, you know, is that kind of difference. >> That is an amazing vision. I think society now has opportunities to kind of take that from distance learning to distance connections, the distance sharing experiences, whether it's immersion, virtual analog face, the face could really be powerful. Yeah, >> and this is not even a year old. >> That's right. >> So if you look at your your launch, you said, I think let june fourth twenty eighteen. What? Where do you go from here? I mean, like we said, this is like, unlimited possibilities. But besides putting Robbie in the movie, naturally, Yes, of course I have >> a star here >> who? E. >> So I got to say he's got star power. >> What's what's next year? Exactly? >> Very exciting. I will say we have shark tank Thie Advanced Imaging Society gives an award for being the first volume met you set ever put out on the airwaves. Uh, for that television show is a great honor. We have already captured uh, men in black. We captured a fifty thousand square foot stage that had the men in black headquarters has been used for commercials to market the film that comes out this June. We have captured sets where television shows >> and in hopes, that they got a second season and one television show called up and said, Guys, we got the second season so they don't have to go back to what was a very expensive set and a beautiful set >> way captured that set. It reminds me of a story of productions and a friend of mine said, which is every year. The greatest gift I have is building a beautiful set and and to me, the biggest challenges. When I say, remember that sent you built four years ago? I need that again. Now you can go >> toe. It's hard to replicate the exact set. You capture it digitally. It lives. >> That's exactly it. >> And this is amazing. I mean, I'd love to do a cube set into do ah, like a simulcast. Virtually. >> So. This is the next thing John and Lisa. You guys could be sitting anywhere going forward >> way. You don't have to be really sitting here >> you could be doing. What do you have to do? And, you know, you got everything rendered >> captured. We don't have to come to Vegas twenty times a year. >> We billed upset once. You >> know you want to see you here believing that So I'LL take that >> visual is a really beautiful thing. So if we can with hologram just seeing people doing conscious with Hollywood. Frank Zappa just did a concert hologram concert, but bringing real people and from communities around the world where the localization diversity right into a content mixture is just so powerful. >> Actually, you said something very interesting, John, which is one of the other teams to which is, if you have a globally connected society and he wanted try and personalize it to that particular nation ethnicity group. You can do that easily now because you can probably pop in actors from the local area with the same. Yeah, think about it. >> It's surely right. >> There's a cascade of transformations that that this is going Teo to generate. I mean just thinking of how different even acting schools and drama schools will be well, teaching people how to behave in these virtual environments, right? >> How to immerse themselves in these environments. And we have tricks up our sleeves that Khun put the actor in that moment through projection mapping and the other techniques that allow filmmakers and actors to actually understand the world. They're about to stepped in rather than a green screen and saying, OK, there's going to be a creature over here is gonna be blue Water falls over there will actually be able to see that environment because that environment will exist before they step on the stage. >> Well, great job the Del Partnership. On my final question, Glenn, free since you're awesome and got a great vision so smart, experienced, I've been really thinking a lot about how visualization and artistry are coming together and how disciplines silo disciplines like music. They do great music, but they're not translating to the graphics. It was just some about Ray tracing and the impact with GP use for an immersive experiences, which we're seeing on the client side of the house. It del So you got the back and stuff you metrics. And so, as artist trees, the next generation come up. This is now a link between the visual that audio the storytelling. It's not a siloed. >> It is not >> your I want to get your vision on. How do you see this playing out and your advice for young artists? That might be, you know, looked as country. What do you know? That's not how we do it. >> Well, the beautiful thing is that there are new ways to tell stories. You know, Hollywood has evolved over the last century. If you look at the studios and still exist, they have all evolved, and that's why they do exist. Great storytellers evolved. We tell stories differently, so long as we can emotionally relate to the story that's being told. I say, Do it in your own voice. The cinematic power is among us. We're blessed that when we look back, we have that shared experience, whether it's animate from Japan or traditional animation from Walt Disney everybody, she shares a similar history. Now it's opportunity to author our new stories, and we can do that and physical assets and volumetric assets and weaken blend the real and the unreal. With the compute power. The world is our oyster. >> Wow, >> What a nice >> trap right there. >> Exactly. That isn't my job. The transformation of of Hollywood. What it's really like the tip of the iceberg. Unlimited story potential. Thank you, Glenn. Thank you. This has been a fascinating cannot wait to hear, See and feel and touch What's next for Sony Animation studios With your technology power, we appreciate your time. >> Thank you. Thank you both. Which of >> our pleasure for John Carrier? I'm Lisa Martin. You're watching the Cube lie from Del Technologies World twenty nineteen We've just wrapped up Day two we'LL see you tomorrow.

>> Live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas. Lisa Martin with John Ferrier. You're watching the Cube live at Del Technologies World twenty nineteen. This is our second full day of Double Cube set coverage. We've got a couple of we got a really cool conversation coming up for you. We've got Robbie Pender County, one of our alumni on the cue back as VP product management server solutions. Robbie, Welcome back. >> Thank you, Lisa. Much appreciated. >> And you brought some Hollywood? Yes, Glenn Glenn er, president of Sony Innovation Studios. Glenn and welcome to the Cube. >> Thank you very much. It's great to be here. >> So you are love this intersection of Hollywood and technology. But you're a filmmaker. >> Yeah, I have been filming movies for many years. I started off making motion pictures for many years. Executive produced him and oversaw production for them at one of our movie labels called Screen Gems, which is part of Sony Pictures. >> Wait a tremendous amount of evolution of the creative process being really fueled by technology and vice versa. Sony Innovation Studios is not quite one year old. This is a really exciting venture. Tell us about that and and what the The impetus was to start this company. >> You know that the genesis for it was based out of necessity because I looked at a nice Well, you know, I love making movies were doing it for a long time. And the challenge of making good pictures is resource is and you never get enough money. Believe or not, you never get enough money and never get enough time. That's everybody's issue, particularly time management. And I thought, Well, you know, we got a pretty good technology company behind us. What if we looked inward towards technology to help us find solutions? And so innovation studios is born out of that idea on what was exciting about it was to know that we had, uh, invited partners to the game right here with Del so that we could make movies and television shows and commercials and even enterprise solutions leaning into state of the art and cutting edge technology. >> And what some of the work private you guys envision coming out this mission you mentioned commercials TV. Is it going to be like an artist's studio actor actress in ball is take us through what this is going to look like. How does it get billed out? >> I lean into my career as a producer. To answer that one and say is going to enable that's one of the greatest things about being a producer is enabling stories, uh, inspiring ideas to be green lit that may not have been able to be done so before. And there's a key reason why we can't do that, because one of our key technologies is what we call the volumetric image acquisition. That's a lot of words. You probably say. What the heck is that? But a volumetric image acquisition is our ability to capture a real world, this analog world and digitize it, bring it into our servers using the power of Del and then live in that new environment, which is now a virtual sets. And that virtual set is made out of billions and trillions in quadrillions of points, much like the matter around us. And that's a difference because many people use pixels, which is interpretation of like we're using points which is representative of the world around us, so it's a whole revolutionary way of looking at it. But what it allows us to do is actually film in it in a thirty K moving volume. >> It's like a monster green screen for the world. Been away >> in a way, you're you're you're interaction around it because you have peril X, so these cameras could be photographing us. And for all you know, we may not be here. Could be at stage seven at Innovation Studios and not physically here, but you couldn't tell the >> difference. This is like cloud computing. We talking check world, you don't the provisional these resource is you just get what you want. This is Hollywood looking at the artistry, enabling faster, more agile storytelling. You don't need to go set up a town and go get the permit. All the all the heavy lifting you're shooting in this new digital realm. >> That's right. Exactly. Now I love going on location on There's a lot to celebrate about going on location, but we can always get to that location. Think of all the locations that we want to be in that air >> base off limits. Both space, the one I >> haven't been, uh, but but on said I've been I've walked on virtual moons and I've walked on set moons. But what if we did a volumetric image acquisition of someone set off the moon? Now we have that, and then we can walk around it. Or what if there's a great club, a nightclub? This says guys and wanted to shoot here. But we have performances Monday night, Tuesday night, Wednesday night there. You know they have a job. What? We grabbed that image acquired it. And then you could be there anytime you want. >> Robbie, we could go for an hour here. This is just a great comic. I >> completely agree with >> you. The Cube. You could You could sponsor a cube in this new world. We could run the Q twenty four seven is absolutely >> right. And we don't even have >> to talk about the relationship with Dale because on Del Technologies, because you're enabling new capabilities. New kind of artistry, just totally cool. Want to get back to the second? But you guys were involved. What's your role? How do you get involved? Tell the story about your >> John. I mean, first and foremost one of the things didn't Glendon mention is he's actually got about fifty movies to his credit. So the guy actually knows this stuff. So which is absolutely fantastic. So we said, How do you go take coverage to the next level? So what else is better than trying to work something out, wherein we together between what Glenn and Esteem does at the Sony Innovation Labs for Studio Sorry. And as in Dead Technologies could do is to try and actually stretch the boundaries of our technology to a next tent that when he talks about kazillion bytes of data right one followed by harmony, our zeros. We have to be able to process the data quickly. We have to be able to go out and do their rendering. We probably have to go out and do whatever is needed to make a high quality movie, and that, I think, in a way, is actually giving us an opportunity to go back and test the boundaries of their technology. They're building, which we believe this is the first of its kind in the media industry. If we can go learn together from this experience, we can actually go ahead and do other things in other industries do. Maybe. And we were just talking about how we could also take this. He's got his labs here in Los Angeles, were thinking maybe one of the next things we do based on the learning to get. We probably could take it to other parts of the world. And if we are successful, we might even take it to other industries. What if we could go do something to help in this field of medicine? >> It's just thinking that, right? Yes. Think >> about it. Lisa, John. I mean, it's phenomenal. I mean, this is something Michael always talks about is how do we as del technologies help in progress in the human kind? And if this is something that we can learn from, I think it's going to be phenomenal. >> I think I think that's so interesting. Not only is that a good angle for Del Technologies, the thing that strikes me is the access to artist trees, voices, new voices that may be missed in the prop the vetting process the old way. But, you know, you got to know where we're going. No, in the venture, cobble way seen this with democratization of seed labs and incubators where, if you can create access to the story, tells on the artists we're gonna have one more exposure to people might have missed. But also as things change, like whether it's Ray Ray beaming and streaming we saw in the gaming side to volumetric or volumetric things, you're gonna have a better canvas, more paint brushes on the creative side and more action. Is that the mission to get AC Get those artists in there? Is it? Is that part of the core mission submission? Because you're going to be essentially incubating new opportunities really fast. >> It's, uh, it's very important to me. Personally. I know it speaks of the values of both Sony and L. I like to call it the democratization of storytelling. You know, I've been very blessed again, a Hollywood producer, and we maybe curate a certain kind of movie, a certain kind of experience. But there's so many voices around the world that need to be hurt, and there are so many stories that otherwise can't be enabled. Imagine a story that perhaps is >> a unique special voice but requires distance. It requires five disparate locations. Perhaps it's in London Piccadilly Circus and in Times Square. And perhaps it's overto Abu Dhabi on DH Libya somewhere because that's part of the story. We can now collapse geography and bring those locations to a central place and allow a story to be told that may not otherwise have been able to be created. And that's vital to the fabric of storytelling. Worldwide >> is going to change the creative process to You don't have to have that waterfall kind of mentality like we don't talk about intact. You're totally distributed content, decentralized, potentially the creative process going change with all the tools and also the visual tools. >> That's right. It's >> almost becoming unlimited. >> You want it to be unlimited. You want the human spirit to be unlimited. You want to be able to elevate people on. That's the great thing about what we're trying to achieve and will achieve. >> It is your right. I mean, it is interesting, you know, we were just talking about this too. We're in, you know, as an example, shock tank. Yes, right. I mean, they obviously did it the filming and stuff, and then they don't have the access, let's say to the right studio, but The fact is, there had all this done on DH. No, they had all the rendering. They had the captured already done. You could now go out and do your chute without having all the space you needed. >> That's right. In the case of Shark Tank, which shoots a Sony Pictures studios, they knew they had a real estate issue. The fact of the matter is, there's a limited amount of sound stages around the world. They needed to sound stages and only had access to one. So we went in and we did a volumetric image acquisition of their exit interview stage. They're set. And then when it came time to shoot the second half a season ten, one hundred contestants went into a virtual set and were filmed in that set. And the funny thing is, one of the guys in the truck you know how you have the camera trucks and, you know, off offstage, he leaned into the mike. Is that you guys, could you move that plant a couple inches to the left and somebody said, Uh, I don't think we can do it right now, he said. We're on a movie lot. You could move a plant. They said, No, it's physically not there. We're on innovation studios goes Oh, that's right. It's virtual mind. >> So he was fooled. >> He was pulled. In a way, we're >> being hashing it out within a team. When we heard about some of the things you know Glenn and Team are doing is think about this. If you have to teach people when we are running short of doctors, right? Yeah, if you could. With this technology and the learnings that come from here, if you could go have an expert surgeon do surgery once you're captured, it would be nice. Just imagine, to take that learning, go to the new surgeons of the future and trained them and so they can get into the act without actually doing it. So my point in all this is this is where I think we can take technology, that next level where we can not only learn from one specific industry, but we could potentially put it to human good in terms of what we could to and not only preparing the next of doctors, but also take it to the next level. >> This was a great theme to Michael Dell put out there about these new kinds of use case is that the time is now to do before. Maybe you couldn't get there with technology, but maybe aspirational, eh? Let's do it. I could see that. Glenn, I want to ask you specifically. The time is now. This is all kind of coming together. Timing's pretty good. It's only gonna get better. It's gonna be good. Tech, Tech mojo Coming for the creative side. Where were we before? Because I could almost imagine this is not a new vision for you. Probably seen it now that this house here now what was it like before for, um and compare contrast where you were a few years ago, maybe decades. Now what's different? Why? Why is this so important? >> You know, for me, there's a fundamental change in how we can create content and how we can tell stories. It used to be the two most expensive words in the movie TV industry were what if today that the most important words to me or what if Because what if we could collapse geography? What if we could empower a new story? Technology is at a place where if we can dream it. Chances are we can make it a reality. We're changing the dynamics of how we may content. He used to be lights, action, camera. I think it's now lights, action, compute power action, you know, is that kind of difference. >> That is an amazing vision. I think society now has opportunities to kind of take that from distance learning to distance connections, the distance sharing experiences, whether it's immersion, virtual analog face the face. I could really be powerful. Yeah, >> and this is not even a year old. >> That's right. >> So if you look at your your launch, you said, I think let june fourth twenty eighteen. What? Where do you go from here? I mean, like we said, this is like, unlimited possibilities. But besides putting Robbie in the movie, naturally, Yes, of course I have >> a star here >> who video. >> So I got to say he's got star power. >> What's what. The next year? Exactly. >> Very exciting. I will say we have shark tank Thie Advanced Imaging Society gives an award for being the first volume metric set ever put out on the airwaves. Uh, for that television show was a great honor. Uh, we have already captured, uh, men in black. We captured a fifty thousand square foot stage that had the men in black headquarters has been used for commercials to market the film that comes out this June. We have captured sets where television >> shows and in the in hopes that they got a second season and one television show called up and said, Guys, we got the second season so they don't have to go back to what was a very expensive set and a beautiful set >> Way captured that set. It reminds me of a story of productions and a friend of mine said, which is every year. The greatest gift I have is building a beautiful set and and to me, the biggest challenges. When I say, remember that sent you built four years ago. I need that again. Now you can go >> toe hard, replicate the exact set, you capture it digitally. It lives. >> That's exactly it. >> And this is amazing. I mean, I'd love to do a cube set into do ah, like a simulcasts. Virtually. >> So. This is the next thing John and Lisa. You guys could be sitting anywhere going forward. We don't have to be really sitting here you could be doing. What do you have to do? And, you know, you got everything rendered >> captured. We don't have to come to Vegas twenty times a year. >> We billed upset once >> You want to see you here believing that So I'LL take that >> visual is a really beautiful thing. So if we can with hologram just seeing people doing conscious. But Hollywood Frank Zappa just did a concert hologram concert, but bringing real people and from communities around the world where the localization diversity right into a content mixture is just so powerful. >> Actually, you said something very interesting, John, which is one of the other teams to which is, if you have a globally connected society and he wanted try and personalize it to that particular nation ethnicity group. You can do that easily now because you can probably pop in actors from the local area with the same city. Yeah, think about it. >> It's surely right. >> There's a cascade of transformations that that this is going Teo to generate. I mean just thinking of how different even acting schools and drama schools will be well, teaching people how to behave in these virtual environments, right? >> How to immerse themselves in these environments. And we have tricks up our sleeves that Khun put the actor in that moment through projection mapping and the other techniques that allow filmmakers and actors to actually understand the world. They're about to stepped in rather than a green screen and saying, OK, there's going to be a creature over here is gonna be blue Water Falls over there will actually be able to see that environment because that environment will exist before they step on the stage. >> Well, great job the Dale Partnership On my final question, Glenn free since you're awesome and got a great vision so smart, experienced, I've been really thinking a lot about how visualization and artistry are coming together and how disciplines silo disciplines like music. They do great music, but they're not translating to the graphics. It was just some about Ray tracing and the impact with GP use for immersive experiences, which was seeing on the client side of the house. It del So you got the back and stuff, but you metrics. And so, as artist trees, the next generation come up. This is now a link between the visual that audio, the storytelling. It's not a siloed. >> It is not >> your I want to get your vision on. How do you see this playing out and your advice for young artists? That might be, you know, looked as country. What do you know? That's not how we do it. >> Well, the beautiful thing is that there are new ways to tell stories. You know, Hollywood has evolved over the last century. If you look at the studios and still exist, they have all evolved, and that's why they do exist. Great storytellers evolved. We tell stories differently, so long as we can emotionally relate to the story that's being told. I say Do it in your own voice. The cinematic power is among us. We're blessed that when we look back, we have that shared experience, whether it's animate from Japan or traditional animation from Walt Disney, everybody shares a similar history. Now it's opportunity to author our new stories and we can do that and physical assets and volumetric assets and weakened blend the real and the unreal. With the compute power. The world is our oyster. >> Wow, >> What a nice >> trap right there. >> Exactly that is, um I dropped the transformation of Hollywood. What? And it's really think the tip of the iceberg. Unlimited story potential. Thank you, Glenn. Thank you. This has been a fascinating cannot wait to hear, See and feel and touch What's next for Sony Animation studios With your technology power We appreciate your time. >> Yeah, Thank you. Thank you both of >> our pleasure for John Farrier. I'm Lisa Martin. You're watching the Cube lie from Del Technologies World twenty nineteen We've just wrapped up Day two we'LL see you tomorrow.

