(gentle music playing) >> For centuries, we've been captivated by the concept of machines doing the job of humans. And over the past decade or so, we've really focused on AI and the possibility of intelligent machines that can perform cognitive tasks. Now in the past few years, with the popularity of machine learning models ranging from recent ChatGPT to Bert, we're starting to see how AI is changing the way we interact with the world. How is AI transforming the way we do business? And what does the future hold for us there. At theCube, we've covered Oracle's AI and ML strategy for years, which has really been used to drive automation into Oracle's autonomous database. We've talked a lot about MySQL HeatWave in database machine learning, and AI pushed into Oracle's business apps. Oracle, it tends to lead in AI, but not competing as a direct AI player per se, but rather embedding AI and machine learning into its portfolio to enhance its existing products, and bring new services and offerings to the market. Now, last October at Cloud World in Las Vegas, Oracle partnered with Nvidia, which is the go-to AI silicon provider for vendors. And they announced an investment, a pretty significant investment to deploy tens of thousands more Nvidia GPUs to OCI, the Oracle Cloud Infrastructure and build out Oracle's infrastructure for enterprise scale AI. Now, Oracle CEO, Safra Catz said something to the effect of this alliance is going to help customers across industries from healthcare, manufacturing, telecoms, and financial services to overcome the multitude of challenges they face. Presumably she was talking about just driving more automation and more productivity. Now, to learn more about Oracle's plans for AI, we'd like to welcome in Elad Ziklik, who's the vice president of AI services at Oracle. Elad, great to see you. Welcome to the show. >> Thank you. Thanks for having me. >> You're very welcome. So first let's talk about Oracle's path to AI. I mean, it's the hottest topic going for years you've been incorporating machine learning into your products and services, you know, could you tell us what you've been working on, how you got here? >> So great question. So as you mentioned, I think most of the original four-way into AI was on embedding AI and using AI to make our applications, and databases better. So inside mySQL HeatWave, inside our autonomous database in power, we've been driving AI, all of course are SaaS apps. So Fusion, our large enterprise business suite for HR applications and CRM and ELP, and whatnot has built in AI inside it. Most recently, NetSuite, our small medium business SaaS suite started using AI for things like automated invoice processing and whatnot. And most recently, over the last, I would say two years, we've started exposing and bringing these capabilities into the broader OCI Oracle Cloud infrastructure. So the developers, and ISVs and customers can start using our AI capabilities to make their apps better and their experiences and business workflow better, and not just consume these as embedded inside Oracle. And this recent partnership that you mentioned with Nvidia is another step in bringing the best AI infrastructure capabilities into this platform so you can actually build any type of machine learning workflow or AI model that you want on Oracle Cloud. >> So when I look at the market, I see companies out there like DataRobot or C3 AI, there's maybe a half dozen that sort of pop up on my radar anyway. And my premise has always been that most customers, they don't want to become AI experts, they want to buy applications and have AI embedded or they want AI to manage their infrastructure. So my question to you is, how does Oracle help its OCI customers support their business with AI? >> So it's a great question. So I think what most customers want is business AI. They want AI that works for the business. They want AI that works for the enterprise. I call it the last mile of AI. And they want this thing to work. The majority of them don't want to hire a large and expensive data science teams to go and build everything from scratch. They just want the business problem solved by applying AI to it. My best analogy is Lego. So if you think of Lego, Lego has these millions Lego blocks that you can use to build anything that you want. But the majority of people like me or like my kids, they want the Lego death style kit or the Lego Eiffel Tower thing. They want a thing that just works, and it's very easy to use. And still Lego blocks, you still need to build some things together, which just works for the scenario that you're looking for. So that's our focus. Our focus is making it easy for customers to apply AI where they need to, in the right business context. So whether it's embedding it inside the business applications, like adding forecasting capabilities to your supply chain management or financial planning software, whether it's adding chat bots into the line of business applications, integrating these things into your analytics dashboard, even all the way to, we have a new platform piece we call ML applications that allows you to take a machine learning model, and scale it for the thousands of tenants that you would be. 'Cause this is a big problem for most of the ML use cases. It's very easy to build something for a proof of concept or a pilot or a demo. But then if you need to take this and then deploy it across your thousands of customers or your thousands of regions or facilities, then it becomes messy. So this is where we spend our time making it easy to take these things into production in the context of your business application or your business use case that you're interested in right now. >> So you mentioned chat bots, and I want to talk about ChatGPT, but my question here is different, we'll talk about that in a minute. So when you think about these chat bots, the ones that are conversational, my experience anyway is they're just meh, they're not that great. But the ones that actually work pretty well, they have a conditioned response. Now they're limited, but they say, which of the following is your problem? And then if that's one of the following is your problem, you can maybe solve your problem. But this is clearly a trend and it helps the line of business. How does Oracle think about these use cases for your customers? >> Yeah, so I think the key here is exactly what you said. It's about task completion. The general purpose bots are interesting, but as you said, like are still limited. They're getting much better, I'm sure we'll talk about ChatGPT. But I think what most enterprises want is around task completion. I want to automate my expense report processing. So today inside Oracle we have a chat bot where I submit my expenses the bot ask a couple of question, I answer them, and then I'm done. Like I don't need to go to our fancy application, and manually submit an expense report. I do this via Slack. And the key is around managing the right expectations of what this thing is capable of doing. Like, I have a story from I think five, six years ago when technology was much inferior than it is today. Well, one of the telco providers I was working with wanted to roll a chat bot that does realtime translation. So it was for a support center for of the call centers. And what they wanted do is, Hey, we have English