>> Hello, everyone. Welcome to the special CUBE conversation. I'm John Furrier, the co-founder of SiliconANGLE Media, co-host of theCUBE. We're here at Sundance Film Festival, the Intel Tech Lounge for a one on one conversation with Kane Lee, who's the head of content at Baobab Studios in California. Thanks for joining me here at the Intel Tech Lounge. >> Really excited to be here. >> You know we just had a panel on the new creative here, and Intel is showing some great technology. Things like volumetric, all kinds of really hardcore tech. Really powering some of the VR, AR, mixed reality, all the trends that are happening around user experience. But, a new creative artist is out there. A new storyteller. It could be a 12 year old to a 50 year old. You're in the middle of it. You're an award winning producer. So you're building the stories, you're building the content. What's the biggest thing happening here at Sundance? >> I think it's really interesting, because content has always been my passion. Good storytelling. And growing up, it was always books and films, and all these traditional mediums that inspired me to sort of dream, and right here in Sundance, we're in the middle of a great sea change going on, because technology and art are coming together in such a fast pace, to really usher in the new generation of storytelling, and we're all very fortunate to be in the middle of that. This is a very unique period in our history as humans, and our culture, to challenge what storytelling really means, because VR, for us at Baobab, is the next great medium. And Sundance recognizes that. Technology companies like Intel recognize that. So we're all coming together at the film festival, and working together to define what that will mean. >> Kane, you're an Emmy award winning producer. Baobab's doing some cutting edge work. Take a minute to talk about what Baobab is doing, and why is it so relevant? We know it's cool. We've interviewed the CEO and Founder before. Share with the audience, what is Baobab doing? Why is it so relevant? >> So, we formed a couple years ago, and at the time, VR was, and it still is, in its very nascent stage. One thing that we recognized, was an opportunity to try to create content that would appeal for people from the ages of five to 105. There was a lot of documentaries, there was a lot of experiential art house type of material. And there was a lot of gaming type of content for VR. For us, we're big lovers of animation and how that unites families, kids, grandparents, teenagers, and we saw an opportunity to try to create content that could appeal to all of these different types of people through animation. So that's sort of our mission, is to inspire your childlike sense of wonder, using two mediums that are so meant for each other, which are animation and VR. >> I'd like to talk about some of the work you got going on a little bit later, but I want to talk about that 12 year old in his room, or the 16 year old that's got a full rig, tricked out with the keyboard, they're laying down music, they're building music, they're gaming, they might be creating art. They are a living, breathing creative. And, they're self learning. They're jumping on Youtube. They're jumping into VR meetups and groups. They're self learning. >> Kane: Absolutely. >> How do you connect to them? What do they do? What's the playbook? How do these people go to the next level? What's the industry doing around this? >> I think, one example I'll give is, I was at Annecy Film Festival, and that's one of the biggest animation focused film festivals in the world, and I was showcasing our very first piece, it was called Invasion, starring Ethan Hawke, where you're actually in the body of a bunny rabbit, and you meet another bunny rabbit. You create a bond. And together you thwart an alien invasion on Earth. What was so interesting to me, was I had never seen that sort of, that demo, that teenage demo, where young boys and girls would actually bring their parents back to the experience, and say this is what I want to study in college. This is what I want to do in art school. So, I think that they, growing up with all this new technology, really sort of get the idea of being in realtime, and having storytelling in realtime. And seeing that level of interest from that age group was very sort of affirming to us that we're on the right track, in terms of the next generation of storytelling. >> Well you guys are definitely on the right track, I can say that. But I think what your point confirms, and connects the dots for people that might not be in the industry is that the old tech world was, the geeks did it, software was an art and you had to be in that CS club. The democratization is a big trend here, and what you're talking about is, people are humanizing, they can see real emotional, practical examples. So the young guns, the young kids, they don't have baggage. They look at it with a clean slate and going, I want that. I can see myself using this. I can self actualize with this. So really kind of tips the scales, and proves the point. >> Absolutely. We world premiered Asteroids, our second VR experience, starring Elizabeth Banks, and one of the biggest millennial stars, Ingrid Nilson, last year at Sundance. Even had the first red carpet VR premiere in Sundance history. And watching the younger generation, it was our first piece where we actually used the controllers that had just come out in that past year. And watching them go in with no preconceived notions on what using controllers could be, to be a character in the experience, it was just fascinating, because they picked it up faster than anyone, and learned the language of being a character, and having hand controllers as a robot, so you could play fetch with an alien dog, or you could mirror their actions, or they might mirror yours, and creating these bonds and these experiences. So, that sort of fresh perspective is really exciting. >> Talk about the role of these experiences, and how they connect people, because one of the big trends also online today, in today's, I would say, yeah the peg the evolution is, you're really getting into the immersive experience, I believe that. But, content creates bonds between people, and good experiences creates glue between relationships, and forges new ones, maybe enhances existing ones. This is a big part of the media. >> Absolutely. For us, emotional connection is the key to getting people to put on headsets, and to come back to our experiences. And that emotional connection for us, is what we've witnessed, in terms of people forming bonds with our characters. So, everyone knows that VR can bring you to brand new worlds, and exciting places, and immerse you in places that you can never go. But, the one thing that I think we learned in our experience with VR, is that if you can create a bond between the user and other characters in the experience that they believe is real, and we use psychology, technology, and storytelling to do that, then they want to come back again and again. So, one of the trickiest parts of VR is trying to get people to have repeat views. And the feedback we've gotten from a lot of the technology platforms is people come back time and time again, and it seems to be because they actually believe these characters are real, and that they're friends. >> So talk about your journey, because you're at the front end of this wave, and you're participating, you're creating art, you're creating work product. You're building technology with the Baobab Studios. What would you do if you were 16? If you were a sophomore in high school, knowing what you know, and you could go back in time, or you could be today what you know at 16, what would you do? >> When I was 16, I had no idea what I was going to do. When I graduated from college, I had no idea what I was going to do. But what I will say is, VR is really unique because it's so interdisciplinary. So, it actually invites people from all different fabrics of society, and different types of education. The most, I would encourage 16 year olds to just be who they are, and to play. And if I talked to my 16 year old self, I would have just encouraged myself to follow my interest and pursuits more, because many years later, actually VR has brought me back to a lot of my roots, and different things that I studied growing up, and was fascinated by. >> So it ignited your passion. >> Absolutely. >> Or things that you were really into, that you might have forgotten. Is that- >> Yeah, I studied something called symbolic systems at Stanford University, and I had no idea what I was doing. It combined computer science, psychology, linguistics, and philosophy. And the first thing I did after college was pursue potentially a career as a lawyer. But now it all makes sense. VR makes, brings everything together. >> What could have been, you know? >> Absolutely. >> Well, a lot of neural network, symbolic systems, this is the underpinnings of this complex fabric that is powering this content market. So I'd love to get your thoughts. Is there a success formula that you're seeing emerging, I know there's no silver bullet yet. A lot of experimentation. A lot of new things happening. But as this technology, and the scaffolding around it is being built, while also original content is being built, it's still evolving. What's the success formula, and what's the pitfall? What to stay away from? >> I think it's about, it's really about good storytelling. And I think it's a time to be courageous and brave, and put forward stories that wouldn't have otherwise been told in the more traditional mediums. Our latest project in production that I'm so personally excited about, is called Legend of Crow. It stars John Legend as a beautiful bird with the most beautiful feathers, and the most gorgeous voice, who during dark and cold times, must go on a heroes journey to bring light back to the world. Something I feel like in this day and age, a lot of people can relate to. But, on top of this story being based upon a beautiful Native American legend that hasn't really been exposed to the world, we've taken the opportunity to take the themes of diversity and self sacrifice, and self acceptance, to create an all star cast of minorities and women, and that's something I feel the younger generations can really relate to, because having worked a lot in Hollywood as a producer in traditional TV and film, things take a while, and there's a certain way of casting and doing things that follow an older model, and I think younger audiences are excited to have a character like Moth in our experience who speaks both Spanish and English, because that's the way the world is today. >> So I got to ask you a quick, you brought up diversity and inclusion kind of in your comment. I got to bring this up, because you guys do hit a nice demographic that I think is super relevant and important, the younger generation. So I talk to a lot of young people all the time. I say things like, you don't need to be a computer scientist to get into this game. You can be super smart. You don't need to learn how to code hardcore coding to get into this. And they respond to that. And that's one kind of, I would say, narrative that conventional wisdom might not be right. And the other one is the diversity. So my son, 16 year old, says, "Dad, your generation is so politically correct. All this nonsense." So, the younger generation is not living what we're living in, in these dark times, I would say, certainly with diversity, but how does VR really equalize? And will the storm pass? Diversity, inclusion, all that great stuff that are core issues, certainly are being worked on. But, do we see hope here? >> Absolutely. I think disruption in the form of a new technology and a new medium is, while scary to some people, is actually the most exciting and fertile time to equalize. Our CEO, Maureen Fan, who is a college classmate of mine, always wanted to work in animation. And she finally saw the right opportunity when VR came, and we put on headsets for the first time, and saw how there could be a new wave of exciting animators, through this disruptive technology. Because everyone else in more traditional animation is so focused on the old model, and the old ways of doing things, of getting things off the ground, of financing, of creating certain kinds of content that have been proven over time, in the old sort of studio model. >> What were some of those things that were instrumental in this breakout, to forge this new ground? >> I think a lot of it is the technology finally being ready. Our CTO, Larry Cutler, actually studied virtual reality at Stanford a decade before Maureen and I were there, and he had always been waiting for the right time to go into VR. >> Does he preach down, hey kids, I used to walk in the snow with bare feet to you guys, or has he, what's his role, how's he doing? >> He's amazing. He was the head of global character tech for all of Dreamworks animation, and like I said, I think one thing that distinguishes us from some of the other people in VR is that we're so focused on characters, so focused on them making eye contact with you, or with their facial features reacting in realtime, and being very believable, and forging that bond between you and that character. So, for us, that character technology, and having the top people in that space work with us, is the long term thing that is going to differentiate us from the crowd. >> I'd like to get your reaction to my comment about the computer science, and that's mainly, mostly a Silicon Valley thing, living in Palo Alto, so, but people are struggling when they go to college. What should I major in? And there's a narrative right now, oh you got to learn how to code, you got to be a computer science major. You don't. You don't have to be a CS major. Some of the most creative and technical brilliance can also come from other disciplines. What's your reaction to that, and what's your advice? >> I think people should just follow their effort. Because, if you follow what naturally comes to you, what you're good at, and that also has meaning and interest to you, and something that you can get feedback along the way, which is the great thing about being in a growing space, you are going to just spend your, you're going to spend a lot of late nights doing that stuff, and you can always bring it into your career path when that happens. And I think, we're in a very DIY time in VR. No one knows anything. We're constantly making mistakes, but then learning from them. And that's the most exciting process of being where we are. So, to people who are of college age, I would just tell them follow your effort. If you're interested in VR, it's an exciting time to just do it yourself. Learn from your mistakes. And then, and try to create something new. >> What does the new creative mean to you. When you hear that, new creative, what does that mean to you? >> You know, it's interesting being at these talks and panels, and at all these festivals, because I feel like a lot of people are looking for that new innovator who comes out of nowhere, and sort of just redefines the industry. And that could very well happen. But I actually think what's really exciting about right now is, it's more about having, understanding the bridge between all the different mediums and disciplines. I think new things are created when you combine areas that have not been traditionally aligned. So for example, Orson Welles arguably created one of the first great cinematic masterpieces in Citizen Kane, but he was able to do so by bringing values from theater, and from radio, and areas where he sort of learned the art of storytelling. And he was able to combine them in new and interesting ways that people hadn't seen before. So, for me it's less about looking for that silver bullet of a creative person who comes out of nowhere, but these younger generations who understand these different mediums, combining them and creating connections with them in an exciting way. >> Brooks Brown from Starbury Studios said on the panel, the next breakout star is going to be the kid in the basement that no one's ever heard of. >> Very possibly, but that kid in the basement, he needs to be passionate about a lot of different disciplines. So, what we've tried to emulate in doing so, is bringing the best people in gaming, bringing the best people from traditional film, bringing people who had interests in a lot of different areas, different art forms, and letting them kind of play together and learn from each other. Argue with each other, you know? And then come up with something that no one's seen before. >> We're going to have to come up with a camera, so that could be like an experiment. Like it's just a reality show in and of itself. All that talent, multi discipline together. >> Absolutely. >> John: It's like dynamite ready to explode. >> It's the challenge, it's the blessing, it's the curse and the blessing of our medium right now, because there's so much more to discover, but if people come in and have an open mind, and are willing ... If the people from Hollywood are willing to learn from the people who do gaming in Silicon Valley, who are open to learning from the people in New York who grew up on live theater, I feel those, finding that intersection, finding those beautiful intersections are where we're going to thrive. >> Well you guys highlight that multi disciplinary thing, but also highlights why diversity is so important. Diversity brings the most perspectives to the table, the most data, most contribution. It might be a little bit longer to work through the arguments, right? You got to be patient. >> Absolutely you have to be patient. We're really lucky to be working with John Legend on our VR piece. He had actually been looking for several years to find, wanting to play in this space, but not wanting to do it with the wrong partner at the wrong time. So, it's, there's an art to timing in everything that we do right now, and when we presented to him the story we're doing with the Legend of Crow, it felt like the perfect sort of match. >> Legend of Crow coming out. Head of Content, Kane Lee here, Baobab Studios. Thanks for spending the time here on the Cube Conversation. What's the timing of the release of the program? >> Probably late spring, but we're going to be announcing some news around that soon, and we have some more exciting updates about it that I can't wait to share. >> Alright, we are here at the Intel Tech Lounge as the Cube's Conversation at Sundance Film Festival, part of our coverage of Sundance 2018. I'm John Furrier, thanks for watching.