speaking employees, whatever, 24/7, if somebody's calling, and the native tongue is different like Hebrew in my case, or Chinese or whatnot, then we'll give them a chat bot that they will interact with and will translate this on the fly and everything would work. And when they rolled it out, the feedback from customers was horrendous. Customers said, the technology sucks. It's not good. I hate it, I hate your company, I hate your support. And what they've done is they've changed the narrative. Instead of, you go to a support center, and you assume you're going to talk to a human, and instead you get a crappy chat bot, they're like, Hey, if you want to talk to a Hebrew speaking person, there's a four hour wait, please leave your phone and we'll call you back. Or you can try a new amazing Hebrew speaking AI powered bot and it may help your use case. Do you want to try it out? And some people said, yeah, let's try it out. Plus one to try it out. And the feedback, even though it was the exact same technology was amazing. People were like, oh my God, this is so innovative, this is great. Even though it was the exact same experience that they hated a few weeks earlier on. So I think the key lesson that I picked from this experience is it's all about setting the right expectations, and working around the right use case. If you are replacing a human, the level is different than if you are just helping or augmenting something that otherwise would take a lot of time. And I think this is the focus that we are doing, picking up the tasks that people want to accomplish or that enterprise want to accomplish for the customers, for the employees. And using chat bots to make those specific ones better rather than, hey, this is going to replace all humans everywhere, and just be better than that. >> Yeah, I mean, to the point you mentioned expense reports. I'm in a Twitter thread and one guy says, my favorite part of business travel is filling out expense reports. It's an hour of excitement to figure out which receipts won't scan. We can all relate to that. It's just the worst. When you think about companies that are building custom AI driven apps, what can they do on OCI? What are the best options for them? Do they need to hire an army of machine intelligence experts and AI specialists? Help us understand your point of view there. >> So over the last, I would say the two or three years we've developed a full suite of machine learning and AI services for, I would say probably much every use case that you would expect right now from applying natural language processing to understanding customer support tickets or social media, or whatnot to computer vision platforms or computer vision services that can understand and detect objects, and count objects on shelves or detect cracks in the pipe or defecting parts, all the way to speech services. It can actually transcribe human speech. And most recently we've launched a new document AI service. That can actually look at unstructured documents like receipts or invoices or government IDs or even proprietary documents, loan application, student application forms, patient ingestion and whatnot and completely automate them using AI. So if you want to do one of the things that are, I would say common bread and butter for any industry, whether it's financial services or healthcare or manufacturing, we have a suite of services that any developer can go, and use easily customized with their own data. You don't need to be an expert in deep learning or large language models. You could just use our automobile capabilities, and build your own version of the models. Just go ahead and use them. And if you do have proprietary complex scenarios that you need customer from scratch, we actually have the most cost effective platform for that. So we have the OCI data science as well as built-in machine learning platform inside the databases inside the Oracle database, and mySQL HeatWave that allow data scientists, python welding people that actually like to build and tweak and control and improve, have everything that they need to go and build the machine learning models from scratch, deploy them, monitor and manage them at scale in production environment. And most of it is brand new. So we did not have these technologies four or five years ago and we've started building them and they're now at enterprise scale over the last couple of years. >> So what are some of the state-of-the-art tools, that AI specialists and data scientists need if they're going to go out and develop these new models? >> So I think it's on three layers. I think there's an infrastructure layer where the Nvidia's of the world come into play. For some of these things, you want massively efficient, massively scaled infrastructure place. So we are the most cost effective and performant large scale GPU training environment today. We're going to be first to onboard the new Nvidia H100s. These are the new super powerful GPU's for large language model training. So we have that covered for you in case you need this 'cause you want to build these ginormous things. You need a data science platform, a platform where you can open a Python notebook, and just use all these fancy open source frameworks and create the models that you want, and then click on a button and deploy it. And it infinitely scales wherever you need it. And in many cases you just need the, what I call the applied AI services. You need the Lego sets, the Lego death style, Lego Eiffel Tower. So we have a suite of these sets for typical scenarios, whether it's cognitive services of like, again, understanding images, or documents all the way to solving particular business problems. So an anomaly detection service, demand focusing service that will be the equivalent of these Lego sets. So if this is the business problem that you're looking to solve, we have services out there where we can bring your data, call an API, train a model, get the model and use it in your production environment. So wherever you want to play, all the way into embedding this thing, inside this applications, obviously, wherever you want to play, we have the tools for you to go and engage from infrastructure to SaaS at the top, and everything in the middle. >> So when you think about the data pipeline, and the data life cycle, and the specialized roles that came out of kind of the (indistinct) era if you will. I want to focus on two developers and data scientists. So the developers, they hate dealing with infrastructure and they got to deal with infrastructure. Now they're being asked to secure the infrastructure, they just want to write code. And a data scientist, they're spending all their time trying to figure out, okay, what's the data quality? And they're wrangling data and they don't spend enough time doing what they want to do. So there's been a lack of collaboration. Have you seen that change, are these approaches allowing collaboration between data scientists and developers on a single platform? Can you talk about that a little bit? >> Yeah, that is a great question. One of the biggest set of scars that I have on my back from for building these platforms in other companies is exactly that. Every persona had a set of tools, and these tools didn't talk to each other and the handoff was painful. And most of the machine learning things evaporate