>> Announcer: Live, from Orlando, Florida it's the Cube, covering Grace Hopper's Celebration of Women in Computing, brought to you by SiliconANGLE Media. >> Welcome back to the Cube's coverage of the Grace Hopper Conference, here at the Orange County Convention Center. I'm your host, Rebecca Knight. We're joined by Maureen Fan. She is the CEO and co-founder of Baobab Studio, which is the industry's leading VR animation studio, so, welcome Maureen. >> Thank you so much for having me. >> It's excited to talk to you, because you just won an Emmy. Congratulations. >> Thank you. >> You just won an Emmy for "Invasion", so, tell us a little bit about invasion. >> It was our first piece ever and it was just an experiment to see if we could even create VR and it's a story about these adorable little bunnies and you are actually a bunny too, you look down, you have a furry, little bunny body and these aliens that come to try to take over the Earth, with their advanced technology and you and your bunny friend end up saving the entire Earth and it's starring Ethan Hawk and it just came out last year. And we're really excited, because it became the number one top downloaded VR experience across all the headsets and it's getting turned into a Hollywood Feature Film. >> Very cool, very cool >> Thank you. >> And you have another film coming out too and this is "Rainbow Crow" >> Yes. >> Tell our viewers a little bit about "Rainbow". >> So, "Rainbow Crow" is based off of a Native American legend about how the crow used to have beautiful rainbow feathers and a beautiful singing voice and it's John Legend, in our piece and how he decides to sacrifice himself, by flying into the sun to bring warmth and fire back to the Earth and in the process, loses all his beautiful feathers, becomes black and burnt and his voice becomes like the crow's voice, but it's about how beauty is within and there's also, huge themes about diversity and how if you learn to accept yourself and your differences, that's when you can accept others and that's why we specifically cast minorities and women, so, we have John Legend, Constance Wu, from "Fresh off the Boat" as a skunk character, Diego Luna, from "Rogue One", for the moth character, as well as Randy Edmunds, as a Native American elder, narrator, and we have a whole bunch of other stars to announce, soon-- >> Well we cannot wait to hear. That's already an amazing line-up. >> Thank you. >> So, when you're thinking about "Rainbow Crow" and particularly, because it's VR, which is relatively new, still experimental, I mean, the messages of diversity, does it lend itself to VR, better than, say, a standard animation film? >> Absolutely, because if you think about stories that you just watch passively, the reason why we need stories and humanity, in general is to experience characters and stories beyond those we can experience in our real lives and we think, "Oh, how would I feel if I was in the "position of that character or what would I do?" but in VR, because you are actually playing a character in a role, you actually have to decide at that point, "what would I do?" so, it's not just a experience that I just see, it's one where I'm actively experiencing it, so, I create a memory and remember afterwards and there's all these research studies at Stanford by Jeremy Bailenson, who is head of the Stanford VR lab, that shows if you are made a homeless person, inside a VR experience and you have to go through a day in the life of a homeless person or you would look in the mirror and see that you are a black woman, that you, when you get out of the headset, you act completely differently. You have so much more empathy for these people than you would normally and so, it gets you to care about these characters, in a way that you don't normally and in VR, because you're doing it in a real-time game engine, these characters can act and react to what you do, so you can turn that empathy into action and actually act upon your caring, which we call compassion, so, it really changes you in a way, that normal, traditional story-telling doesn't, so, I think that having voices and characters that are different, in front of the screen, and also, behind the screen are really important to create role models and different perspectives for all the people out in the world. >> And these are movies that are targeted at kids, children, but do you see a future in which, where there is more targeted at adults, for VR? >> Absolutely. The funny thing is, in the beginning, the VR distributors didn't think that people would want our VR animation, because they're like, "Oh, it's just going to be these hardcore boys "that just love to play games. "Are they going to want this animation?" and VR is targeted towards adults, that's why they were surprised and we were surprised when "Invasion" became the number one downloaded VR experience. It shows that the audience for our content is from little kids to grandmas and everyone in between and that's probably why it became the top downloaded experience, is because it's universally appealing and has themes that are appealing to just, every single generation, so, absolutely, but for VR to become mainstream, there needs to be more universally appealing content. Right now, the content tends to be for games, like parkour games, as well as documentaries, which are two amazing pieces of content for this medium, but for it to become mainstream, we need more universally appealing content and I'm excited about, right now, it's a new industry. This is when minorities and women in particular, can enter the space and help shape the voices and the direction of the industry. >> That is exactly where I wanted to go next. So, let's talk a little bit about Baobab Studio. It's not that old and VR is not that old and so, why are there more opportunities, would you say, for women, and minorities? >> Well, if you look at traditional animation in the traditional entertainment fields that's a very mature industry and to break into that industry, you have to either have lots and lots of money or unfair distribution advantage, but VR, there's technological disruption, which means nobody has an advantage at all, means it's a level playing field and everybody can come in and start something, so, this is a perfect opportunity, when there's low barriers to entry of coming in, for women and minorities, anyone who wants their voice heard, to start companies or to make experiences and we can set the groundwork, because there's no one telling us what we can and can't do, because no one actually knows what we can and can't do yet. >> Right, right, but yet you are still of a female, asian figurehead of a studio, that will hopefully, someday be a major studio. You're working on it, but do you find that people take you as seriously in Hollywood? I mean, what are you coming up against? >> Well, it's really interesting, because I heard for even fundraising is one of the hardest parts of starting a company and there was a Stanford Research Study that showed that if you took a deck, a pitch deck for a company and you had a male voice-over versus a female voice-over the male voice-over was, I don't remember what, it was like 50% more likely to get funded than the woman with the same exact pitch deck, so I knew from that and they also show that if you are married and wear a ring you're taken more seriously, or if you're less attractive, also, you're taken more seriously and my hypothesis and some of the hypotheses out there, is it takes away the whole entire female attraction thing, like what does it mean to be an attractive female, so, I had to go into the meetings, knowing this. I even considered wearing a ring. I considered wearing a paper bag over my head. >> A bag over you head. Exactly, exactly. >> But at the same time I felt that I need to be myself and the best thing to, there's a correlation between the perceived leadership and confidence, that I needed to just go in there and be confident in myself so, I knew that, that could work against me, but I just needed to be myself, but I had to make sure that I was really confident and really believed in what I said and honestly, besides being confident and aggressive, I also, felt comfortable, because a lot of the people I talked to, I knew from my network and I had many of my male friends and female friends who knew these VC's, do the initial introduction, so I felt more comfortable going in, for them already knowing that I had somebody else saying that I was awesome. >> Yeah, and you've had many mentors and sponsors along the way too. >> Absolutely, I would say it's one of the most important things, for my career from the very beginning. When I graduated from business school, I actually emailed my mentors and said, "Here are the things I care about for finding a job." I didn't have to go find any jobs. They actually found all these jobs. for me, set up informational interviews, for me and I just went in and did it, all the informational interviews, got the offers and just choose one of them that I wanted to be in but, even for starting my company, my co-founder, Eric Darnell was a write and director of all four "Madagascar" films and I got introduced to him, through my mentor, Glen Entis who is the co-founder of PDI Dreamworks Animation and he was my mentor through Zynga and then, Gen Entis introduced me to Alvy Ray Smith, who is the co-founder of Pixar, who also became our advisor, Alvy Ray Smith, then introduced us to Glen Keane, who is the animator for "Little Mermaid", "Alaadin". >> The power of networks. >> It was all through the network and through my mentors that I found, a lot of the opportunities that I have and they also helped my through my personal life and how to navigate being entrepreneur and I rely on them so much. >> So, beyond finding the right mentor and sponsor what else would you give, your parting words to the young Maureen fans out there? >> I think there's a tendency for society to pressure you to conform, to money, fame, beauty and you don't need to listen to that and you don't need to be bucketed. I designed my own major at Stanford and with an eBay, I took four different roles. I just kept on creating my own roles and refusing to be bucketed as a creative or a suit and you can be who you are and create a category onto yourself and so, don't feel pressured to listen to what society is telling you. The other thing, is if you are faced with pushed back for being promoted and you feel like it's maybe because you're a woman, we have a tendency as women to start blaming ourselves and thinking there's something wrong with us, versus research shows men are most likely to blame the system, don't let it affect you and bring you down, because you need to actually be confident and believe in yourself in order to rise above. >> Great. Great advice. Maureen, it's been a pleasure having you on the show. Thanks so much. >> Thank you. >> And best of luck to you. >> Thank you, so much. >> Hope you win another Emmy. >> Thank you. >> Come back and talk to us again. >> Thank you. I'm Rebecca Knight, we'll have more from Grace Hopper, just after this. (techno music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

(upbeat music) >> Hello and welcome to theCube's coverage of International Women's Day. I'm John Furrier, your host of "theCUBE." We've got great two remote guests coming into our Palo Alto Studios, some tech athletes, as we say, people that've been in the trenches, years of experience, Lena Smart, CISO at MongoDB, Cube alumni, and Tara Hernandez, VP of Developer Productivity at MongoDB as well. Thanks for coming in to this program and supporting our efforts today. Thanks so much. >> Thanks for having us. >> Yeah, everyone talk about the journey in tech, where it all started. Before we get there, talk about what you guys are doing at MongoDB specifically. MongoDB is kind of gone the next level as a platform. You have your own ecosystem, lot of developers, very technical crowd, but it's changing the business transformation. What do you guys do at Mongo? We'll start with you, Lena. >> So I'm the CISO, so all security goes through me. I like to say, well, I don't like to say, I'm described as the ones throat to choke. So anything to do with security basically starts and ends with me. We do have a fantastic Cloud engineering security team and a product security team, and they don't report directly to me, but obviously we have very close relationships. I like to keep that kind of church and state separate and I know I've spoken about that before. And we just recently set up a physical security team with an amazing gentleman who left the FBI and he came to join us after 26 years for the agency. So, really starting to look at the physical aspects of what we offer as well. >> I interviewed a CISO the other day and she said, "Every day is day zero for me." Kind of goofing on the Amazon Day one thing, but Tara, go ahead. Tara, go ahead. What's your role there, developer productivity? What are you focusing on? >> Sure. Developer productivity is kind of the latest description for things that we've described over the years as, you know, DevOps oriented engineering or platform engineering or build and release engineering development infrastructure. It's all part and parcel, which is how do we actually get our code from developer to customer, you know, and all the mechanics that go into that. It's been something I discovered from my first job way back in the early '90s at Borland. And the art has just evolved enormously ever since, so. >> Yeah, this is a very great conversation both of you guys, right in the middle of all the action and data infrastructures changing, exploding, and involving big time AI and data tsunami and security never stops. Well, let's get into, we'll talk about that later, but let's get into what motivated you guys to pursue a career in tech and what were some of the challenges that you faced along the way? >> I'll go first. The fact of the matter was I intended to be a double major in history and literature when I went off to university, but I was informed that I had to do a math or a science degree or else the university would not be paid for. At the time, UC Santa Cruz had a policy that called Open Access Computing. This is, you know, the late '80s, early '90s. And anybody at the university could get an email account and that was unusual at the time if you were, those of us who remember, you used to have to pay for that CompuServe or AOL or, there's another one, I forget what it was called, but if a student at Santa Cruz could have an email account. And because of that email account, I met people who were computer science majors and I'm like, "Okay, I'll try that." That seems good. And it was a little bit of a struggle for me, a lot I won't lie, but I can't complain with how it ended up. And certainly once I found my niche, which was development infrastructure, I found my true love and I've been doing it for almost 30 years now. >> Awesome. Great story. Can't wait to ask a few questions on that. We'll go back to that late '80s, early '90s. Lena, your journey, how you got into it. >> So slightly different start. I did not go to university. I had to leave school when I was 16, got a job, had to help support my family. Worked a bunch of various jobs till I was about 21 and then computers became more, I think, I wouldn't say they were ubiquitous, but they were certainly out there. And I'd also been saving up every penny I could earn to buy my own computer and bought an Amstrad 1640, 20 meg hard drive. It rocked. And kind of took that apart, put it back together again, and thought that could be money in this. And so basically just teaching myself about computers any job that I got. 'Cause most of my jobs were like clerical work and secretary at that point. But any job that had a computer in front of that, I would make it my business to go find the guy who did computing 'cause it was always a guy. And I would say, you know, I want to learn how these work. Let, you know, show me. And, you know, I would take my lunch hour and after work and anytime I could with these people and they were very kind with their time and I just kept learning, so yep. >> Yeah, those early days remind me of the inflection point we're going through now. This major C change coming. Back then, if you had a computer, you had to kind of be your own internal engineer to fix things. Remember back on the systems revolution, late '80s, Tara, when, you know, your career started, those were major inflection points. Now we're seeing a similar wave right now, security, infrastructure. It feels like it's going to a whole nother level. At Mongo, you guys certainly see this as well, with this AI surge coming in. A lot more action is coming in. And so there's a lot of parallels between these inflection points. How do you guys see this next wave of change? Obviously, the AI stuff's blowing everyone away. Oh, new user interface. It's been called the browser moment, the mobile iPhone moment, kind of for this generation. There's a lot of people out there who are watching that are young in their careers, what's your take on this? How would you talk to those folks around how important this wave is? >> It, you know, it's funny, I've been having this conversation quite a bit recently in part because, you know, to me AI in a lot of ways is very similar to, you know, back in the '90s when we were talking about bringing in the worldwide web to the forefront of the world, right. And we tended to think in terms of all the optimistic benefits that would come of it. You know, free passing of information, availability to anyone, anywhere. You just needed an internet connection, which back then of course meant a modem. >> John: Not everyone had though. >> Exactly. But what we found in the subsequent years is that human beings are what they are and we bring ourselves to whatever platforms that are there, right. And so, you know, as much as it was amazing to have this freely available HTML based internet experience, it also meant that the negatives came to the forefront quite quickly. And there were ramifications of that. And so to me, when I look at AI, we're already seeing the ramifications to that. Yes, are there these amazing, optimistic, wonderful things that can be done? Yes. >> Yeah. >> But we're also human and the bad stuff's going to come out too. And how do we- >> Yeah. >> How do we as an industry, as a community, you know, understand and mitigate those ramifications so that we can benefit more from the positive than the negative. So it is interesting that it comes kind of full circle in really interesting ways. >> Yeah. The underbelly takes place first, gets it in the early adopter mode. Normally industries with, you know, money involved arbitrage, no standards. But we've seen this movie before. Is there hope, Lena, that we can have a more secure environment? >> I would hope so. (Lena laughs) Although depressingly, we've been in this well for 30 years now and we're, at the end of the day, still telling people not to click links on emails. So yeah, that kind of still keeps me awake at night a wee bit. The whole thing about AI, I mean, it's, obviously I am not an expert by any stretch of the imagination in AI. I did read (indistinct) book recently about AI and that was kind of interesting. And I'm just trying to teach myself as much as I can about it to the extent of even buying the "Dummies Guide to AI." Just because, it's actually not a dummies guide. It's actually fairly interesting, but I'm always thinking about it from a security standpoint. So it's kind of my worst nightmare and the best thing that could ever happen in the same dream. You know, you've got this technology where I can ask it a question and you know, it spits out generally a reasonable answer. And my team are working on with Mark Porter our CTO and his team on almost like an incubation of AI link. What would it look like from MongoDB? What's the legal ramifications? 'Cause there will be legal ramifications even though it's the wild, wild west just now, I think. Regulation's going to catch up to us pretty quickly, I would think. >> John: Yeah, yeah. >> And so I think, you know, as long as companies have a seat at the table and governments perhaps don't become too dictatorial over this, then hopefully we'll be in a good place. But we'll see. I think it's a really interest, there's that curse, we're living in interesting times. I think that's where we are. >> It's interesting just to stay on this tech trend for a minute. The standards bodies are different now. Back in the old days there were, you know, IEEE standards, ITF standards. >> Tara: TPC. >> The developers are the new standard. I mean, now you're seeing open source completely different where it was in the '90s to here beginning, that was gen one, some say gen two, but I say gen one, now we're exploding with open source. You have kind of developers setting the standards. If developers like it in droves, it becomes defacto, which then kind of rolls into implementation. >> Yeah, I mean I think if you don't have developer input, and this is why I love working with Tara and her team so much is 'cause they get it. If we don't have input from developers, it's not going to get used. There's going to be ways of of working around it, especially when it comes to security. If they don't, you know, if you're a developer and you're sat at your screen and you don't want to do that particular thing, you're going to find a way around it. You're a smart person. >> Yeah. >> So. >> Developers on the front lines now versus, even back in the '90s, they're like, "Okay, consider the dev's, got a QA team." Everything was Waterfall, now it's Cloud, and developers are on the front lines of everything. Tara, I mean, this is where the standards are being met. What's your reaction to that? >> Well, I think it's outstanding. I mean, you know, like I was at Netscape and part of the crowd that released the browser as open source and we founded mozilla.org, right. And that was, you know, in many ways kind of the birth of the modern open source movement beyond what we used to have, what was basically free software foundation was sort of the only game in town. And I think it is so incredibly valuable. I want to emphasize, you know, and pile onto what Lena was saying, it's not just that the developers are having input on a sort of company by company basis. Open source to me is like a checks and balance, where it allows us as a broader community to be able to agree on and enforce certain standards in order to try and keep the technology platforms as accessible as possible. I think Kubernetes is a great example of that, right. If we didn't have Kubernetes, that would've really changed the nature of how we think about container orchestration. But even before that, Linux, right. Linux allowed us as an industry to end the Unix Wars and as someone who was on the front lines of that as well and having to support 42 different operating systems with our product, you know, that was a huge win. And it allowed us to stop arguing about operating systems and start arguing about software or not arguing, but developing it in positive ways. So with, you know, with Kubernetes, with container orchestration, we all agree, okay, that's just how we're going to orchestrate. Now we can build up this huge ecosystem, everybody gets taken along, right. And now it changes the game for what we're defining as business differentials, right. And so when we talk about crypto, that's a little bit harder, but certainly with AI, right, you know, what are the checks and balances that as an industry and as the developers around this, that we can in, you know, enforce to make sure that no one company or no one body is able to overly control how these things are managed, how it's defined. And I think that is only for the benefit in the industry as a whole, particularly when we think about the only other option is it gets regulated in ways that do not involve the people who actually know the details of what they're talking about. >> Regulated and or thrown away or bankrupt or- >> Driven underground. >> Yeah. >> Which would be even worse actually. >> Yeah, that's a really interesting, the checks and balances. I love that call out. And I was just talking with another interview part of the series around women being represented in the 51% ratio. Software is for everybody. So that we believe that open source movement around the collective intelligence of the participants in the industry and independent of gender, this is going to be the next wave. You're starting to see these videos really have impact because there are a lot more leaders now at the table in companies developing software systems and with AI, the aperture increases for applications. And this is the new dynamic. What's your guys view on this dynamic? How does this go forward in a positive way? Is there a certain trajectory you see? For women in the industry? >> I mean, I think some of the states are trying to, again, from the government angle, some of the states are trying to force women into the boardroom, for example, California, which can be no bad thing, but I don't know, sometimes I feel a bit iffy about all this kind of forced- >> John: Yeah. >> You know, making, I don't even know how to say it properly so you can cut this part of the interview. (John laughs) >> Tara: Well, and I think that they're >> I'll say it's not organic. >> No, and I think they're already pulling it out, right. It's already been challenged so they're in the process- >> Well, this is the open source angle, Tara, you are getting at it. The change agent is open, right? So to me, the history of the proven model is openness drives transparency drives progress. >> No, it's- >> If you believe that to be true, this could have another impact. >> Yeah, it's so interesting, right. Because if you look at McKinsey Consulting or Boston Consulting or some of the other, I'm blocking on all of the names. There has been a decade or more of research that shows that a non homogeneous employee base, be it gender or ethnicity or whatever, generates more revenue, right? There's dollar signs that can be attached to this, but it's not enough for all companies to want to invest in that way. And it's not enough for all, you know, venture firms or investment firms to grant that seed money or do those seed rounds. I think it's getting better very slowly, but socialization is a much harder thing to overcome over time. Particularly, when you're not just talking about one country like the United States in our case, but around the world. You know, tech centers now exist all over the world, including places that even 10 years ago we might not have expected like Nairobi, right. Which I think is amazing, but you have to factor in the cultural implications of that as well, right. So yes, the openness is important and we have, it's important that we have those voices, but I don't think it's a panacea solution, right. It's just one more piece. I think honestly that one of the most important opportunities has been with Cloud computing and Cloud's been around for a while. So why would I say that? It's because if you think about like everybody holds up the Steve Jobs, Steve Wozniak, back in the '70s, or Sergey and Larry for Google, you know, you had to have access to enough credit card limit to go to Fry's and buy your servers and then access to somebody like Susan Wojcicki to borrow the garage or whatever. But there was still a certain amount of upfrontness that you had to be able to commit to, whereas now, and we've, I think, seen a really good evidence of this being able to lease server resources by the second and have development platforms that you can do on your phone. I mean, for a while I think Africa, that the majority of development happened on mobile devices because there wasn't a sufficient supply chain of laptops yet. And that's no longer true now as far as I know. But like the power that that enables for people who would otherwise be underrepresented in our industry instantly opens it up, right? And so to me that's I think probably the biggest opportunity that we've seen from an industry on how to make more availability in underrepresented representation for entrepreneurship. >> Yeah. >> Something like AI, I think that's actually going to take us backwards if we're not careful. >> Yeah. >> Because of we're reinforcing that socialization. >> Well, also the bias. A lot of people commenting on the biases of the large language inherently built in are also problem. Lena, I want you to weigh on this too, because I think the skills question comes up here and I've been advocating that you don't need the pedigree, college pedigree, to get into a certain jobs, you mentioned Cloud computing. I mean, it's been around for you think a long time, but not really, really think about it. The ability to level up, okay, if you're going to join something new and half the jobs in cybersecurity are created in the past year, right? So, you have this what used to be a barrier, your degree, your pedigree, your certification would take years, would be a blocker. Now that's gone. >> Lena: Yeah, it's the opposite. >> That's, in fact, psychology. >> I think so, but the people who I, by and large, who I interview for jobs, they have, I think security people and also I work with our compliance folks and I can't forget them, but let's talk about security just now. I've always found a particular kind of mindset with security folks. We're very curious, not very good at following rules a lot of the time, and we'd love to teach others. I mean, that's one of the big things stem from the start of my career. People were always interested in teaching and I was interested in learning. So it was perfect. And I think also having, you know, strong women leaders at MongoDB allows other underrepresented groups to actually apply to the company 'cause they see that we're kind of talking the talk. And that's been important. I think it's really important. You know, you've got Tara and I on here today. There's obviously other senior women at MongoDB that you can talk to as well. There's a bunch of us. There's not a whole ton of us, but there's a bunch of us. And it's good. It's definitely growing. I've been there for four years now and I've seen a growth in women in senior leadership positions. And I think having that kind of track record of getting really good quality underrepresented candidates to not just interview, but come and join us, it's seen. And it's seen in the industry and people take notice and they're like, "Oh, okay, well if that person's working, you know, if Tara Hernandez is working there, I'm going to apply for that." And that in itself I think can really, you know, reap the rewards. But it's getting started. It's like how do you get your first strong female into that position or your first strong underrepresented person into that position? It's hard. I get it. If it was easy, we would've sold already. >> It's like anything. I want to see people like me, my friends in there. Am I going to be alone? Am I going to be of a group? It's a group psychology. Why wouldn't? So getting it out there is key. Is there skills that you think that people should pay attention to? One's come up as curiosity, learning. What are some of the best practices for folks trying to get into the tech field or that's in the tech field and advancing through? What advice are you guys- >> I mean, yeah, definitely, what I say to my team is within my budget, we try and give every at least one training course a year. And there's so much free stuff out there as well. But, you know, keep learning. And even if it's not right in your wheelhouse, don't pick about it. Don't, you know, take a look at what else could be out there that could interest you and then go for it. You know, what does it take you few minutes each night to read a book on something that might change your entire career? You know, be enthusiastic about the opportunities out there. And there's so many opportunities in security. Just so many. >> Tara, what's your advice for folks out there? Tons of stuff to taste, taste test, try things. >> Absolutely. I mean, I always say, you know, my primary qualifications for people, I'm looking for them to be smart and motivated, right. Because the industry changes so quickly. What we're doing now versus what we did even last year versus five years ago, you know, is completely different though themes are certainly the same. You know, we still have to code and we still have to compile that code or package the code and ship the code so, you know, how well can we adapt to these new things instead of creating floppy disks, which was my first job. Five and a quarters, even. The big ones. >> That's old school, OG. There it is. Well done. >> And now it's, you know, containers, you know, (indistinct) image containers. And so, you know, I've gotten a lot of really great success hiring boot campers, you know, career transitioners. Because they bring a lot experience in addition to the technical skills. I think the most important thing is to experiment and figuring out what do you like, because, you know, maybe you are really into security or maybe you're really into like deep level coding and you want to go back, you know, try to go to school to get a degree where you would actually want that level of learning. Or maybe you're a front end engineer, you want to be full stacked. Like there's so many different things, data science, right. Maybe you want to go learn R right. You know, I think it's like figure out what you like because once you find that, that in turn is going to energize you 'cause you're going to feel motivated. I think the worst thing you could do is try to force yourself to learn something that you really could not care less about. That's just the worst. You're going in handicapped. >> Yeah and there's choices now versus when we were breaking into the business. It was like, okay, you software engineer. They call it software engineering, that's all it was. You were that or you were in sales. Like, you know, some sort of systems engineer or sales and now it's,- >> I had never heard of my job when I was in school, right. I didn't even know it was a possibility. But there's so many different types of technical roles, you know, absolutely. >> It's so exciting. I wish I was young again. >> One of the- >> Me too. (Lena laughs) >> I don't. I like the age I am. So one of the things that I did to kind of harness that curiosity is we've set up a security champions programs. About 120, I guess, volunteers globally. And these are people from all different backgrounds and all genders, diversity groups, underrepresented groups, we feel are now represented within this champions program. And people basically give up about an hour or two of their time each week, with their supervisors permission, and we basically teach them different things about security. And we've now had seven full-time people move from different areas within MongoDB into my team as a result of that program. So, you know, monetarily and time, yeah, saved us both. But also we're showing people that there is a path, you know, if you start off in Tara's team, for example, doing X, you join the champions program, you're like, "You know, I'd really like to get into red teaming. That would be so cool." If it fits, then we make that happen. And that has been really important for me, especially to give, you know, the women in the underrepresented groups within MongoDB just that window into something they might never have seen otherwise. >> That's a great common fit is fit matters. Also that getting access to what you fit is also access to either mentoring or sponsorship or some sort of, at least some navigation. Like what's out there and not being afraid to like, you know, just ask. >> Yeah, we just actually kicked off our big mentor program last week, so I'm the executive sponsor of that. I know Tara is part of it, which is fantastic. >> We'll put a plug in for it. Go ahead. >> Yeah, no, it's amazing. There's, gosh, I don't even know the numbers anymore, but there's a lot of people involved in this and so much so that we've had to set up mentoring groups rather than one-on-one. And I think it was 45% of the mentors are actually male, which is quite incredible for a program called Mentor Her. And then what we want to do in the future is actually create a program called Mentor Them so that it's not, you know, not just on the female and so that we can live other groups represented and, you know, kind of break down those groups a wee bit more and have some more granularity in the offering. >> Tara, talk about mentoring and sponsorship. Open source has been there for a long time. People help each other. It's community-oriented. What's your view of how to work with mentors and sponsors if someone's moving through ranks? >> You know, one of the things that was really interesting, unfortunately, in some of the earliest open source communities is there was a lot of pervasive misogyny to be perfectly honest. >> Yeah. >> And one of the important adaptations that we made as an open source community was the idea, an introduction of code of conducts. And so when I'm talking to women who are thinking about expanding their skills, I encourage them to join open source communities to have opportunity, even if they're not getting paid for it, you know, to develop their skills to work with people to get those code reviews, right. I'm like, "Whatever you join, make sure they have a code of conduct and a good leadership team. It's very important." And there are plenty, right. And then that idea has come into, you know, conferences now. So now conferences have codes of contact, if there are any good, and maybe not all of them, but most of them, right. And the ideas of expanding that idea of intentional healthy culture. >> John: Yeah. >> As a business goal and business differentiator. I mean, I won't lie, when I was recruited to come to MongoDB, the culture that I was able to discern through talking to people, in addition to seeing that there was actually women in senior leadership roles like Lena, like Kayla Nelson, that was a huge win. And so it just builds on momentum. And so now, you know, those of us who are in that are now representing. And so that kind of reinforces, but it's all ties together, right. As the open source world goes, particularly for a company like MongoDB, which has an open source product, you know, and our community builds. You know, it's a good thing to be mindful of for us, how we interact with the community and you know, because that could also become an opportunity for recruiting. >> John: Yeah. >> Right. So we, in addition to people who might become advocates on Mongo's behalf in their own company as a solution for themselves, so. >> You guys had great successful company and great leadership there. I mean, I can't tell you how many times someone's told me "MongoDB doesn't scale. It's going to be dead next year." I mean, I was going back 10 years. It's like, just keeps getting better and better. You guys do a great job. So it's so fun to see the success of developers. Really appreciate you guys coming on the program. Final question, what are you guys excited about to end the segment? We'll give you guys the last word. Lena will start with you and Tara, you can wrap us up. What are you excited about? >> I'm excited to see what this year brings. I think with ChatGPT and its copycats, I think it'll be a very interesting year when it comes to AI and always in the lookout for the authentic deep fakes that we see coming out. So just trying to make people aware that this is a real thing. It's not just pretend. And then of course, our old friend ransomware, let's see where that's going to go. >> John: Yeah. >> And let's see where we get to and just genuine hygiene and housekeeping when it comes to security. >> Excellent. Tara. >> Ah, well for us, you know, we're always constantly trying to up our game from a security perspective in the software development life cycle. But also, you know, what can we do? You know, one interesting application of AI that maybe Google doesn't like to talk about is it is really cool as an addendum to search and you know, how we might incorporate that as far as our learning environment and developer productivity, and how can we enable our developers to be more efficient, productive in their day-to-day work. So, I don't know, there's all kinds of opportunities that we're looking at for how we might improve that process here at MongoDB and then maybe be able to share it with the world. One of the things I love about working at MongoDB is we get to use our own products, right. And so being able to have this interesting document database in order to put information and then maybe apply some sort of AI to get it out again, is something that we may well be looking at, if not this year, then certainly in the coming year. >> Awesome. Lena Smart, the chief information security officer. Tara Hernandez, vice president developer of productivity from MongoDB. Thank you so much for sharing here on International Women's Day. We're going to do this quarterly every year. We're going to do it and then we're going to do quarterly updates. Thank you so much for being part of this program. >> Thank you. >> Thanks for having us. >> Okay, this is theCube's coverage of International Women's Day. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (light airy music) >> Hi, everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante. I'm here with my co-host Dave Nicholson. Lisa Martin is in the house. John Furrier is pounding the news from our Palo Alto studio. We are super excited to be talking about cloud at the edge, what that means. Charles Tsai is here. He's the Senior Director of product management at Dell Technologies and Peter Fetterolf is the Chief Technology Officer at ACG Business Analytics, a firm that goes deep into the TCO and the telco space, among other things. Gents, welcome to theCUBE. Thanks for coming on. Thank you. >> Good to be here. >> Yeah, good to be here. >> So I've been in search all week of the elusive next wave of monetization for the telcos. We know they make great money on connectivity, they're really good at that. But they're all talking about how they can't let this happen again. Meaning we can't let the over the top vendors yet again, basically steal our cookies. So we're going to not mess it up this time. We're going to win in the monetization. Charles, where are those monetization opportunities? Obviously at the edge, the telco cloud at the edge. What is that all about and where's the money? >> Well, Dave, I think from a Dell's perspective, what we want to be able to enable operators is a solution that enable them to roll out services much quicker, right? We know there's a lot of innovation around IoT, MEG and so on and so forth, but they continue to rely on traditional technology and way of operations is going to take them years to enable new services. So what Dell is doing is now, creating the entire vertical stack from the hardware through CAST and automation that enable them, not only to push out services very quickly, but operating them using cloud principles. >> So it's when you say the entire vertical stack, it's the integrated hardware components with like, for example, Red Hat on top- >> Right. >> Or a Wind River? >> That's correct. >> Okay, and then open API, so the developers can create workloads, I presume data companies. We just had a data conversation 'cause that was part of the original stack- >> That's correct. >> So through an open ecosystem, you can actually sort of recreate that value, correct? >> That's correct. >> Okay. >> So one thing Dell is doing, is we are offering an infrastructure block where we are taking over the overhead of certifying every release coming from the Red Hat or the Wind River of the world, right? We want telcos to spend their resources on what is going to generate them revenue. Not the overhead of creating this cloud stack. >> Dave, I remember when we went through this in the enterprise and you had companies like, you know, IBM with the AS400 and the mainframe saying it's easier to manage, which it was, but it's still, you know, it was subsumed by the open systems trend. >> Yeah, yeah. And I think that's an important thing to probe on, is this idea of what is, what exactly does it mean to be cloud at the edge in the telecom space? Because it's a much used term. >> Yeah. >> When we talk about cloud and edge, in sort of generalized IT, but what specifically does it mean? >> Yeah, so when we talk about telco cloud, first of all it's kind of different from what you're thinking about public cloud today. And there's a couple differences. One, if you look at the big hyperscaler public cloud today, they tend to be centralized in huge data centers. Okay, telco cloud, there are big data centers, but then there's also regional data centers. There are edge data centers, which are your typical like access central offices that have turned data centers, and then now even cell sites are becoming mini data centers. So it's distributed. I mean like you could have like, even in a country like say Germany, you'd have 30,000 soul sites, each one of them being a data center. So it's a very different model. Now the other thing I want to go back to the question of monetization, okay? So how do you do monetization? The only way to do that, is to be able to offer new services, like Charles said. How do you offer new services? You have to have an open ecosystem that's going to be very, very flexible. And if we look at where telcos are coming from today, they tend to be very inflexible 'cause they're all kind of single vendor solutions. And even as we've moved to virtualization, you know, if you look at packet core for instance, a lot of them are these vertical stacks of say a Nokia or Ericson or Huawei where you know, you can't really put any other vendors or any other solutions into that. So basically the idea is this kind of horizontal architecture, right? Where now across, not just my central data centers, but across my edge data centers, which would be traditionally my access COs, as well as my cell sites. I have an open environment. And we're kind of starting with, you know, packet core obviously with, and UPFs being distributed, but now open ran or virtual ran, where I can have CUs and DUs and I can split CUs, they could be at the soul site, they could be in edge data centers. But then moving forward, we're going to have like MEG, which are, you know, which are new kinds of services, you know, could be, you know, remote cars it could be gaming, it could be the Metaverse. And these are going to be a multi-vendor environment. So one of the things you need to do is you need to have you know, this cloud layer, and that's what Charles was talking about with the infrastructure blocks is helping the service providers do that, but they still own their infrastructure. >> Yeah, so it's still not clear to me how the service providers win that game but we can maybe come back to that because I want to dig into TCO a little bit. >> Sure. >> Because I have a lot of friends at Dell. I don't have a lot of friends at HPE. I've always been critical when they take an X86 server put a name on it that implies edge and they throw it over the fence to the edge, that's not going to work, okay? We're now seeing, you know we were just at the Dell booth yesterday, you did the booth crawl, which was awesome. Purpose-built servers for this environment. >> Charles: That's right. >> So there's two factors here that I want to explore in TCO. One is, how those next gen servers compare to the previous gen, especially in terms of power consumption but other factors and then how these sort of open ran, open ecosystem stacks compared to proprietary stacks. Peter, can you help us understand those? >> Yeah, sure. And Charles can comment on this as well. But I mean there, there's a couple areas. One is just moving the next generation. So especially on the Intel side, moving from Ice Lake to the Sapphire Rapids is a big deal, especially when it comes to the DU. And you know, with the radios, right? There's the radio unit, the RU, and then there's the DU the distributed unit, and the CU. The DU is really like part of the radio, but it's virtualized. When we moved from Ice lake to Sapphire Rapids, which is third generation intel to fourth generation intel, we're literally almost doubling the performance in the DU. And that's really important 'cause it means like almost half the number of servers and we're talking like 30, 40, 50,000 servers in some cases. So, you know, being able to divide that by two, that's really big, right? In terms of not only the the cost but all the TCO and the OpEx. Now another area that's really important, when I was talking moving from these vertical silos to the horizontal, the issue with the vertical silos is, you can't place any other workloads into those silos. So it's kind of inefficient, right? Whereas when we have the horizontal architecture, now you can place workloads wherever you want, which basically also means less servers but also more flexibility, more service agility. And then, you know, I think Charles can comment more, specifically on the XR8000, some things Dell's doing, 'cause it's really exciting relative to- >> Sure. >> What's happening in there. >> So, you know, when we start looking at putting compute at the edge, right? We recognize the first thing we have to do is understand the environment we are going into. So we spend with a lot of time with telcos going to the south side, going to the edge data center, looking at operation, how do the engineer today deal with maintenance replacement at those locations? Then based on understanding the operation constraints at those sites, we create innovation and take a traditional server, remodel it to make sure that we minimize the disruption to the operations, right? Just because we are helping them going from appliances to open compute, we do not want to disrupt what is have been a very efficient operation on the remote sites. So we created a lot of new ideas and develop them on general compute, where we believe we can save a lot of headache and disruptions and still provide the same level of availability, resiliency, and redundancy on an open compute platform. >> So when we talk about open, we don't mean generic? Fair? See what I mean? >> Open is more from the software workload perspective, right? A Dell server can run any type of workload that customer intend. >> But it's engineered for this? >> Environment. >> Environment. >> That's correct. >> And so what are some of the environmental issues that are dealt with in the telecom space that are different than the average data center? >> The most basic one, is in most of the traditional cell tower, they are deployed within cabinets instead of racks. So they are depth constraints that you just have no access to the rear of the chassis. So that means on a server, is everything you need to access, need to be in the front, nothing should be in the back. Then you need to consider how labor union come into play, right? There's a lot of constraint on who can go to a cell tower and touch power, who can go there and touch compute, right? So we minimize all that disruption through a modular design and make it very efficient. >> So when we took a look at XR8000, literally right here, sitting on the desk. >> Uh-huh. >> Took it apart, don't panic, just pulled out some sleds and things. >> Right, right. >> One of the interesting demonstrations was how it compared to the size of a shoe. Now apparently you hired someone at Dell specifically because they wear a size 14 shoe, (Charles laughs) so it was even more dramatic. >> That's