or die on the floor because of this problem. It's very rarely that they are unsuccessful because the algorithm wasn't good enough. In most cases it's somebody builds something, and then you can't take it to production, you can't integrate it into your business application. You can't take the data out, train, create an endpoint and integrate it back like it's too painful. So the way we are approaching this is focused on this problem exactly. We have a single set of tools that if you publish a model as a data scientist and developers, and even business analysts that are seeing a inside of business application could be able to consume it. We have a single model store, a single feature store, a single management experience across the various personas that need to play in this. And we spend a lot of time building, and borrowing a word that cellular folks used, and I really liked it, building inside highways to make it easier to bring these insights into where you need them inside applications, both inside our applications, inside our SaaS applications, but also inside custom third party and even first party applications. And this is where a lot of our focus goes to just because we have dealt with so much pain doing this inside our own SaaS that we now have built the tools, and we're making them available for others to make this process of building a machine learning outcome driven insight in your app easier. And it's not just the model development, and it's not just the deployment, it's the entire journey of taking the data, building the model, training it, deploying it, looking at the real data that comes from the app, and creating this feedback loop in a more efficient way. And that's our focus area. Exactly this problem. >> Well thank you for that. So, last week we had our super cloud two event, and I had Juan Loza on and he spent a lot of time talking about how open Oracle is in its philosophy, and I got a lot of feedback. They were like, Oracle open, I don't really think, but the truth is if you think about database Oracle database, it never met a hardware platform that it didn't like. So in that sense it's open. So, but my point is, a big part of of machine learning and AI is driven by open source tools, frameworks, what's your open source strategy? What do you support from an open source standpoint? >> So I'm a strong believer that you don't actually know, nobody knows where the next slip fog or the next industry shifting innovation in AI is going to come from. If you look six months ago, nobody foreseen Dali, the magical text to image generation and the exploding brought into just art and design type of experiences. If you look six weeks ago, I don't think anybody's seen ChatGPT, and what it can do for a whole bunch of industries. So to me, assuming that a customer or partner or developer would want to lock themselves into only the tools that a specific vendor can produce is ridiculous. 'Cause nobody knows, if anybody claims that they know where the innovation is going to come from in a year or two, let alone in five or 10, they're just wrong or lying. So our strategy for Oracle is to, I call this the Netflix of AI. So if you think about Netflix, they produced a bunch of high quality shows on their own. A few years ago it was House of Cards. Last month my wife and I binge watched Ginny and Georgie, but they also curated a lot of shows that they found around the world and bought them to their customers. So it started with things like Seinfeld or Friends and most recently it was Squid games and those are famous Israeli TV series called Founder that Netflix bought in, and they bought it as is and they gave it the Netflix value. So you have captioning and you have the ability to speed the movie and you have it inside your app, and you can download it and watch it offline and everything, but nobody Netflix was involved in the production of these first seasons. Now if these things hunt and they're great, then the third season or the fourth season will get the full Netflix production value, high value budget, high value location shooting or whatever. But you as a customer, you don't care whether the producer and director, and screenplay writing is a Netflix employee or is somebody else's employee. It is fulfilled by Netflix. I believe that we will become, or we are looking to become the Netflix of AI. We are building a bunch of AI in a bunch of places where we think it's important and we have some competitive advantage like healthcare with Acellular partnership or whatnot. But I want to bring the best AI software and hardware to OCI and do a fulfillment by Oracle on that. So you'll get the Oracle security and identity and single bill and everything you'd expect from a company like Oracle. But we don't have to be building the data science, and the models for everything. So this means both open source recently announced a partnership with Anaconda, the leading provider of Python distribution in the data science ecosystem where we are are doing a joint strategic partnership of bringing all the goodness into Oracle customers as well as in the process of doing the same with Nvidia, and all those software libraries, not just the Hubble, both for other stuff like Triton, but also for healthcare specific stuff as well as other ISVs, other AI leading ISVs that we are in the process of partnering with to get their stuff into OCI and into Oracle so that you can truly consume the best AI hardware, and the best AI software in the world on Oracle. 'Cause that is what I believe our customers would want the ability to choose from any open source engine, and honestly from any ISV type of solution that is AI powered and they want to use it in their experiences. >> So you mentioned ChatGPT, I want to talk about some of the innovations that are coming. As an AI expert, you see ChatGPT on the one hand, I'm sure you weren't surprised. On the other hand, maybe the reaction in the market, and the hype is somewhat surprising. You know, they say that we tend to under or over-hype things in the early stages and under hype them long term, you kind of use the internet as example. What's your take on that premise? >> So. I think that this type of technology is going to be an inflection point in how software is being developed. I truly believe this. I think this is an internet style moment, and the way software interfaces, software applications are being developed will dramatically change over the next year two or three because of this type of technologies. I think there will be industries that will be shifted. I think education is a good example. I saw this thing opened on my son's laptop. So I think education is going to be transformed. Design industry like images or whatever, it's already been transformed. But I think that for mass adoption, like beyond the hype, beyond the peak of inflected expectations, if I'm using Gartner terminology, I think certain things need to go and happen. One is this thing needs to become more reliable. So right now it is a complete black box that sometimes produce magic, and sometimes produce just nonsense. And it needs to have better explainability and better lineage to, how did you get to this answer? 