right. >> But when you see it, and I would suggest that viewers go back and take a look at that segment, specifically on the hardware. You can see exactly what you just referenced. This idea that everything is accessible from the front. Yeah. >> So I want to dig in a couple things. So I want to push back a little bit on what you were saying about the horizontal 'cause there's the benefit, if you've got the horizontal infrastructure, you can run a lot more workloads. But I compare it to the enterprise 'cause I, that was the argument, I've made that argument with converged infrastructure versus say an Oracle vertical stack, but it turned out that actually Oracle ran Oracle better, okay? Is there an analog in telco or is this new open architecture going to be able to not only service the wide range of emerging apps but also be as resilient as the proprietary infrastructure? >> Yeah and you know, before I answer that, I also want to say that we've been writing a number of white papers. So we have actually three white papers we've just done with Dell looking at infrastructure blocks and looking at vertical versus horizontal and also looking at moving from the previous generation hardware to the next generation hardware. So all those details, you can find the white papers, and you can find them either in the Dell website or at the ACG research website >> ACGresearch.com? >> ACG research. Yeah, if you just search ACG research, you'll find- >> Yeah. >> Lots of white papers on TCO. So you know, what I want to say, relative to the vertical versus horizontal. Yeah, obviously in the vertical side, some of those things will run well, I mean it won't have issues. However, that being said, as we move to cloud native, you know, it's very high performance, okay? In terms of the stack, whether it be a Red Hat or a VMware or other cloud layers, that's really become much more mature. It now it's all CNF base, which is really containerized, very high performance. And so I don't think really performance is an issue. However, my feeling is that, if you want to offer new services and generate new revenue, you're not going to do it in vertical stacks, period. You're going to be able to do a packet core, you'll be able to do a ran over here. But now what if I want to offer a gaming service? What if I want to do metaverse? What if I want to do, you have to have an environment that's a multi-vendor environment that supports an ecosystem. Even in the RAN, when we look at the RIC, and the xApps and the rApps, these are multi-vendor environments that's going to create a lot of flexibility and you can't do that if you're restricted to, I can only have one vendor running on this hardware. >> Yeah, we're seeing these vendors work together and create RICs. That's obviously a key point, but what I'm hearing is that there may be trade offs, but the incremental value is going to overwhelm that. Second question I have, Peter is, TCO, I've been hearing a lot about 30%, you know, where's that 30% come from? Is it Op, is it from an OpEx standpoint? Is it labor, is it power? Is it, you mentioned, you know, cutting the number of servers in half. If I can unpack the granularity of that TCO, where's the benefit coming from? >> Yeah, the answer is yes. (Peter and Charles laugh) >> Okay, we'll do. >> Yeah, so- >> One side that, in terms of, where is the big bang for the bucks? >> So I mean, so you really need to look at the white paper to see details, but definitely power, definitely labor, definitely reducing the number of servers, you know, reducing the CapEx. The other thing is, is as you move to this really next generation horizontal telco cloud, there's the whole automation and orchestration, that is a key component as well. And it's enabled by what Dell is doing. It's enabled by the, because the thing is you're not going to have end-to-end automation if you have all this legacy stuff there or if you have these vertical stacks where you can't integrate. I mean you can automate that part and then you have separate automation here, you separate. you need to have integrated automation and orchestration across the whole thing. >> One other point I would add also, right, on the hardware perspective, right? With the customized hardware, what we allow operator to do is, take out the existing appliance and push a edge optimized server without reworking the entire infrastructure. There is a significant saving where you don't have to rethink about what is my power infrastructure, right? What is my security infrastructure? The server is designed to leverage the existing, what is already there. >> How should telco, Charles, plan for this transformation? Are there specific best practices that you would recommend in terms of the operational model? >> Great question. I think first thing is do an inventory of what you have. Understand what your constraints are and then come to Dell, we will love to consult with you, based on our experience on the best practices. We know how to minimize additional changes. We know how to help your support engineer, understand how to shift appliance based operation to a cloud-based operation. >> Is that a service you offer? Is that a pre-sales freebie? What is maybe both? >> It's both. >> Yeah. >> It's both. >> Yeah. >> Guys- >> Just really quickly. >> We're going to wrap. >> The, yeah. Dave loves the TCO discussion. I'm always thinking in terms of, well how do you measure TCO when you're comparing something where you can't do something to an environment where you're going to be able to do something new? And I know that that's always the challenge in any kind of emerging market where things are changing, any? >> Well, I mean we also look at, not only TCO, but we look at overall business case. So there's basically service at GLD and revenue and then there's faster time to revenues. Well, and actually ACG, we actually have a platform called the BAE or Business Analytics Engine that's a very sophisticated simulation cloud-based platform, where we can actually look at revenue month by month. And we look at what's the impact of accelerating revenue by three months. By four months. >> So you're looking into- >> By six months- >> So you're forward looking. You're just not consistently- >> So we're not just looking at TCO, we're looking at the overall business case benefit. >> Yeah, exactly right. There's the TCO, which is the hard dollars. >> Right. >> CFO wants to see that, he or she needs to see that. But you got to, you can convince that individual, that there's a business case around it. >> Peter: Yeah. >> And then you're going to sign up for that number. >> Peter: Yeah. >> And they're going to be held to it. That's the story the world wants. >> At the end of the day, telcos have to be offered new services 'cause look at all the money that's been spent. >> Dave: Yeah, that's right. >> On investment on 5G and everything else. >> 0.5 trillion over the next seven years. All right, guys, we got to go. Sorry to cut you off. >> Okay, thank you very much. >> But we're wall to wall here. All right, thanks so much for coming on. >> Dave: Fantastic. >> All right, Dave Vellante, for Dave Nicholson. Lisa Martin's in the house. John Furrier in Palo Alto Studios. Keep it right there. MWC 23 live from the Fira in Barcelona. (light airy music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> While never really meant to be a consumer tech event, the rapid ascendancy of smartphones sucked much of the air out of Mobile World Congress over the years, now MWC. And while the device manufacturers continue to have a major presence at the show, the maturity of intelligent devices, longer life cycles, and the disaggregation of the network stack, have put enterprise technologies front and center in the telco business. Semiconductor manufacturers, network equipment players, infrastructure companies, cloud vendors, software providers, and a spate of startups are eyeing the trillion dollar plus communications industry as one of the next big things to watch this decade. Hello, and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we bring you part two of our ongoing coverage of MWC '23, with some new data on enterprise players specifically in large telco environments, a brief glimpse at some of the pre-announcement news and corresponding themes ahead of MWC, and some of the key announcement areas we'll be watching at the show on theCUBE. Now, last week we shared some ETR data that showed how traditional enterprise tech players were performing, specifically within the telecoms vertical. Here's a new look at that data from ETR, which isolates the same companies, but cuts the data for what ETR calls large telco. The N in this cut is 196, down from 288 last week when we included all company sizes in the dataset. Now remember the two dimensions here, on the y-axis is net score, or spending momentum, and on the x-axis is pervasiveness in the data set. The table insert in the upper left informs how the dots and companies are plotted, and that red dotted line, the horizontal line at 40%, that indicates a highly elevated net score. Now while the data are not dramatically different in terms of relative positioning, there are a couple of changes at the margin. So just going down the list and focusing on net score. Azure is comparable, but slightly lower in this sector in the large telco than it was overall. Google Cloud comes in at number two, and basically swapped places with AWS, which drops slightly in the large telco relative to overall telco. Snowflake is also slightly down by one percentage point, but maintains its position. Remember Snowflake, overall, its net score is much, much higher when measuring across all verticals. Snowflake comes down in telco, and relative to overall, a little bit down in large telco, but it's making some moves to attack this market that we'll talk about in a moment. Next are Red Hat OpenStack and Databricks. About the same in large tech telco as they were an overall telco. Then there's Dell next that has a big presence at MWC and is getting serious about driving 16G adoption, and new servers, and edge servers, and other partnerships. Cisco and Red Hat OpenShift basically swapped spots when moving from all telco to large telco, as Cisco drops and Red Hat bumps up a bit. And VMware dropped about four percentage points in large telco. Accenture moved up dramatically, about nine percentage points in big telco, large telco relative to all telco. HPE dropped a couple of percentage points. Oracle stayed about the same. And IBM surprisingly dropped by about five points. So look, I understand not a ton of change in terms of spending momentum in the large sector versus telco overall, but some deltas. The bottom line for enterprise players is one, they're just getting started in this new disruption journey that they're on as the stack disaggregates. Two, all these players have experience in delivering horizontal solutions, but now working with partners and identifying big problems to be solved, and three, many of these companies are generally not the fastest moving firms relative to smaller disruptive disruptors. Now, cloud has been an exception in fairness. But the good news for the legacy infrastructure and IT companies is that the telco transformation and the 5G buildout is going to take years. So it's moving at a pace that is very favorable to many of these companies. Okay, so looking at just some of the pre-announcement highlights that have hit the wire this week, I want to give you a glimpse of the diversity of innovation that is occurring in the telecommunication space. You got semiconductor manufacturers, device makers, network equipment players, carriers, cloud vendors, enterprise tech companies, software companies, startups. Now we've included, you'll see in this list, we've included OpeRAN, that logo, because there's so much buzz around the topic and we're going to come back to that. But suffice it to say, there's no way we can cover all the announcements from the 2000 plus exhibitors at the show. So we're going to cherry pick here and make a few call outs. Hewlett Packard Enterprise announced an acquisition of an Italian private cellular network company called AthoNet. Zeus Kerravala wrote about it on SiliconANGLE if you want more details. Now interestingly, HPE has a partnership with Solana, which also does private 5G. But according to Zeus, Solona is more of an out-of-the-box solution, whereas AthoNet is designed for the core and requires more integration. And as you'll see in a moment, there's going to be a lot of talk at the show about private network. There's going to be a lot of news there from other competitors, and we're going to be watching that closely. And while many are concerned about the P5G, private 5G, encroaching on wifi, Kerravala doesn't see it that way. Rather, he feels that these private networks are really designed for more industrial, and you know mission critical environments, like factories, and warehouses that are run by robots, et cetera. 'Cause these can justify the increased expense of private networks. Whereas wifi remains a very low cost and flexible option for, you know, whatever offices and homes. Now, over to Dell. Dell announced its intent to go hard after opening up the telco network with the announcement that in the second half of this year it's going to begin shipping its infrastructure blocks for Red Hat. Remember it's like kind of the converged infrastructure for telco with a more open ecosystem and sort of more flexible, you know, more mature engineered system. Dell has also announced a range of PowerEdge servers for a variety of use cases. A big wide line bringing forth its 16G portfolio and aiming squarely at the telco space. Dell also announced, here we go, a private wireless offering with airspan, and Expedo, and a solution with AthoNet, the company HPE announced it was purchasing. So I guess Dell and HPE are now partnering up in the private wireless space, and yes, hell is freezing over folks. We'll see where that relationship goes in the mid- to long-term. Dell also announced new lab and certification capabilities, which we said last week was going to be critical for the further adoption of open ecosystem technology. So props to Dell for, you know, putting real emphasis and investment in that. AWS also made a number of announcements in this space including private wireless solutions and associated managed services. AWS named Deutsche Telekom, Orange, T-Mobile, Telefonica, and some others as partners. And AWS announced the stepped up partnership, specifically with T-Mobile, to bring AWS services to T-Mobile's network portfolio. Snowflake, back to Snowflake, announced its telecom data cloud. Remember we showed the data earlier, it's Snowflake not as strong in the telco sector, but they're continuing to move toward this go-to market alignment within key industries, realigning their go-to market by vertical. It also announced that AT&T, and a number of other partners, are collaborating to break down data silos specifically in telco. Look, essentially, this is Snowflake taking its core value prop to the telco vertical and forming key partnerships that resonate in the space. So think simplification, breaking down silos, data sharing, eventually data monetization. Samsung previewed its future capability to allow smartphones to access satellite services, something Apple has previously done. AMD, Intel, Marvell, Qualcomm, are all in the act, all the semiconductor players. Qualcomm for example, announced along with Telefonica, and Erickson, a 5G millimeter network that will be showcased in Spain at the event this coming week using Qualcomm Snapdragon chipset platform, based on none other than Arm technology. Of course, Arm we said is going to dominate the edge, and is is clearly doing so. It's got the volume advantage over, you know, traditional Intel, you know, X86 architectures. And it's no surprise that Microsoft is touting its open AI relationship. You're going to hear a lot of AI talk at this conference as is AI is now, you know, is the now topic. All right, we could go on and on and on. There's just so much going on at Mobile World Congress or MWC, that we just wanted to give you a glimpse of some of the highlights that we've been watching. Which brings us to the key topics and issues that we'll be exploring at MWC next week. We touched on some of this last week. A big topic of conversation will of course be, you know, 5G. Is it ever going to become real? Is it, is anybody ever going to make money at 5G? There's so much excitement around and anticipation around 5G. It has not lived up to the hype, but that's because the rollout, as we've previous reported, is going to take years. And part of that rollout is going to rely on the disaggregation of the hardened telco stack, as we reported last week and in previous Breaking Analysis episodes. OpenRAN is a big component of that evolution. You know, as our RAN intelligent controllers, RICs, which essentially the brain of OpenRAN, if you will. Now as we build out 5G networks at massive scale and accommodate unprecedented volumes of data and apply compute-hungry AI to all this data, the issue of energy efficiency is going to be front and center. It has to be. Not only is it a, you know, hot political issue, the reality is that improving power efficiency is compulsory or the whole vision of telco's future is going to come crashing down. So chip manufacturers, equipment makers, cloud providers, everybody is going to be doubling down and clicking on this topic. Let's talk about AI. AI as we said, it is the hot topic right now, but it is happening not only in consumer, with things like ChatGPT. And think about the theme of this Breaking Analysis in the enterprise, AI in the enterprise cannot be ChatGPT. It cannot be error prone the way ChatGPT is. It has to be clean, reliable, governed, accurate. It's got to be ethical. It's got to be trusted. Okay, we're going to have Zeus Kerravala on the show next week and definitely want to get his take on private networks and how they're going to impact wifi. You know, will private networks cannibalize wifi? If not, why not? He wrote about this again on SiliconANGLE if you want more details, and we're going to unpack that on theCUBE this week. And finally, as always we'll be following the data flows to understand where and how telcos, cloud players, startups, software companies, disruptors, legacy companies, end customers, how are they going to make money from new data opportunities? 'Cause we often say in theCUBE, don't ever bet against data. All right, that's a wrap for today. Remember theCUBE is going to be on location at MWC 2023 next week. We got a great set. We're in the walkway in between halls four and five, right in Congress Square, stand CS-60. Look for us, we got a full schedule. If you got a great story or you have news, stop by. We're going to try to get you on the program. I'll be there with Lisa Martin, co-hosting, David Nicholson as well, and the entire CUBE crew, so don't forget to come by and see us. I want to thank Alex Myerson, who's on production and manages the podcast, and Ken Schiffman, as well, in our Boston studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at SiliconANGLE.com. He does some great editing. Thank you. All right, remember all these episodes they are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcasts. I publish each week on Wikibon.com and SiliconANGLE.com. All the video content is available on demand at theCUBE.net, or you can email me directly if you want to get in touch David.Vellante@SiliconANGLE.com or DM me @DVellante, or comment on our LinkedIn posts. And please do check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. We'll see you next week at Mobile World Congress '23, MWC '23, or next time on Breaking Analysis. (bright music)

>> Hello and welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. Got a variety of interviews across the gamut from topics, women in tech, mentoring, pipelining, developers, open source, executives. Stanford's having International Women's Day celebration with the women in data science, which we're streaming that live as well. Variety of programs. In this segment, Meagen Eisenberg, friend of theCUBE, she's the CMO of Laceworks, is an amazing executive, got a great journey story as a CMO but she's also actively advising startups, companies and really pays it forward. I want to say Meagen, thank you for coming on the program and thanks for sharing. >> Yeah, thank you for having me. I'm happy to be here. >> Well, we're going to get into some of the journey celebrations that you've gone through and best practice what you've learned is pay that forward. But I got to say, one of the things that really impresses me about you as an executive is you get stuff done. You're a great CMO but also you're advised a lot of companies, you have a lot of irons in the fires and you're advising companies and sometimes they're really small startups to bigger companies, and you're paying it forward, which I love. That's kind of the spirit of this day. >> Yeah, I mean, I agree with you. When I think about my career, a lot of it was looking to mentors women out in the field. This morning I was at a breakfast by Eileen and we had the CEO of General Motors on, and she was talking about her journey nine years as a CEO. And you know, and she's paying it forward with us. But I think about, you know, when you're advising startups, you know, I've gathered knowledge and pattern recognition and to be able to share that is, you know, I enjoy it. >> Yeah. And the startups are also fun too, but it's not always easy and it can get kind of messy as you know. Some startups don't make it some succeed and it's always like the origination story is kind of rewritten and then that's that messy middle. And then it's like that arrows that don't look like a straight line but everyone thinks it's great and you know, it's not for the faint of heart. And Teresa Carlson, who I've interviewed many times, former Amazon, now she's the president of Flexport, she always says, sometimes startups on certain industries aren't for the faint of heart so you got to have a little bit of metal, right? You got to be tough. And some cases that you don't need that, but startups, it's not always easy. What have you learned? >> Yeah, I mean, certainly in the startup world, grit, creativity. You know, when I was at TripActions travel company, pandemic hits, nobody's traveling. You cut budget, you cut heads, but you focus on the core, right? You focus on what you need to survive. And creativity, I think, wins. And, you know, as a CMO when you're marketing, how do you get through that noise? Even the security space, Lacework, it's a fragmented market. You've got to be differentiated and position yourself and you know, be talking to the right target audience and customers. >> Talk about your journey over the years. What have you learned? What's some observations? Can you share any stories and best practices that someone watching could learn from? I know there's a lot of people coming into the tech space with the generative AI things going on in Cloud computing, scaling to the edge, there's a lot more aperture for technical jobs as well as just new roles and new roles that haven't, you really don't go to college for anymore. You got cybersecurity you're in. What are some of the things that you've done over your career if you can share and some best practices? >> Yeah, I think number one, continual learning. When I look through my career, I was constantly reading, networking. Part of the journey is who you're meeting along the way. As you become more senior, your ability to hire and bring in talent matters a lot. I'm always trying to meet with new people. Yeah, if I look at my Amazon feed of books I've bought, right, it kind of chronicle of my history of things I was learning about. Right now I'm reading a lot about cybersecurity, how the, you know, how how they tell me the world ends is the one I'm reading most recently. But you've got to come up to speed and then know the product, get in there and talk to customers. Certainly on the marketing front, anytime I can talk with the customer and find out how they're using us, why they love us, that, you know, helps me better position and differentiate our company. >> By the way, that book is amazing. I saw Nicole speak on Tuesday night with John Markoff and Palo Alto here. What a great story she told there. I recommend that book to everyone. It goes in and she did eight years of research into that book around zero day marketplaces to all the actors involved in security. And it was very interesting. >> Yeah, I mean, it definitely wakes you up, makes you think about what's going on in the world. Very relevant. >> It's like, yeah, it was happening all the time, wasn't it. All the hacking. But this brings me, this brings up an interesting point though, because you're in a cybersecurity area, which by the way, it's changing very fast. It's becoming a bigger industry. It's not just male dominated, although it is now, it's still male dominated, but it's becoming much more and then just tech. >> Yeah, I mean it's a constantly evolving threat landscape and we're learning, and I think more than ever you need to be able to use the data that companies have and, you know, learn from it. That's one of the ways we position ourselves. We're not just about writing rules that won't help you with those zero day attacks. You've got to be able to understand your particular environment and at any moment if it changes. And that's how we help you detect a threat. >> How is, how are things going with you? Is there any new things you guys got going on? Initiatives or programs for women in tech and increasing the range of diversity inclusion in the industry? Because again, this industry's getting much wider too. It's not just specialized, it's also growing. >> Yes, actually I'm excited. We're launching secured by women, securedbywomen.com and it's very much focused on women in the industry, which some studies are showing it's about 25% of security professionals are women. And we're going to be taking nominations and sponsoring women to go to upcoming security events. And so excited to launch that this month and really celebrate women in security and help them, you know, part of that continual learning that I talked about, making sure they're there learning, having the conversations at the conferences, being able to network. >> I have to ask you, what inspired you to pursue the career in tech? What was the motivation? >> You know, if I think way back, originally I wanted to be on the art side and my dad said, "You can do anything as long as it's in the sciences." And so in undergrad I did computer science and MIS. Graduated with MIS and computer science minor. And when I came out I was a IT engineer at Cisco and you know, that kind of started my journey and decided to go back and get my MBA. And during that process I fell in love with marketing and I thought, okay, I understand the buyer, I can come out and market technology to the IT world and developers. And then from there went to several tech companies. >> I mean my father was an engineer. He had the same kind of thing. You got to be an engineer, it's a steady, stable job. But that time, computer science, I mean we've seen the evolution of computer science now it's the most popular degree at Berkeley we've heard and around the world and the education formats are changing. You're seeing a lot of people's self-training on YouTube. The field has really changed. What are some of the challenges you see for folks trying to get into the industry and how would you advise today if you were talking to your young self, what would you, what would be the narrative? >> Yeah, I mean my drawback then was HTML pages were coming out and I thought it would be fun to design, you know, webpages. So you find something you're passionate about in the space today, whether it's gaming or it's cybersecurity. Go and be excited about it and apply and don't give up, right? Do whatever you can to read and learn. And you're right, there are a ton of online self-help. I always try to hire women and people who are continual learners and are teaching themselves something. And I try to find that in an interview to know that they, because when you come to a business, you're there to solve problems and challenges. And the folks that can do that and be innovative and learn, those are the ones I want on my team. >> It's interesting, you know, technology is now impacting society and we need everyone involved to participate and give requirements. And that kind of leads my next question for you is, like, in your opinion, or let me just step back, let me rephrase. What are some of the things that you see technology being used for, for society right now that will impact people's lives? Because this is not a gender thing. We need everybody involved 'cause society is now digital. Technology's pervasive. The AI trends now we're seeing is clearly unmasking to the mainstream that there's some cool stuff happening. >> Yeah, I mean, I think ChatGPT, think about that. All the different ways we're using it we're writing content and marketing with it. We're, you know, I just read an article yesterday, folks are using it to write children's stories and then selling those stories on Amazon, right? And the amount that they can produce with it. But if you think about it, there's unlimited uses with that technology and you've got all the major players getting involved on it. That one major launch and piece of technology is going to transform us in the next six months to a year. And it's the ability to process so much data and then turn that into just assets that we use and the creativity that's building on top of it. Even TripActions has incorporated ChatGPT into your ability to figure out where you want when you're traveling, what's happening in that city. So it's just, you're going to see that incorporated everywhere. >> I mean we've done an interview before TripAction, your other company you were at. Interesting point you don't have to type in a box to say, I'm traveling, I want a hotel. You can just say, I'm going to Barcelona for Mobile World Congress, I want to have a good time. I want some tapas and a nice dinner out. >> Yes. Yeah. That easy. We're making it easy. >> It's efficiency. >> And actually I was going to say for women specifically, I think the reason why we can do so much today is all the technology and apps that we have. I think about DoorDash, I think about Waze you know, when I was younger you had to print out instructions. Now I get in the car real quick, I need to go to soccer practice, I enter it, I need to pick them up at someone's house. I enter it. It's everything's real time. And so it takes away all the things that I don't add value to and allows me to focus on what I want in business. And so there's a bunch of, you know, apps out there that have allowed me to be so much more efficient and productive that my mother didn't have for sure when I was growing up. >> That is an amazing, I think that actually illustrates, in my opinion, the best example of ChatGPT because the maps and GPS integration were two techs, technologies merged together that replace driving and looking at the map. You know, like how do you do that? Like now it's automatically. This is what's going to happen to creative, to writing, to ideation. I even heard Nicole from her book read said that they're using ChatGPT to write zero day exploits. So you seeing it... >> That's scary stuff. You're right. >> You're seeing it everywhere. Super exciting. Well, I got to ask you before you get into some of the Lacework things that you're involved with, cause I think you're doing great work over there is, what was the most exciting projects you've worked on in your career? You came in Cisco, very technical company, so got the technical chops, CSMIS which stands for Management of Information Science for all the young people out there, that was the state of the art back then. What are some of the exciting things you've done? >> Yeah, I mean, I think about, I think about MongoDB and learning to market to developers. Taking the company public in 2017. Launching Atlas database as a service. Now there's so much more of that, you know, the PLG motion, going to TripActions, you know, surviving a pandemic, still being able to come out of that and all the learnings that went with it. You know, they recently, I guess rebranded, so they're Navan now. And then now back in the security space, you know, 14 years ago I was at ArcSite and we were bought by HP. And so getting back into the security world is exciting and it's transformed a ton as you know, it's way more complicated than it was. And so just understanding the pain of our customers and how we protect them as is fun. And I like, you know, being there from a marketing standpoint. >> Well we really appreciate you coming on and sharing that. I got to ask you, for folks watching they might be interested in some advice that you might have for them and their career in tech. I know a lot of young people love the tech. It's becoming pervasive in our lives, as we mentioned. What advice would you give for folks watching that want to start a career in tech? >> Yeah, so work hard, right? Study, network, your first job, be the best at it because every job after that you get pulled into a network. And every time I move, I'm hiring people from the last job, two jobs before, three jobs before. And I'm looking for people that are working hard, care, you know, are continual learners and you know, add value. What can you do to solve problems at your work and add value? >> What's your secret networking hack or growth hack or tip that you can share? Because you're a great networker by the way. You're amazing and you do add a lot of value. I've seen you in action. >> Well, I try never to eat alone. I've got breakfast, I've got lunch, I've got coffee breaks and dinner. And so when I'm at work, I try and always sit and eat with a team member, new group. If I'm out on the road, I'm, you know, meeting people for lunch, going for dinner, just, you know, don't sit at your desk by yourself and don't sit in the hotel room. Get out and meet with people. >> What do you think about now that we're out of the pandemic or somewhat out of the pandemic so to speak, events are back. >> Yes. >> RSA is coming up. It's a big event. The bigger events are getting bigger and then the other events are kind of smaller being distributed. What's your vision of how events are evolving? >> Yeah, I mean, you've got to be in person. Those are the relationships. Right now more than ever people care about renewals and you are building that rapport. And if you're not meeting with your customers, your competitors are. So what I would say is get out there Lacework, we're going to be at RSA, we're going to be at re:Inforce, we're going to be at all of these events, building relationships, you know, coffee, lunch, and yeah, I think the future of events are here to stay and those that don't embrace in person are going to give up business. They're going to lose market share to us. >> And networking is obviously very key on events as well. >> Yes. >> A good opportunity as always get out to the events. What's the event networking trick or advice do you give folks that are going to get out to the networking world? >> Yeah, schedule ahead of time. Don't go to an event and expect people just to come by for great swag. You should be partnering with your sales team and scheduling ahead of time, getting on people's calendars. Don't go there without having 100 or 200 meetings already booked. >> Got it. All right. Let's talk about you, your career. You're currently at Lacework. It's a very hot company in a hot field, security, very male dominated, you're a leader there. What's it like? What's the strategies? How does a woman get in there and be successful? What are some tricks, observations, any data you can share? What's the best practice? What's the secret sauce from Meagen Eisenberg? >> Yes. Yeah, for Meagen Eisenberg. For Lacework, you know, we're focused on our customers. There's nothing better than getting, being close to them, solving their pain, showcasing them. So if you want to go into security, focus on their, the issues and their problems and make sure they're aware of what you're delivering. I mean, we're focused on cloud security and we go from build time to run time. And that's the draw for me here is we had a lot of, you know, happy, excited customers by what we were doing. And what we're doing is very different from legacy security providers. And it is tapping into the trend of really understanding how much data you have and what's happening in the data to detect the anomalies and the threats that are there. >> You know, one of the conversations that I was just having with a senior leader, she was amazing and I asked her what she thought of the current landscape, the job market, the how to get promoted through the careers, all those things. And the response was interesting. I want to get your reaction. She said interdisciplinary skills are critical. And now more than ever, the having that, having a set of skills, technical and social and emotional are super valuable. Do you agree? What's your reaction to that and what would, how would you reframe that? >> Yeah, I mean, I completely agree. You can't be a leader without balance. You've got to know your craft because you're developing and training your team, but you also need to know the, you know, how to build relationships. You're not going to be successful as a C-level exec if you're not partnering across the functions. As a CMO I need to partner with product, I need to partner with the head of sales, I need to partner with finance. So those relationships matter a ton. I also need to attract the right talent. I want to have solid people on the team. And what I will say in the security, cybersecurity space, there's a talent shortage and you cannot hire enough people to protect your company in that space. And that's kind of our part of it is we reduce the number of alerts that you're getting. So you don't need hundreds of people to detect an issue. You're using technology to show, you know, to highlight the issue and then your team can focus on those alerts that matter. >> Yeah, there's a lot of emerging markets where leveling up and you don't need pedigree. You can just level up skill-wise pretty quickly. Which brings me to the next question for you is how do you keep up with all the tech day-to-day and how should someone watching stay on top of it? Because I mean, you got to be on top of this stuff and you got to ride the wave. It's pretty turbulent, but it's still growing and changing. >> Yeah, it's true. I mean, there's a lot of reading. I'm watching the news. Anytime something comes out, you know, ChatGPT I'm playing with it. I've got a great network and sharing. I'm on, you know, LinkedIn reading articles all the time. I have a team, right? Every time I hire someone, they bring new information and knowledge in and I'm you know, Cal Poly had this learn by doing that was the philosophy at San Luis Obispo. So do it. Try it, don't be afraid of it. I think that's the advice. >> Well, I love some of the points you mentioned community and network. You mentioned networking. That brings up the community question, how could people get involved? What communities are out there? How should they approach communities? 'Cause communities are also networks, but also they're welcoming people in that form networks. So it's a network of networks. So what's your take on how to engage and work with communities? How do you find your tribe? If someone's getting into the business, they want support, they might want technology learnings, what's your approach? >> Yeah, so a few, a few different places. One, I'm part of the operator collective, which is a strong female investment group that's open and works a lot with operators and they're in on the newest technologies 'cause they're investing in it. Chief I think is a great organization as well. You've got a lot of, if you're in marketing, there's a ton of CMO networking events that you can go to. I would say any field, even for us at Lacework, we've got some strong CISO networks and we do dinners around you know, we have one coming up in the Bay area, in Boston, New York, and you can come and meet other CISOs and security leaders. So when I get an invite and you know we all do, I will go to it. I'll carve out the time and meet with others. So I think, you know, part of the community is get out there and, you know, join some of these different groups. >> Meagen, thank you so much for spending the time. Final question for you. How do you see the future of tech evolving and how do you see your role in it? >> Yeah, I mean, marketing's changing wildly. There's so many different channels. You think about all the social media channels that have changed over the last five years. So when I think about the future of tech, I'm looking at apps on my phone. I have three daughters, 13, 11, and 8. I'm telling you, they come to me with new apps and new technology all the time, and I'm paying attention what they're, you know, what they're participating in and what they want to be a part of. And certainly it's going to be a lot more around the data and AI. I think we're only at the beginning of that. So we will continue to, you know, learn from it and wield it and deal with the mass amount of data that's out there. >> Well, you saw TikTok just got banned by the European Commission today around their staff. Interesting times. >> It is. >> Meagen, thank you so much as always. You're a great tech athlete. Been following your career for a while, a long time. You're an amazing leader. Thank you for sharing your story here on theCUBE, celebration of International Women's Day. Every day is IWD and thanks for coming on. >> Thank you for having me. >> Okay. I'm John Furrier here in theCUBE Studios in Palo Alto. Thank you for watching, more to come stay with us. (bright music)

(soft electronic music) >> Hey everyone, welcome to this CUBE conversation as part of the AWS Startup Showcase, season three, episode one, featuring Astronomer. I'm your host, Lisa Martin. I'm in the CUBE's Palo Alto Studios, and today excited to be joined by a couple of guests, a couple of co-founders from Astronomer. Viraj Parekh is with us, as is Paola Peraza-Calderon. Thanks guys so much for joining us. Excited to dig into Astronomer. >> Thank you so much for having us. >> Yeah, thanks for having us. >> Yeah, and we're going to be talking about the role of data orchestration. Paola, let's go ahead and start with you. Give the audience that understanding, that context about Astronomer and what it is that you guys do. >> Mm-hmm. Yeah, absolutely. So, Astronomer is a, you know, we're a technology and software company for modern data orchestration, as you said, and we're the driving force behind Apache Airflow. The Open Source Workflow Management tool that's since been adopted by thousands and thousands of users, and we'll dig into this a little bit more. But, by data orchestration, we mean data pipeline, so generally speaking, getting data from one place to another, transforming it, running it on a schedule, and overall just building a central system that tangibly connects your entire ecosystem of data services, right. So what, that's Redshift, Snowflake, DVT, et cetera. And so tangibly, we build, we at Astronomer here build products powered by Apache Airflow for data teams and for data practitioners, so that they don't have to. So, we sell to data engineers, data scientists, data admins, and we really spend our time doing three things. So, the first is that we build Astro, our flagship cloud service that we'll talk more on. But here, we're really building experiences that make it easier for data practitioners to author, run, and scale their data pipeline footprint on the cloud. And then, we also contribute to Apache Airflow as an open source project and community. So, we cultivate the community of humans, and we also put out open source developer tools that actually make it easier for individual data practitioners to be productive in their day-to-day jobs, whether or not they actually use our product and and pay us money or not. And then of course, we also have professional services and education and all of these things around our commercial products that enable folks to use our products and use Airflow as effectively as possible. So yeah, super, super happy with everything we've done and hopefully that gives you an idea of where we're starting. >> Awesome, so when you're talking with those, Paola, those data engineers, those data scientists, how do you define data orchestration and what does it mean to them? >> Yeah, yeah, it's a good question. So, you know, if you Google data orchestration you're going to get something about an automated process for organizing silo data and making it accessible for processing and analysis. But, to your question, what does that actually mean, you know? So, if you look at it from a customer's perspective, we can share a little bit about how we at Astronomer actually do data orchestration ourselves and the problems that it solves for us. So, as many other companies out in the world do, we at Astronomer need to monitor how our own customers use our products, right? And so, we have a weekly meeting, for example, that goes through a dashboard and a dashboarding tool called Sigma where we see the number of monthly customers and how they're engaging with our product. But, to actually do that, you know, we have to use data from our application database, for example, that has behavioral data on what they're actually doing in our product. We also have data from third party API tools, like Salesforce and HubSpot, and other ways in which our customer, we actually engage with our customers and their behavior. And so, our data team internally at Astronomer uses a bunch of tools to transform and use that data, right? So, we use FiveTran, for example, to ingest. We use Snowflake as our data warehouse. We use other tools for data transformations. And even, if we at Astronomer don't do this, you can imagine a data team also using tools like, Monte Carlo for data quality, or Hightouch for Reverse ETL, or things like that. And, I think the point here is that data teams, you know, that are building data-driven organizations have a plethora of tooling to both ingest the right data and come up with the right interfaces to transform and actually, interact with that data. And so, that movement and sort of synchronization of data across your ecosystem is exactly what data orchestration is responsible for. Historically, I think, and Raj will talk more about this, historically, schedulers like KRON and Oozie or Control-M have taken a role here, but we think that Apache Airflow has sort of risen over the past few years as the defacto industry standard for writing data pipelines that do tasks, that do data jobs that interact with that ecosystem of tools in your organization. And so, beyond that sort of data pipeline unit, I think where we see it is that data acquisition is not only writing those data pipelines that move your data, but it's also all the things around it, right, so, CI/CD tool and Secrets Management, et cetera. So, a long-winded answer here, but I think that's how we talk about it here at Astronomer and how we're building our products. >> Excellent. Great context, Paola. Thank you. Viraj, let's bring you into the conversation. Every company these days has to be a data company, right? They've got to be a software company- >> Mm-hmm. >> whether it's my bank or my grocery store. So, how are companies actually doing data orchestration today, Viraj? >> Yeah, it's a great question. So, I think one thing to think about is like, on one hand, you know, data orchestration is kind of a new category that we're helping define, but on the other hand, it's something that companies have been doing forever, right? You need to get data moving to use it, you know. You've got it all in place, aggregate it, cleaning it, et cetera. So, when you look at what companies out there are doing, right. Sometimes, if you're a more kind of born in the cloud company, as we say, you'll adopt all these cloud native tooling things your cloud provider gives you. If you're a bank or another sort of institution like that, you know, you're probably juggling an even wider variety of tools. You're thinking about a cloud migration. You might have things like Kron running in one place, Uzi running somewhere else, Informatics running somewhere else, while you're also trying to move all your workloads to the cloud. So, there's quite a large spectrum of what the current state is for companies. And then, kind of like Paola was saying, Apache Airflow started in 2014, and it was actually started by Airbnb, and they put out this blog post that was like, "Hey here's how we use Apache Airflow to orchestrate our data across all their sources." And really since then, right, it's almost been a decade since then, Airflow emerged as the open source standard, and there's companies of all sorts using it. And, it's really used to tie all these tools together, especially as that number of tools increases, companies move to hybrid cloud, hybrid multi-cloud strategies, and so on and so forth. But you know, what we found is that if you go to any company, especially a larger one and you say like, "Hey, how are you doing data orchestration?" They'll probably say something like, "Well, I have five data teams, so I have eight different ways I do data orchestration." Right. This idea of data orchestration's been there but the right way to do it, kind of all the abstractions you need, the way your teams need to work together, and so on and so forth, hasn't really emerged just yet, right? It's such a quick moving space that companies have to combine what they were doing before with what their new business initiatives are today. So, you know, what we really believe here at Astronomer is Airflow is the core of how you solve data orchestration for any sort of use case, but it's not everything. You know, it needs a little more. And, that's really where our commercial product, Astro comes in, where we've built, not only the most tried and tested airflow experience out there. We do employ a majority of the Airflow Core Committers, right? So, we're kind of really deep in the project. We've also built the right things around developer tooling, observability, and reliability for customers to really rely on Astro as the heart of the way they do data orchestration, and kind of think of it as the foundational layer that helps tie together all the different tools, practices and teams large companies have to do today. >> That foundational layer is absolutely critical. You've both mentioned open source software. Paola, I want to go back to you, and just give the audience an understanding of how open source really plays into Astronomer's mission as a company, and into the technologies like Astro. >> Mm-hmm. Yeah, absolutely. I mean, we, so we at Astronomers started using Airflow and actually building our products because Airflow is open source and we were our own customers at the beginning of our company journey. And, I think the open source community is at the core of everything we do. You know, without that open source community and culture, I think, you know, we have less of a business, and so, we're super invested in continuing to cultivate and grow that. And, I think there's a couple sort of concrete ways in which we do this that personally make me really excited to do my own job. You know, for one, we do things like we organize meetups and we sponsor the