'Cause I think enterprises are going to really care about the things that they surface with the customers or use internally. So I think that is one thing that's going to come out. And the other thing that's going to come out is I think it's going to come industry specific large language models or industry specific ChatGPTs. Something like how OpenAI did co-pilot for writing code. I think we will start seeing this type of apps solving for specific business problems, understanding contracts, understanding healthcare, writing doctor's notes on behalf of doctors so they don't have to spend time manually recording and analyzing conversations. And I think that would become the sweet spot of this thing. There will be companies, whether it's OpenAI or Microsoft or Google or hopefully Oracle that will use this type of technology to solve for specific very high value business needs. And I think this will change how interfaces happen. So going back to your expense report, the world of, I'm going to go into an app, and I'm going to click on seven buttons in order to get some job done like this world is gone. Like I'm going to say, hey, please do this and that. And I expect an answer to come out. I've seen a recent demo about, marketing in sales. So a customer sends an email that is interested in something and then a ChatGPT powered thing just produces the answer. I think this is how the world is going to evolve. Like yes, there's a ton of hype, yes, it looks like magic and right now it is magic, but it's not yet productive for most enterprise scenarios. But in the next 6, 12, 24 months, this will start getting more dependable, and it's going to change how these industries are being managed. Like I think it's an internet level revolution. That's my take. >> It's very interesting. And it's going to change the way in which we have. Instead of accessing the data center through APIs, we're going to access it through natural language processing and that opens up technology to a huge audience. Last question, is a two part question. And the first part is what you guys are working on from the futures, but the second part of the question is, we got data scientists and developers in our audience. They love the new shiny toy. So give us a little glimpse of what you're working on in the future, and what would you say to them to persuade them to check out Oracle's AI services? >> Yep. So I think there's two main things that we're doing, one is around healthcare. With a new recent acquisition, we are spending a significant effort around revolutionizing healthcare with AI. Of course many scenarios from patient care using computer vision and cameras through automating, and making better insurance claims to research and pharma. We are making the best models from leading organizations, and internal available for hospitals and researchers, and insurance providers everywhere. And we truly are looking to become the leader in AI for healthcare. So I think that's a huge focus area. And the second part is, again, going back to the enterprise AI angle. Like we want to, if you have a business problem that you want to apply here to solve, we want to be your platform. Like you could use others if you want to build everything complicated and whatnot. We have a platform for that as well. But like, if you want to apply AI to solve a business problem, we want to be your platform. We want to be the, again, the Netflix of AI kind of a thing where we are the place for the greatest AI innovations accessible to any developer, any business analyst, any user, any data scientist on Oracle Cloud. And we're making a significant effort on these two fronts as well as developing a lot of the missing pieces, and building blocks that we see are needed in this space to make truly like a great experience for developers and data scientists. And what would I recommend? Get started, try it out. We actually have a shameless sales plug here. We have a free deal for all of our AI services. So it typically cost you nothing. I would highly recommend to just go, and try these things out. Go play with it. If you are a python welding developer, and you want to try a little bit of auto mail, go down that path. If you're not even there and you're just like, hey, I have these customer feedback things and I want to try out, if I can understand them and apply AI and visualize, and do some cool stuff, we have services for that. My recommendation is, and I think ChatGPT got us 'cause I see people that have nothing to do with AI, and can't even spell AI going and trying it out. I think this is the time. Go play with these things, go play with these technologies and find what AI can do to you or for you. And I think Oracle is a great place to start playing with these things. >> Elad, thank you. Appreciate you sharing your vision of making Oracle the Netflix of AI. Love that and really appreciate your time. >> Awesome. Thank you. Thank you for having me. >> Okay. Thanks for watching this Cube conversation. This is Dave Vellante. We'll see you next time. (gentle music playing)

>> From San Francisco, it's theCube, covering Girls in Tech Catalyst Conference. Brought to you by Girls in Tech. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at Bespoke. It's at the top of the Westfield Mall. Come on by sometime if you're doin' some shoppin', but we're here at Girls in Tech Catalyst. Really cool convention. We were in Phoenix a couple years ago, about 700 women, girls, some students that came in, and a few men as well. Really talking about great inspirational stories. Unbelievable lineup of leaders telling their stories, how they got to where they were, and sharing the information. Now we're excited to have our first guest. She's Patty McCord. She's an author of this great book, Powerful, and she used to be the head of HR, or chief, >> Talent officer. >> Talent officer at Netflix. Patty, great to see you. >> Nice to meet you, too. >> Absolutely. >> Yay. >> So you're going to be speaking in a little bit, so what is your topic on today? >> The future of work, but mostly I'm going to talk about my experience with work over the last 30 years, and that the future of work is right now. We just don't operate that way. >> So, Patty, what do people get right about culture, what do they get wrong about culture? >> What they get wrong about it is they think it's a document. They think if you write it down, you're done, and what they get wrong about it is they don't walk the walk, right? I mean, I know that's simple and cliched, but I can't tell you how many startup CEOs I meet that say, I want to be this, and I want to this. I want to be efficient, right? I want to be straightforward. Well, is there information that you hide from employees, and do you show up to meetings on time? Uuh, right? And it's also about the stories that you tell, what becomes legend, and people love those positive legend stories, and they forget that when you screw up, we'll tell that story for 10 years, right? And the third thing, I think, is people get nostalgic, and they think they can keep it, especially in little companies. Let's keep the culture the way it is, and you cannot do that if you are successful. >> It's so bizarre that people don't get the kind of do what I say, versus do what I do. I mean, this is 101, and it's such a slap in the face when a leader says one thing, and then immediately does something different. >> And you wonder, how did they end up to be this way, when their saying is break things, right? >> Well, failing up is a whole different conversation that we'll save for another day. >> But let's take failing up, right? I talked to a CEO who told me I give everybody who makes a mistake a bottle of champagne to celebrate failure. I'm like, well that's stupid. (both laugh) But it only matters if you fail up if you learn something from it, and that's the demonstrable stories that you tell, that become part of your culture, rather than you get rewarded with champagne if you screw up. >> And then the other thing we were talking about before we turned the camera on are some of these just kind of arcane, old processes that people just put 'em in it, is it-- >> Well, let's start with the fundamentals. I'm going to hire you, and you'll have a career for the rest of your life in our company. Big fat lie, has been for decades, and we still tell it, right? Second one-- >> God, people still tell that story? >> They still do tell that story. >> I've been in Silicon Valley too long. >> Yeah, me too, but I mean, I travel all over the world now, and I hear that story all the time. The second one, which I'll talk about in a minute, is we're family. You're not, right? It's not family, it's work. It's two different things. >> You pay me to come every day. >> That's right, so it's not just the processes that are archaic, like you and I talked about the annual performance review, which is we don't step back like we do with every part of the business and say, why did we do that? What's its purpose? Is it still effective, right? If it is a process to give people feedback so they'll improve their performance, you do it once a year? >> Right, like that's really going to make a big impact. >> Yeah, that's kind of dumb, and then I'm going to figure out how to pay you based on what you did or didn't do last year? And particularly for women, I mean particularly for women, the compensation systems that we have, equal pay, those are issues that they're just writing checks. How hard can this be to figure this stuff out, and when you do it, it's actually not that much money. So I'm advocating, I'm kind of on my bully pulpit here, saying people in my organizations, in HR, we need to fix the stuff that's broken, own the fact that we broke it, and stop talking about it in a language nobody understands. >> Is there some just historical legal compliance issues and stuff that keeps things like the annual performance review going, or is just minutiae? >> There's sort of this inward naval gazing thing we do, where go, well, it's best practices, 'cause everybody does it. I mean, I talk to people who say, well it's a legal requirement. No, it's not, right? We don't even ask. (laughs) If there's a law, I coach people-- >> You haven't seen it yet, and you've had the C title forever. >> I coach people all the time. When your lawyer says you can't do it, you say why, and they say, well it's against the law, and you say can I see the statute? They're written in english, right? It's not Sanskrit, right? So look at it and say, well that's not what I read it to say. Well, if you didn't do it, you'd get sued. What's the most recent lawsuit? What was the settlement? How many people sue for this sort of thing? So there's a-- >> Such a business-minded and analytical approach to these questions, right, as opposed to just accepting the status quo. >> You just said you'd been in Silicon Valley all your life, me too, and I love innovation, and I love being inventive, and I don't want to be on the outside of all this fun. It's like, if somebody, like when I was at Netflix, if we could invent a new way of consuming entertainment, then why couldn't I invent a new way of working? >> Right, interesting. And so I wanted your take on another kind of hot trend right now, especially with the younger kids comin' up, is mission, and mission-driven companies, mission-driven opportunities, and I think, rightly or wrongly, and it's kind of like we're going to have you forever, it's a more and more important reason why people are choosing to go to certain companies, and it's so competitive here, say in the Bay Area specifically, whereas you're an employer, how should they look at this? How real is it? >> Well, first of all, before you blame them, right? Before we blame them, we have to realize that these are the children of burnt-out corporate citizens, right? And they've watched their parents toil, and come home burnt-out and upset and empty after careers in the companies that they belonged in that didn't give 'em back what they promised, right? So I think we all want mission. I don't think it's unusual for them. I think they're just asking for it. When we felt like we had to wait until later in our career to ask for stuff that mattered to us. And the other thing is, I think the companies who are building now are more capable of delivering that. And I don't think that by mission they're talking about world peace. They just want to know, how do I matter, right? And so, for me, that's teaching people how businesses work, and what the complexities are, and what the opportunities and the challenges are, and being part of that, right? So that's what they're asking for. They're not asking for every single day to go home and feel like they've changed the world, for curing, not that curing cancer's a bad thing, I think we should do that too, but we can still feel mission driven by just connecting with our customers and our products and the work that we do every day. >> All right, Patty, well I know you got to go get mic'd up for your big presentation, so thanks for takin' a few minutes of your time. >> Yeah, it was great fun. >> And I look forward to jumping into the book and getting into the day. >> Great, okay, thanks. >> She's Patty, I'm Jeff. We're at Girls in Tech, the Catalyst Conference at Bespoke in San Francisco. Thanks for watching. (laid-back electronic music)

>> From San Francisco, it's theCube, covering Girls in Tech Catalyst Conference. Brought to you by Girls in Tech. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at Bespoke. It's at the top of the Westfield Mall. Come on by sometime if you're doin' some shoppin', but we're here at Girls in Tech Catalyst. Really cool convention. We were in Phoenix a couple years ago, about 700 women, girls, some students that came in, and a few men as well. Really talking about great inspirational stories. Unbelievable lineup of leaders telling their stories, how they got to where they were, and sharing the information. Now we're excited to have our first guest. She's Patty McCord. She's an author of this great book, Powerful, and she used to be the head of HR, or chief, >> Talent officer. >> Talent officer at Netflix. Patty, great to see you. >> Nice to meet you, too. >> Absolutely. >> Yay. >> So you're going to be speaking in a little bit, so what is your topic on today? >> The future of work, but mostly I'm going to talk about my experience with work over the last 30 years, and that the future of work is right now. We just don't operate that way. >> So, Patty, what do people get right about culture, what do they get wrong about culture? >> What they get wrong about it is they think it's a document. They think if you write it down, you're done, and what they get wrong about it is they don't walk the walk, right? I mean, I know that's simple and cliched, but I can't tell