Airflow Summit and there's these sort of baseline community efforts that I think are really important and that reminds you, hey, there just humans trying to do their jobs and learn and use both our technology and things that are out there and contribute to it. So, making it easier to contribute to Airflow, for example, is another one of our efforts. As Viraj mentioned, we also employ, you know, engineers internally who are on our team whose full-time job is to make the open source project better. Again, regardless of whether or not you're a customer of ours or not, we want to make sure that we continue to cultivate the Airflow project in and of itself. And, we're also building developer tooling that might not be a part of the Apache Open Source project, but is still open source. So, we have repositories in our own sort of GitHub organization, for example, with tools that individual data practitioners, again customers are not, can use to make them be more productive in their day-to-day jobs with Airflow writing Dags for the most common use cases out there. The last thing I'll say is how important I think we've found it to build sort of educational resources and documentation and best practices. Airflow can be complex. It's been around for a long time. There's a lot of really, really rich feature sets. And so, how do we enable folks to actually use those? And that comes in, you know, things like webinars, and best practices, and courses and curriculum that are free and accessible and open to the community are just some of the ways in which I think we're continuing to invest in that open source community over the next year and beyond. >> That's awesome. It sounds like open source is really core, not only to the mission, but really to the heart of the organization. Viraj, I want to go back to you and really try to understand how does Astronomer fit into the wider modern data stack and ecosystem? Like what does that look like for customers? >> Yeah, yeah. So, both in the open source and with our commercial customers, right? Folks everywhere are trying to tie together a huge variety of tools in order to start making sense of their data. And you know, I kind of think of it almost like as like a pyramid, right? At the base level, you need things like data reliability, data, sorry, data freshness, data availability, and so on and so forth, right? You just need your data to be there. (coughs) I'm sorry. You just need your data to be there, and you need to make it predictable when it's going to be there. You need to make sure it's kind of correct at the highest level, some quality checks, and so on and so forth. And oftentimes, that kind of takes the case of ELT or ETL use cases, right? Taking data from somewhere and moving it somewhere else, usually into some sort of analytics destination. And, that's really what businesses can do to just power the core parts of getting insights into how their business is going, right? How much revenue did I had? What's in my pipeline, salesforce, and so on and so forth. Once that kind of base foundation is there and people can get the data they need, how they need it, it really opens up a lot for what customers can do. You know, I think one of the trendier things out there right now is MLOps, and how do companies actually put machine learning into production? Well, when you think about it you kind of have to squint at it, right? Like, machine learning pipelines are really just any other data pipeline. They just have a certain set of needs that might not not be applicable to ELT pipelines. And, when you kind of have a common layer to tie together all the ways data can move through your organization, that's really what we're trying to make it so companies can do. And, that happens in financial services where, you know, we have some customers who take app data coming from their mobile apps, and actually run it through their fraud detection services to make sure that all the activity is not fraudulent. We have customers that will run sports betting models on our platform where they'll take data from a bunch of public APIs around different sporting events that are happening, transform all of that in a way their data scientist can build models with it, and then actually bet on sports based on that output. You know, one of my favorite use cases I like to talk about that we saw in the open source is we had there was one company whose their business was to deliver blood transfusions via drone into remote parts of the world. And, it was really cool because they took all this data from all sorts of places, right? Kind of orchestrated all the aggregation and cleaning and analysis that happened had to happen via airflow and the end product would be a drone being shot out into a real remote part of the world to actually give somebody blood who needed it there. Because it turns out for certain parts of the world, the easiest way to deliver blood to them is via drone and not via some other, some other thing. So, these kind of, all the things people do with the modern data stack is absolutely incredible, right? Like you were saying, every company's trying to be a data-driven company. What really energizes me is knowing that like, for all those best, super great tools out there that power a business, we get to be the connective tissue, or the, almost like the electricity that kind of ropes them all together and makes so people can actually do what they need to do. >> Right. Phenomenal use cases that you just described, Raj. I mean, just the variety alone of what you guys are able to do and impact is so cool. So Paola, when you're with those data engineers, those data scientists, and customer conversations, what's your pitch? Why use Astro? >> Mm-hmm. Yeah, yeah, it's a good question. And honestly, to piggyback off of Viraj, there's so many. I think what keeps me so energized is how mission critical both our product and data orchestration is, and those use cases really are incredible and we work with customers of all shapes and sizes. But, to answer your question, right, so why use Astra? Why use our commercial products? There's so many people using open source, why pay for something more than that? So, you know, the baseline for our business really is that Airflow has grown exponentially over the last five years, and like we said has become an industry standard that we're confident there's a huge opportunity for us as a company and as a team. But, we also strongly believe that being great at running Airflow, you know, doesn't make you a successful company at what you do. What makes you a successful company at what you do is building great products and solving problems and solving pin points of your own customers, right? And, that differentiating value isn't being amazing at running Airflow. That should be our job. And so, we want to abstract those customers from meaning to do things like manage Kubernetes infrastructure that you need to run Airflow, and then hiring someone full-time to go do that. Which can be hard, but again doesn't add differentiating value to your team, or to your product, or to your customers. So, folks to get away from managing that infrastructure sort of a base, a base layer. Folks who are looking for differentiating features that make their team more productive and allows them to spend less time tweaking Airflow configurations and more time working with the data that they're getting from their business. For help, getting, staying up with Airflow releases. There's a ton of, we've actually been pretty quick to come out with new Airflow features and releases, and actually just keeping up with that feature set and working strategically with a partner to help you make the most out of those feature sets is a key part of it. And, really it's, especially if you're an organization who currently is committed to using Airflow, you likely have a lot of Airflow environments across your organization. And, being able to see those Airflow environments in a single place and being able to enable your data practitioners to create Airflow environments with a click of a button, and then use, for example, our command line to develop your Airflow Dags locally and push them up to our product, and use all of the sort of testing and monitoring and observability that we have on top of our product is such a key. It sounds so simple, especially if you use Airflow, but really those things are, you know, baseline value props that we have for the customers that continue to be excited to work with us. And of course, I think we can go beyond that and there's, we have ambitions to add whole, a whole bunch of features and expand into different types of personas. >> Right? >> But really our main value prop is for companies who are committed to Airflow and want to abstract themselves and make use of some of the differentiating features that we now have at Astronomer. >> Got it. Awesome. >> Thank you. One thing, one thing I'll add to that, Paola, and I think you did a good job of saying is because every company's trying to be a data company, companies are at different parts of their journey along that, right? And we want to meet customers where they are, and take them through it to where they want to go. So, on one end you have folks who are like, "Hey, we're just building a data team here. We have a new initiative. We heard about Airflow. How do you help us out?" On the farther end, you know, we have some customers that have been using Airflow for five plus years and they're like, "Hey, this is awesome. We have 10 more teams we want to bring on. How can you help with this? How can we do more stuff in the open source with you? How can we tell our story together?" And, it's all about kind of taking this vast community of data users everywhere, seeing where they're at, and saying like, "Hey, Astro and Airflow can take you to the next place that you want to go." >> Which is incredibly- >> Mm-hmm. >> and you bring up a great point, Viraj, that every company is somewhere in a different place on that journey. And it's, and it's complex. But it sounds to me like a lot of what you're doing is really stripping away a lot of the complexity, really enabling folks to use their data as quickly as possible, so that it's relevant and they can serve up, you know, the right products and services to whoever wants what. Really incredibly important. We're almost out of time, but I'd love to get both of your perspectives on what's next for Astronomer. You give us a a great overview of what the company's doing, the value in it for customers. Paola, from your lens as one of the co-founders, what's next? >> Yeah, I mean, I think we'll continue to, I think cultivate in that open source community. I think we'll continue to build products that are open sourced as part of our ecosystem. I also think that we'll continue to build products that actually make Airflow, and getting started with Airflow, more accessible. So, sort of lowering that barrier to entry to our products, whether that's price wise or infrastructure requirement wise. I think making it easier for folks to get started and get their hands on our product is super important for us this year. And really it's about, I think, you know, for us, it's really about focused execution this year and all of the sort of core principles that we've been talking about. And continuing to invest in all of the things around our product that again, enable teams to use Airflow more effectively and efficiently. >> And that efficiency piece is, everybody needs that. Last question, Viraj, for you. What do you see in terms of the next year for Astronomer and for your role? >> Yeah, you know, I think Paola did a really good job of laying it out. So it's, it's really hard to disagree with her on anything, right? I think executing is definitely the most important thing. My own personal bias on that is I think more than ever it's important to really galvanize the community around airflow. So, we're going to be focusing on that a lot. We want to make it easier for our users to get get our product into their hands, be that open source users or commercial users. And last, but certainly not least, is we're also really excited about Data Lineage and this other open source project in our umbrella called Open Lineage to make it so that there's a standard way for users to get lineage out of different systems that they use. When we think about what's in store for data lineage and needing to audit the way automated decisions are being made. You know, I think that's just such an important thing that companies are really just starting with, and I don't think there's a solution that's emerged that kind of ties it all together. So, we think that as we kind of grow the role of Airflow, right, we can also make it so that we're helping solve, we're helping customers solve their lineage problems all in Astro, which is our kind of the best of both worlds for us. >> Awesome. I can definitely feel and hear the enthusiasm and the passion that you both bring to Astronomer, to your customers, to your team. I love it. We could keep talking more and more, so you're going to have to come back. (laughing) Viraj, Paola, thank you so much for joining me today on this showcase conversation. We really appreciate your insights and all the context that you provided about Astronomer. >> Thank you so much for having us. >> My pleasure. For my guests, I'm Lisa Martin. You're watching this Cube conversation. (soft electronic music)

(gentle music) >> Hello, everyone. Welcome to this Cube Conversation. I'm John Furrier, host of theCUBE here, in our Palo Alto Studios. We're featuring OctoML. I'm with the CEO, Luis Ceze. Chief Executive Officer, Co-founder of OctoML. I'm John Furrier of theCUBE. Thanks for joining us today. Luis, great to see you. Last time we spoke was at "re:MARS" Amazon's event. Kind of a joint event between (indistinct) and Amazon, kind of put a lot together. Great to see you. >> Great to see you again, John. I really have good memories of that interview. You know, that was definitely a great time. Great to chat with you again. >> The world of ML and AI, machine learning and AI is really hot. Everyone's talking about it. It's really great to see that advance. So I'm looking forward to this conversation but before we get started, introduce who you are in OctoML. >> Sure. I'm Luis Ceze, Co-founder and CEO at OctoML. I'm also professor of Computer Science at University of Washington. You know, OctoML grew out of our efforts on the Apache CVM project, which is a compiler in runtime system that enables folks to run machine learning models in a broad set of harder in the Edge and in the Cloud very efficiently. You know, we grew that project and grew that community, definitely saw there was something to pain point there. And then we built OctoML, OctoML is about three and a half years old now. And the mission, the company is to enable customers to deploy models very efficiently in the Cloud. And make them, you know, run. Do it quickly, run fast, and run at a low cost, which is something that's especially timely right now. >> I like to point out also for the folks 'casue they should know that you're also a professor in the Computer Science department at University of Washington. A great program there. This is a really an inflection point with AI machine learning. The computer science industry has been waiting for decades to advance AI with all this new cloud computing, all the hardware and silicon advancements, GPUs. This is the perfect storm. And you know, this the computer science now we we're seeing an acceleration. Can you share your view, and you're obviously a professor in that department but also, an entrepreneur. This is a great time for computer science. Explain why. >> Absolutely, yeah, no. Just like the confluence of you know, advances in what, you know, computers can do as devices to computer information. Plus, you know, advances in AI that enable applications that you know, we thought it was highly futuristic and now it's just right there today. You know, AI that can generate photo realistic images from descriptions, you know, can write text that's pretty good. Can help augment, you know, human creativity in a really meaningful way. So you see the confluence of capabilities and the creativity of humankind into new applications is just extremely exciting, both from a researcher point of view as well as an entrepreneur point of view, right. >> What should people know about these large language models we're seeing with ChatGPT and how Google has got a lot of work going on that air. There's been a lot of work recently. What's different now about these models, and why are they so popular and effective now? What's the difference between now, and say five years ago, that makes it more- >> Oh, yeah. It's a huge inflection on their capabilities, I always say like emergent behavior, right? So as these models got more complex and our ability to train and deploy them, you know, got to this point... You know, they really crossed a threshold into doing things that are truly surprising, right? In terms of generating, you know, exhalation for things generating tax, summarizing tax, expending tax. And you know, exhibiting what to some may look like reasoning. They're not quite reasoning fundamentally. They're generating tax that looks like they're reasoning, but they do it so well, that it feels like was done by a human, right. So I would say that the biggest changes that, you know, now, they can actually do things that are extremely useful for business in people's lives today. And that wasn't the case five years ago. So that's in the model capabilities and that is being paired with huge advances in computing that enabled this to be... Enables this to be, you know, actually see line of sites to be deployed at scale, right. And that's where we come in, by the way, but yeah. >> Yeah, I want to get into that. And also, you know, the fusion of data integrating data sets at scales. Another one we're seeing a lot of happening now. It's not just some, you know, siloed, pre-built data modeling. It's a lot of agility and a lot of new integration capabilities of data. How is that impacting the dynamics? >> Yeah, absolutely. So I'll say that the ability to either take the data that has that exists in training a model to do something useful with it, and more interestingly I would say, using baseline foundational models and with a little bit of data, turn them into something that can do a specialized task really, really well. Created this really fast proliferation of really impactful applications, right? >> If every company now is looking at this trend and I'm seeing a lot... And I think every company will rebuild their business with machine learning. If they're not already doing it. And the folks that aren't will probably be dinosaurs will be out of business. This is a real business transformation moment where machine learning and AI, as it goes mainstream. I think it's just the beginning. This is where you guys come in, and you guys are poised for handling this frenzy to change business with machine learning models. How do you guys help customers as they look at this, you know, transition to get, you know, concept to production with machine learning? >> Great. Great questions, yeah, so I would say that it's fair to say there's a bunch of models out there that can do useful things right off the box, right? So and also, the ability to create models improved quite a bit. So the challenge now shifted to customers, you know. Everyone is looking to incorporating AI into their applications. So what we do for them is to, first of all, how do you do that quickly, without needing highly specialized, difficult to find engineering? And very importantly, how do you do that at cost that's accessible, right? So all of these fantastic models that we just talked about, they use an amount of computing that's just astronomical compared to anything else we've done in the past. It means the costs that come with it, are also very, very high. So it's important to enable customers to, you know, incorporate AI into their applications, to their use cases in a way that they can do, with the people that they have, and the costs that they can afford, such that they can have, you know, the maximum impacting possibly have. And finally, you know, helping them deal with hardware availability, as you know, even though we made a lot of progress in making computing cheaper and cheaper. Even to this day, you know, you can never get enough. And getting an allocation, getting the right hardware to run these incredibly hungry models is hard. And we help customers deal with, you know, harder availability as well. >> Yeah, for the folks watching as a... If you search YouTube, there's an interview we did last year at "re:MARS," I mentioned that earlier, just a great interview. You talked about this hardware independence, this traction. I want to get into that, because if you look at all the foundation models that are out there right now, that are getting traction, you're seeing two trends. You're seeing proprietary and open source. And obviously, open source always wins in my opinion, but, you know, there's this iPhone moment and android moment that one of your investors John Torrey from Madrona, talked about was is iPhone versus Android moment, you know, one's proprietary hardware and they're very specialized high performance and then open source. This is an important distinction and you guys are hardware independent. What's the... Explain what all this means. >> Yeah. Great set of questions. First of all, yeah. So, you know, OpenAI, and of course, they create ChatGPT and they offer an API to run these models that does amazing things. But customers have to be able to go and send their data over to OpenAI, right? So, and run the model there and get the outputs. Now, there's open source models that can do amazing things as well, right? So they typically open source models, so they don't lag behind, you know, these proprietary closed models by more than say, you know, six months or so, let's say. And it means that enabling customers to take the models that they want and deploy under their control is something that's very valuable, because one, you don't have to expose your data to externally. Two, you can customize the model even more to the things that you wanted to do. And then three, you can run on an infrastructure that can be much more cost effective than having to, you know, pay somebody else's, you know, cost and markup, right? So, and where we help them is essentially help customers, enable customers to take machine learning models, say an open source model, and automate the process of putting them into production, optimize them to run with the right performance, and more importantly, give them the independence to run where they need to run, where they can run best, right? >> Yeah, and also, you know, I point out all the time that, you know, there's never any stopping the innovation of hardware silicon. You're seeing cloud computing more coming in there. So, you know, being hardware independent has some advantages. And if you look at OpenAI, for instance, you mentioned ChatGPT, I think this is interesting because I think everyone is scratching their head, going, "Okay, I need to move to this new generation." What's your pro tip and advice for folks who want to move to, or businesses that want to say move to machine learning? How do they get started? What are some of the considerations they need to think about to deploy these models into production? >> Yeah, great though. Great set of questions. First of all, I mean, I'm sure they're very aware of the kind of things that you want to do with AI, right? So you could be interacting with customers, you know, automating, interacting with customers. It could be, you know, finding issues in production lines. It could be, you know... Generating, you know, making it easier to produce content and so on. Like, you know, customers, users would have an idea what they want to do. You know, from that it can actually determine, what kind of machine learning models would solve the problem that would, you know, fits that use case. But then, that's when the hard thing begins, right? So when you find a model, identify the model that can do the thing that you wanted to do, you need to turn that into a thing that you can deploy. So how do you go from machine learning model that does a thing that you need to do, to a container with the right executor, the artifact they can actually go and deploy, right? So we've seen customers doing that on their own, right? So, and it's got a bit of work, and that's why we are excited about the automation that we can offer and then turn that into a turnkey problem, right? So a turnkey process. >> Luis, talk about the use cases. If I don't mind going and double down on the previous answer. You got existing services, and then there's new AI applications, AI for applications. What are the use cases with existing stuff, and the new applications that are being built? >> Yeah, I mean, existing itself is, for example, how do you do very smart search and auto completion, you know, when you are editing documents, for example. Very, very smart search of documents, summarization of tax, expanding bullets into pros in a way that, you know, don't have to spend as much human time. Just some of the existing applications, right? So some of the new ones are like truly AI native ways of producing content. Like there's a company that, you know, we share investors and love what they're doing called runwayyML, for example. It's sort of like an AI first way of editing and creating visual content, right? So you could say you have a video, you could say make this video look like, it's night as opposed to dark, or remove that dog in the corner. You can do that in a way that you couldn't do otherwise. So there's like definitely AI native use cases. And yet