you how many startup CEOs I meet that say, I want to be this, and I want to this. I want to be efficient, right? I want to be straightforward. Well, is there information that you hide from employees, and do you show up to meetings on time? Uuh, right? And it's also about the stories that you tell, what becomes legend, and people love those positive legend stories, and they forget that when you screw up, we'll tell that story for 10 years, right? And the third thing, I think, is people get nostalgic, and they think they can keep it, especially in little companies. Let's keep the culture the way it is, and you cannot do that if you are successful. >> It's so bizarre that people don't get the kind of do what I say, versus do what I do. I mean, this is 101, and it's such a slap in the face when a leader says one thing, and then immediately does something different. >> And you wonder, how did they end up to be this way, when their saying is break things, right? >> Well, failing up is a whole different conversation that we'll save for another day. >> But let's take failing up, right? I talked to a CEO who told me I give everybody who makes a mistake a bottle of champagne to celebrate failure. I'm like, well that's stupid. (both laugh) But it only matters if you fail up if you learn something from it, and that's the demonstrable stories that you tell, that become part of your culture, rather than you get rewarded with champagne if you screw up. >> And then the other thing we were talking about before we turned the camera on are some of these just kind of arcane, old processes that people just put 'em in it, is it-- >> Well, let's start with the fundamentals. I'm going to hire you, and you'll have a career for the rest of your life in our company. Big fat lie, has been for decades, and we still tell it, right? Second one-- >> God, people still tell that story? >> They still do tell that story. >> I've been in Silicon Valley too long. >> Yeah, me too, but I mean, I travel all over the world now, and I hear that story all the time. The second one, which I'll talk about in a minute, is we're family. You're not, right? It's not family, it's work. It's two different things. >> You pay me to come every day. >> That's right, so it's not just the processes that are archaic, like you and I talked about the annual performance review, which is we don't step back like we do with every part of the business and say, why did we do that? What's its purpose? Is it still effective, right? If it is a process to give people feedback so they'll improve their performance, you do it once a year? >> Right, like that's really going to make a big impact. >> Yeah, that's kind of dumb, and then I'm going to figure out how to pay you based on what you did or didn't do last year? And particularly for women, I mean particularly for women, the compensation systems that we have, equal pay, those are issues that they're just writing checks. How hard can this be to figure this stuff out, and when you do it, it's actually not that much money. So I'm advocating, I'm kind of on my bully pulpit here, saying people in my organizations, in HR, we need to fix the stuff that's broken, own the fact that we broke it, and stop talking about it in a language nobody understands. >> Is there some just historical legal compliance issues and stuff that keeps things like the annual performance review going, or is just minutiae? >> There's sort of this inward naval gazing thing we do, where go, well, it's best practices, 'cause everybody does it. I mean, I talk to people who say, well it's a legal requirement. No, it's not, right? We don't even ask. (laughs) If there's a law, I coach people-- >> You haven't seen it yet, and you've had the C title forever. >> I coach people all the time. When your lawyer says you can't do it, you say why, and they say, well it's against the law, and you say can I see the statute? They're written in english, right? It's not Sanskrit, right? So look at it and say, well that's not what I read it to say. Well, if you didn't do it, you'd get sued. What's the most recent lawsuit? What was the settlement? How many people sue for this sort of thing? So there's a-- >> Such a business-minded and analytical approach to these questions, right, as opposed to just accepting the status quo. >> You just said you'd been in Silicon Valley all your life, me too, and I love innovation, and I love being inventive, and I don't want to be on the outside of all this fun. It's like, if somebody, like when I was at Netflix, if we could invent a new way of consuming entertainment, then why couldn't I invent a new way of working? >> Right, interesting. And so I wanted your take on another kind of hot trend right now, especially with the younger kids comin' up, is mission, and mission-driven companies, mission-driven opportunities, and I think, rightly or wrongly, and it's kind of like we're going to have you forever, it's a more and more important reason why people are choosing to go to certain companies, and it's so competitive here, say in the Bay Area specifically, whereas you're an employer, how should they look at this? How real is it? >> Well, first of all, before you blame them, right? Before we blame them, we have to realize that these are the children of burnt-out corporate citizens, right? And they've watched their parents toil, and come home burnt-out and upset and empty after careers in the companies that they belonged in that didn't give 'em back what they promised, right? So I think we all want mission. I don't think it's unusual for them. I think they're just asking for it. When we felt like we had to wait until later in our career to ask for stuff that mattered to us. And the other thing is, I think the companies who are building now are more capable of delivering that. And I don't think that by mission they're talking about world peace. They just want to know, how do I matter, right? And so, for me, that's teaching people how businesses work, and what the complexities are, and what the opportunities and the challenges are, and being part of that, right? So that's what they're asking for. They're not asking for every single day to go home and feel like they've changed the world, for curing, not that curing cancer's a bad thing, I think we should do that too, but we can still feel mission driven by just connecting with our customers and our products and the work that we do every day. >> All right, Patty, well I know you got to go get mic'd up for your big presentation, so thanks for takin' a few minutes of your time. >> Yeah, it was great fun. >> And I look forward to jumping into the book and getting into the day. >> Great, okay, thanks. >> She's Patty, I'm Jeff. We're at Girls in Tech, the Catalyst Conference at Bespoke in San Francisco. Thanks for watching. (laid-back electronic music)

>> Narrator: Live from San Francisco, it's theCube, covering Flink Forward, brought to you by Data Artisans. >> Hi, this is George Gilbert. We're back at Flink Forward, the Flink conference sponsored by Data Artisans, the company that commercializes Apache Flink and provides additional application management platforms that make it easy to take stream processing at scale for commercial organizations. We have Steven Wu from Netflix, always a company that is pushing the edge of what's possible, and one of the early Flink users. Steven, welcome. >> Thank you. >> And tell us a little about the use case that was first, you know, applied to Flink. >> Sure, our first-use case is a routing job for Keystone data pipeline. Keystone data pipeline process over three trillion events per day, so we have a thousand routing jobs that we do some simple filter projection, but the Solr routing job is a challenge for us and we recently migrated our routing job to Apache Flink. >> And so is the function of a routing job, is it like an ETL pipeline? >> Not exactly ETL pipeline, but more like it's a data pipeline to deliver data from the producers to the data syncs where people can consume those data like array search, Kafka or higher. >> Oh, so almost like the source and sync with a hub in the middle? >> Yes, that is exactly- >> Okay. >> That's the one with our big use case. And the other thing is our data engineer, they also need some stream processing today to do data analytics, so their job can be stateless or it can be stateful if it's a stateful job it can be as big as a terabyte of base state for a single job. >> So tell me what these stateful jobs, what are some of the things that you use state for? >> So, for example like a session of user activity, like if you have clicked the video on the online URI all those activity, they would need to be sessionalized window, for the windows, sessionalized, yeah those are the states, typical. >> OK, and what sort of calculations might you be doing? And which of the Flink APIs are you using? >> So, right now we're using the data stream API, so a little bit low level, we haven't used the Flink SQL yet but it's in our road map, yeah. >> OK, so what is the data stream, you know, down closer to the metal, what does that give you control over, right now, that is attractive? And will you have as much control with the SQL API? >> OK, yes, so the low level data stream API can give you the full feature set of everything. High level SQL is much easier to use, but obviously you have, the feature set is more limited. Yeah, so that's a trade-off there. >> So, tell me about, for a stateful application, is there sort of scaffolding about managing this distributed cluster that you had to build that you see coming down the pipe from Flink and Data Artisans that might make it easier, either for you or for mainstream customers? >> Sure, I think internal state management, I think that is where Flink really shines compared to other stream processing engine. So they do a lot with work underneath already. I think the main thing we need from Flink for the future, near future is regarding the job recovery performance. But like a state management API is very mature. Flink is, I think it's more mature than most of the other stream processing engines. >> Meaning like Kafka, Spark. So, in the state management, can a business user or business analyst issue a SQL query across the cluster and Flink figures out how to manage the distribution of the query and the filtering and presentation of the results transparently across the cluster? >> I'm not an expert on Flink SQL, but I think yes, essentially Flink SQL will convert to a Flink job which will be using the data stream API, so they will manage the state, yes, but, >> So, when you're using the lower level data stream API, you have to manage the distributed state and sort of retrieving and filtering, but that's something at a higher level abstraction, hopefully that'll be, >> No, I think that in either case, I think the state management is handled by Flint. >> Okay. >> Yeah. >> Distributed. >> All the state management, yes >> Even if it's querying at the data stream level? >> Yeah, but if you query at the SQL level, you won't be able to deal with those state APIs directly. You can still do actual windowing, let's say you have a SQL app doing window with some session by session by idle time that would be transfer for job and Flink will manage those window, manage those session state so you do not need to worry about either way you do not need to worry about state management. Apache Flink take care of it. >> So tell me, some of the other products you might have looked at, is the issue that if they have a clean separation from the storage layer, for large scale state management, you know, as opposed to, in memory, is it that the large scale is almost treated like a second tier and therefore, you almost have a separate set or a restricted set of operations at distributed state level versus at the compute level, would that be a limitation of other streaming processors? >> No, I don't see that. I think that given that stream will have taken a different approach, you find like a Google Cloud data flow, Google Cloud flow, they are thinking about using a big table, for example. But those are external state management. Flint decided to take a the approach of embedded state management inside of Flink. >> And when it's external, what's the trade-off? >> That's good question, I think if external, the latency may be higher, but your throughput might be a little low. Because you're going all the natural. But the benefit of that external state management is now your job becomes stateless. Your job make the recovery much faster for job failure, so either trade-off over there. >> OK. >> Yes. >> OK, got it. Alright, Steven we're going to have to end it on that, but that was most enlightening, and thanks for joining. >> Sure, thank you. >> This is George Gilbert, for Wikibon and theCube, we're again at Flink Forward in San Francisco with Data Artisans, we'll be back after a short break. 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(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> Welcome back to The Cubes coverage of WIDS 2023 the eighth Annual Women in Data Science Conference which is held at Stanford University. I'm your host, Lisa Martin. I'm really excited to be having some great co-hosts today. I've got Hannah Freytag with me, who is a data journalism master student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Hoang joins us, data scientist at Gilead. It's so great to have you, Kelly. >> Hi, thank you for having me today. I'm super excited to be here and share my journey with you guys. >> Let's talk about that journey. You recently got your PhD in information sciences, congratulations. >> Thank you. Yes, I just graduated, I completed my PhD in information sciences from University of Illinois Urbana-Champaign. And right now I moved to Bay Area and started my career as a data scientist at Gilead. >> And you're in better climate. Well, we do get snow here. >> Kelly: That's true. >> We proved that the last... And data science can show us all the climate change that's going on here. >> That's true. That's the topic of the data fund this year, right? To understand the changes in the climate. >> Yeah. Talk a little bit about your background. You were mentioning before we went live that you come from a whole family of STEM students. So you had that kind of in your DNA. >> Well, I consider myself maybe I was a lucky case. I did grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was at a very young age, I already see like datas, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. >> You aspired to be. >> Yes. >> I love that. >> So I consider myself in a lucky place in that way. But also, like during this