not only in life sciences, you know, there's quite a bit of advances on AI-based, you know, therapies and diagnostics processes that are designed using automated processes. And this is something that I feel like, we were just scratching the surface there. There's huge opportunities there, right? >> Talk about the inference and AI and production kind of angle here, because cost is a huge concern when you look at... And there's a hardware and that flexibility there. So I can see how that could help, but is there a cost freight train that can get out of control here if you don't deploy properly? Talk about the scale problem around cost in AI. >> Yeah, absolutely. So, you know, very quickly. One thing that people tend to think about is the cost is. You know, training has really high dollar amounts it tends over index on that. But what you have to think about is that for every model that's actually useful, you're going to train it once, and then run it a large number of times in inference. That means that over the lifetime of a model, the vast majority of the compute cycles and the cost are going to go to inference. And that's what we address, right? So, and to give you some idea, if you're talking about using large language model today, you know, you can say it's going to cost a couple of cents per, you know, 2,000 words output. If you have a million users active, you know, a day, you know, if you're lucky and you have that, you can, this cost can actually balloon very quickly to millions of dollars a month, just in inferencing costs. You know, assuming you know, that you actually have access to the infrastructure to run it, right? So means that if you don't pay attention to these inference costs and that's definitely going to be a surprise. And affects the economics of the product where this is embedded in, right? So this is something that, you know, if there's quite a bit of attention being put on right now on how do you do search with large language models and you don't pay attention to the economics, you know, you can have a surprise. You have to change the business model there. >> Yeah. I think that's important to call out, because you don't want it to be a runaway cost structure where you architected it wrong and then next thing you know, you got to unwind that. I mean, it's more than technical debt, it's actually real debt, it's real money. So, talk about some of the dynamics with the customers. How are they architecting this? How do they get ahead of that problem? What do you guys do specifically to solve that? >> Yeah, I mean, well, we help customers. So, it's first of all, be hyper aware, you know, understanding what's going to be the cost for them deploying the models into production and showing them the possibilities of how you can deploy the model with different cost structure, right? So that's where, you know, the ability to have hardware independence is so important because once you have hardware independence, after you optimize models, obviously, you have a new, you know, dimension of freedom to choose, you know, what is the right throughput per dollar for you. And then where, and what are the options? And once you make that decision, you want to automate the process of putting into production. So the way we help customers is showing very clearly in their use case, you know, how they can deploy their models in a much more cost-effective way. You know, when the cases... There's a case study that we put out recently, showing a 4x reduction in deployment costs, right? So this is by doing a mix optimization and choosing the right hardware. >> How do you address the concern that someone might say, Luis said, "Hey, you know, I don't want to degrade performance and latency, and I don't want the user experience to suffer." What's the answer there? >> Two things. So first of all, all of the manipulations that we do in the model is to turn the model to efficient code without changing the behavior of the models. We wouldn't degrade the experience of the user by having the model be wrong more often. And we don't change that at all. The model behaves the way it was validated for. And then the second thing is, you know, user experience with respect to latency, it's all about a maximum... Like, you could say, I want a model to run at 50 milliseconds or less. If it's much faster than 15 seconds, you're not going to notice the difference. But if it's lower, you're going to notice a difference. So the key here is that, how do you find a set of options to deploy, that you are not overshooting performance in a way that's going to lead to costs that has no additional benefits. And this provides a huge, a very significant margin of choices, set of choices that you can optimize for cost without degrading customer experience, right. End user experience. >> Yeah, and I also point out the large language models like the ChatGPTs of the world, they're coming out with Dave Moth and I were talking on this breaking analysis around, this being like, over 10X more computational intensive on capabilities. So this hardware independence is a huge thing. So, and also supply chain, some people can't get servers by the way, so, or hardware these days. >> Or even more interestingly, right? So they do not grow in trees, John. Like GPUs is not kind of stuff that you plant an orchard until you have a bunch and then you can increase it, but no, these things, you know, take a while. So, and you can't increase it overnight. So being able to live with those cycles that are available to you is not just important for all for cost, but also important for people to scale and serve more users at, you know, at whatever pace that they come, right? >> You know, it's really great to talk to you, and congratulations on OctaML. Looking forward to the startup showcase, we'll be featuring you guys there. But I want to get your personal opinion as someone in the industry and also, someone who's been in the computer science area for your career. You know, computer science has always been great, and there's more people enrolling in computer science, more diversity than ever before, but there's also more computer science related fields. How is this opening up computer science and where's AI going with the computers, with the science? Can you share your vision on, you know, the aperture, or the landscape of CompSci, or CS students, and opportunities. >> Yeah, no, absolutely. I think it's fair to say that computer has been embedded in pretty much every aspect of human life these days. Human life these days, right? So for everything. And AI has been a counterpart, it been an integral component of computer science for a while. And this medicines that happened in the last 10, 15 years in AI has shown, you know, new application has I think re-energized how people see what computers can do. And you, you know, there is this picture in our department that shows computer science at the center called the flower picture, and then all the different paddles like life sciences, social sciences, and then, you know, mechanical engineering, all these other things that, and I feel like it can replace that center with computer science. I put AI there as well, you see AI, you know touching all these applications. AI in healthcare, diagnostics. AI in discovery in the sciences, right? So, but then also AI doing things that, you know, the humans wouldn't have to do anymore. They can do better things with their brains, right? So it's permitting every single aspect of human life from intellectual endeavor to day-to-day work, right? >> Yeah. And I think the ChatGPT and OpenAI has really kind of created a mainstream view that everyone sees value in it. Like you could be in the data center, you could be in bio, you could be in healthcare. I mean, every industry sees value. So this brings up what I can call the horizontally scalable use constance. And so this opens up the conversation, what's going to change from this? Because if you go horizontally scalable, which is a cloud concept as you know, that's going to create a lot of opportunities and some shifting of how you think about architecture around data, for instance. What's your opinion on what this will do to change the inflection of the role of architecting platforms and the role of data specifically? >> Yeah, so good question. There is a lot in there, by the way, I should have added the previous question, that you can use AI to do better AI as well, which is what we do, and other folks are doing as well. And so the point I wanted to make here is that it's pretty clear that you have a cloud focus component with a nudge focused counterparts. Like you have AI models, but both in the Cloud and in the Edge, right? So the ability of being able to run your AI model where it runs best also has a data advantage to it from say, from a privacy point of view. That's inherently could say, "Hey, I want to run something, you know, locally, strictly locally, such that I don't expose the data to an infrastructure." And you know that the data never leaves you, right? Never leaves the device. Now you can imagine things that's already starting to happen, like you do some forms of training and model customization in the model architecture itself and the system architecture, such that you do this as close to the user as possible. And there's something called federated learning that has been around for some time now that's finally happening is, how do you get a data from butcher places, you do, you know, some common learning and then you send a model to the Edges, and they get refined for the final use in a way that you get the advantage of aggregating data but you don't get the disadvantage of privacy issues and so on. >> It's super exciting. >> And some of the considerations, yeah. >> It's super exciting area around data infrastructure, data science, computer science. Luis, congratulations on your success at OctaML. You're in the middle of it. And the best thing about its businesses are looking at this and really reinventing themselves and if a business isn't thinking about restructuring their business around AI, they're probably will be out of business. So this is a great time to be in the field. So thank you for sharing your insights here in theCUBE. >> Great. Thank you very much, John. Always a pleasure talking to you. Always have a lot of fun. And we both speak really fast, I can tell, you know, so. (both laughing) >> I know. We'll not the transcript available, we'll integrate it into our CubeGPT model that we have Luis. >> That's right. >> Great. >> Great. >> Great to talk to you, thank you, John. Thanks, man, bye. >> Hey, this is theCUBE. I'm John Furrier, here in Palo Alto, Cube Conversation. Thanks for watching. (gentle music)

>> From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> The world's leading telcos are trying to shed the stigma of being monopolies lacking innovation. Telcos have been great at operational efficiency and connectivity and living off of transmission, and the costs and expenses or revenue associated with that transmission. But in a world beyond telephone poles and basic wireless and mobile services, how will telcos modernize and become more agile and monetize new opportunities brought about by 5G and private wireless and a spate of new innovations and infrastructure, cloud data and apps? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis and ahead of Mobile World Congress or now, MWC23, we explore the evolution of the telco business and how the industry is in many ways, mimicking transformations that took place decades ago in enterprise IT. We'll model some of the traditional enterprise vendors using ETR data and investigate how they're faring in the telecommunications sector, and we'll pose some of the key issues facing the industry this decade. First, let's take a look at what the GSMA has in store for MWC23. GSMA is the host of what used to be called Mobile World Congress. They've set the theme for this year's event as "Velocity" and they've rebranded MWC to reflect the fact that mobile technology is only one part of the story. MWC has become one of the world's premier events highlighting innovations not only in Telco, mobile and 5G, but the collision between cloud, infrastructure, apps, private networks, smart industries, machine intelligence, and AI, and more. MWC comprises an enormous ecosystem of service providers, technology companies, and firms from virtually every industry including sports and entertainment. And as well, GSMA, along with its venue partner at the Fira Barcelona, have placed a major emphasis on sustainability and public and private partnerships. Virtually every industry will be represented at the event because every industry is impacted by the trends and opportunities in this space. GSMA has said it expects 80,000 attendees at MWC this year, not quite back to 2019 levels, but trending in that direction. Of course, attendance from Chinese participants has historically been very high at the show, and obviously the continued travel issues from that region are affecting the overall attendance, but still very strong. And despite these concerns, Huawei, the giant Chinese technology company. has the largest physical presence of any exhibitor at the show. And finally, GSMA estimates that more than $300 million in economic benefit will result from the event which takes place at the end of February and early March. And The Cube will be back at MWC this year with a major presence thanks to our anchor sponsor, Dell Technologies and other supporters of our content program, including Enterprise Web, ArcaOS, VMware, Snowflake, Cisco, AWS, and others. And one of the areas we're interested in exploring is the evolution of the telco stack. It's a topic that's often talked about and one that we've observed taking place in the 1990s when the vertically integrated IBM mainframe monopoly gave way to a disintegrated and horizontal industry structure. And in many ways, the same thing is happening today in telecommunications, which is shown on the left-hand side of this diagram. Historically, telcos have relied on a hardened, integrated, and incredibly reliable, and secure set of hardware and software services that have been fully vetted and tested, and certified, and relied upon for decades. And at the top of that stack on the left are the crown jewels of the telco stack, the operational support systems and the business support systems. For the OSS, we're talking about things like network management, network operations, service delivery, quality of service, fulfillment assurance, and things like that. For the BSS systems, these refer to customer-facing elements of the stack, like revenue, order management, what products they sell, billing, and customer service. And what we're seeing is telcos have been really good at operational efficiency and making money off of transport and connectivity, but they've lacked the innovation in services and applications. They own the pipes and that works well, but others, be the over-the-top content companies, or private network providers and increasingly, cloud providers have been able to bypass the telcos, reach around them, if you will, and drive innovation. And so, the right-most diagram speaks to the need to disaggregate pieces of the stack. And while the similarities to the 1990s in enterprise IT are greater than the differences, there are things that are different. For example, the granularity of hardware infrastructure will not likely be as high where competition occurred back in the 90s at every layer of the value chain with very little infrastructure integration. That of course changed in the 2010s with converged infrastructure and hyper-converged and also software defined. So, that's one difference. And the advent of cloud, containers, microservices, and AI, none of that was really a major factor in the disintegration of legacy IT. And that probably means that disruptors can move even faster than did the likes of Intel and Microsoft, Oracle, Cisco, and the Seagates of the 1990s. As well, while many of the products and services will come from traditional enterprise IT names like Dell, HPE, Cisco, Red Hat, VMware, AWS, Microsoft, Google, et cetera, many of the names are going to be different and come from traditional network equipment providers. These are names like Ericsson and Huawei, and Nokia, and other names, like Wind River, and Rakuten, and Dish Networks. And there are enormous opportunities in data to help telecom companies and their competitors go beyond telemetry data into more advanced analytics and data monetization. There's also going to be an entirely new set of apps based on the workloads and use cases ranging from hospitals, sports arenas, race tracks, shipping ports, you name it. Virtually every vertical will participate in this transformation as the industry evolves its focus toward innovation, agility, and open ecosystems. Now remember, this is not a binary state. There are going to be greenfield companies disrupting the apple cart, but the incumbent telcos are going to have to continue to ensure newer systems work with their legacy infrastructure, in their OSS and BSS existing systems. And as we know, this is not going to be an overnight task. Integration is a difficult thing, transformations, migrations. So that's what makes this all so interesting because others can come in with Greenfield and potentially disrupt. There'll be interesting partnerships and ecosystems will form and coalitions will also form. Now, we mentioned that several traditional enterprise companies are or will be playing in this space. Now, ETR doesn't have a ton of data on specific telecom equipment and software providers, but it does have some interesting data that we cut for this breaking analysis. What we're showing here in this graphic is some of the names that we've followed over the years and how they're faring. Specifically, we did the cut within the telco sector. So the Y-axis here shows net score or spending velocity. And the horizontal axis, that shows the presence or pervasiveness in the data set. And that table insert in the upper left, that informs as to how the dots are plotted. You know, the two columns there, net score and the ends. And that red-dotted line, that horizontal line at 40%, that is an indicator of a highly elevated level. Anything above that, we consider quite outstanding. And what we'll do now is we'll comment on some of the cohorts and share with you how they're doing in telecommunications, and that sector, that vertical relative to their position overall in the data set. Let's start with the public cloud players. They're prominent in every industry. Telcos, telecommunications is no exception and it's quite an interesting cohort here. On the one hand, they can help telecommunication firms modernize and become more agile by eliminating the heavy lifting and you know, all the cloud, you know, value prop, data center costs, and the cloud benefits. At the same time, public cloud players are bringing their services to the edge, building out their own global networks and are a disruptive force to traditional telcos. All right, let's talk about Azure first. Their net score is basically identical to telco relative to its overall average. AWS's net score is higher in telco by just a few percentage points. Google Cloud platform is eight percentage points higher in telco with a 53% net score. So all three hyperscalers have an equal or stronger presence in telco than their average overall. Okay, let's look at the traditional enterprise hardware and software infrastructure cohort. Dell, Cisco, HPE, Red Hat, VMware, and Oracle. We've highlighted in this chart just as sort of indicators or proxies. Dell's net score's 10 percentage points higher in telco than its overall average. Interesting. Cisco's is a bit higher. HPE's is actually lower by about nine percentage points in the ETR survey, and VMware's is lower by about four percentage points. Now, Red Hat is really interesting. OpenStack, as we've previously reported is popular with telcos who want to build out their own private cloud. And the data shows that Red Hat OpenStack's net score is 15 percentage points higher in the telco sector than its overall average. OpenShift, on the other hand, has a net score that's four percentage points lower in telco than its overall average. So this to us talks to the pace of adoption of microservices and containers. You know, it's going to happen, but it's going to happen more slowly. Finally, Oracle's spending momentum is somewhat lower in the sector than its average, despite the firm having a decent telco business. IBM and Accenture, heavy services companies are both lower in this sector than their average. And real quickly, snowflake's net score is much lower by about 12 percentage points relative to its very high average net score of 62%. But we look for them to be a player in this space as telcos need to modernize their analytics stack and share data in a governed manner. Databricks' net score is also much lower than its average by about 13 points. And same, I would expect them to be a player as open architectures and cloud gains steam in telco. All right, let's close out now on what we're going to be talking about at MWC23 and some of the key issues that we'll be unpacking. We've talked about stack disaggregation in this breaking analysis, but the key here will be the pace at which it will reach the operational efficiency and reliability of closed stacks. Telcos, you know, in a large part, they're engineering heavy firms and much of their work takes place, kind of in the basement, in the dark. It's not really a big public hype machine, and they tend to move slowly and cautiously. While they understand the importance of agility, they're going to be careful because, you know, it's in their DNA. And so at the same time, if they don't move fast enough, they're going to get hurt and disrupted by competitors. So that's going to be a topic of conversation, and we'll be looking for proof points. And the other comment I'll make is around integration. Telcos because of their conservatism will benefit from better testing and those firms that can innovate on the testing front and have labs and certifications and innovate at that level, with an ecosystem are going to be in a better position. Because open sometimes means wild west. So the more players like Dell, HPE, Cisco, Red Hat, et cetera, that do that and align with their ecosystems and provide those resources, the faster adoption is going to go. So we'll be looking for, you know, who's actually doing that, Open RAN or Radio Access Networks. That fits in this discussion because O-RAN is an emerging network architecture. It essentially enables the use of open technologies from an ecosystem and over time, look at O-RAN is going to be open, but the questions, you know, a lot of questions remain as to when it will be able to deliver the operational efficiency of traditional RAN. Got some interesting dynamics going on. Rakuten is a company that's working hard on this problem, really focusing on operational efficiency. Then you got Dish Networks. They're also embracing O-RAN. They're coming at it more from service innovation. So that's something that we'll be monitoring and unpacking. We're going to look at cloud as a disruptor. On the one hand, cloud can help drive agility, as we said earlier and optionality, and innovation for incumbent telcos. But the flip side is going to also do the same for startups trying to disrupt and cloud attracts startups. While some of the telcos are actually embracing the cloud, many are being cautious. So that's going to be an interesting topic of discussion. And there's private wireless networks and 5G, and hyperlocal private networks, they're being deployed, you know, at the edge. This idea of open edge is also a really hot topic and this trend is going to accelerate. You know, the importance here is that the use cases are going to be widely varied. The needs of a hospital are going to be different than those of a sports venue are different from a remote drilling location, and energy or a concert venue. Things like real-time AI inference and data flows are going to bring new services and monetization opportunities. And many firms are going to be bypassing traditional telecommunications networks to build these out. Satellites as well, we're going to see, you know, in this decade, you're going to have, you're going to look down at Google Earth and you're going to see real-time. You know, today you see snapshots and so, lots of innovations going in that space. So how is this going to disrupt industries and traditional industry structures? Now, as always, we'll be looking at data angles, right? 'Cause it's in The Cube's DNA to follow the data and what opportunities and risks data brings. The Cube is going to be on location at MWC23 at the end of the month. We got a great set. We're in the walkway between halls four and five, right in Congress Square, it's booths CS60. So we'll have a full, they're called Stan CS60. We have a full schedule. I'm going to be there with Lisa Martin, Dave Nicholson and the entire Cube crew, so don't forget to stop by. All right, that's a wrap. I want to thank Alex Myerson, who's on production and manages the podcast, Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at Silicon Angle, does some great stuff for us. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search "Breaking Analysis" podcasts I publish each week on wikibon.com and silicon angle.com. And all the video content is available on demand at thecube.net. You can email me directly at david.vellante@silicon angle.com. You can DM me at dvellante or comment on my LinkedIn post. Please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for The Cube Insights powered by ETR. Thanks for watching and we'll see you at Mobile World Congress, and/or at next time on "Breaking Analysis." (bright music) (bright music fades)