journey to become a data scientist you need to navigate yourself too, right? Like you have this roots, like this foundation but then you still need to kind of like figure out yourself what is it? Is it really the career that you want to pursue? But I'm happy that I'm end up here today and where I am right now. >> Oh, we're happy to have you. >> Yeah. So you' re with Gilead now after you're completing your PhD. And were you always interested in the intersection of data science and health, or is that something you explored throughout your studies? >> Oh, that's an excellent question. So I did have background in computer science but I only really get into biomedical domain when I did my PhD at school. So my research during my PhD was natural language processing, NLP and machine learning and their applications in biomedical domains. And then when I graduated, I got my first job in Gilead Science. Is super, super close and super relevant to what my research at school. And at Gilead, I am working in the advanced analytics department, and our focus is to bring artificial intelligence and machine learning into supporting clinical decision making. And really the ultimate goal is how to use AI to accelerate the precision medicine. So yes, it's something very like... I'm very lucky to get the first job that which is very close to my research at school. >> That's outstanding. You know, when we talk about AI, we can't not talk about ethics, bias. >> Kelly: Right. >> We know there's (crosstalk) Yes. >> Kelly: In healthcare. >> Exactly. Exactly. Equities in healthcare, equities in so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead to really influence... I mean this, we're talking about something that's influencing life and death situations. >> Kelly: Right. >> How are you using AI in a way that is really maximizing the opportunities that AI can bring and maximizing the value in the data, but helping to dial down some of the challenges that come with AI? >> Yep. So as you may know already with the digitalization of medical records, this is nowaday, we have a tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines, means now the treatments for people can be really tailored to individual patients depending on their own like characteristic or demographic or whatever. And nature language processing and machine learning, and AI in general really play a key role in that innovation, right? Because like there's a vast amount of information of patients and patient journeys or patient treatment is conducted and recorded in text. So that's why our group was established. Actually our department, advanced analytic department in Gilead is pretty new. We established our department last year. >> Oh wow. >> But really our mission is to bring AI into this field because we see the opportunity now. We have a vast amount of data about patient about their treatments, how we can mine these data how we can understand and tailor the treatment to individuals. And give everyone better care. >> I love that you brought up precision medicine. You know, I always think, if I kind of abstract everything, technology, data, connectivity, we have this expectation in our consumer lives. We can get anything we want. Not only can we get anything we want but we expect whoever we're engaging with, whether it's Amazon or Uber or Netflix to know enough about me to get me that precise next step. I don't think about precision medicine but you bring up such a great point. We expect these tailored experiences in our personal lives. Why not expect that in medicine as well? And have a tailored treatment plan based on whatever you have, based on data, your genetics, and being able to use NLP, machine learning and AI to drive that is really exciting. >> Yeah. You recap it very well, but then you also bring up a good point about the challenges to bring AI into this field right? Definitely this is an emerging field, but also very challenging because we talk about human health. We are doing the work that have direct impact to human health. So everything need to be... Whatever model, machine learning model that you are building, developing you need to be precise. It need to be evaluated properly before like using as a product, apply into the real practice. So it's not like recommendation systems for shopping or anything like that. We're talking about our actual health. So yes, it's challenging that way. >> Yeah. With that, you already answered one of the next questions I had because like medical data and health data is very sensitive. And how you at Gilead, you know, try to protect this data to protect like the human beings, you know, who are the data in the end. >> The security aspect is critical. You bring up a great point about sensitive data. We think of healthcare as sensitive data. Or PII if you're doing a bank transaction. We have to be so careful with that. Where is security, data security, in your everyday work practices within data science? Is it... I imagine it's a fundamental piece. >> Yes, for sure. We at Gilead, for sure, in data science organization we have like intensive trainings for employees about data privacy and security, how you use the data. But then also at the same time, when we work directly with dataset, it's not that we have like direct information about patient at like very granular level. Everything is need to be kind of like anonymized at some points to protect patient privacy. So we do have rules, policies to follow to put that in place in our organization. >> Very much needed. So some of the conversations we heard, were you able to hear the keynote this morning? >> Yes. I did. I attended. Like I listened to all of them. >> Isn't it fantastic? >> Yes, yes. Especially hearing these women from different backgrounds, at different level of their professional life, sharing their journeys. It's really inspiring. >> And Hannah, and I've been talking about, a lot of those journeys look like this. >> I know >> You just kind of go... It's very... Yours is linear, but you're kind of the exception. >> Yeah, this is why I consider my case as I was lucky to grow up in STEM environment. But then again, back to my point at the beginning, sometimes you need to navigate yourself too. Like I did mention about, I did my pa... Sorry, my bachelor degree in Vietnam, in STEM and in computer science. And that time, there's only five girls in a class of 100 students. So I was not the smartest person in the room. And I kept my minority in that areas, right? So at some point I asked myself like, "Huh, I don't know. Is this really my careers." It seems that others, like male people or students, they did better than me. But then you kind of like, I always have this passion of datas. So you just like navigate yourself, keep pushing yourself over those journey. And like being where I am right now. >> And look what you've accomplished. >> Thank you. >> Yeah. That's very inspiring. And yeah, you mentioned how you were in the classroom and you were only one of the few women in the room. And what inspired or motivated you to keep going, even though sometimes you were at these points where you're like, "Okay, is this the right thing?" "Is this the right thing for me?" What motivated you to keep going? >> Well, I think personally for me, as a data scientist or for woman working in data science in general, I always try to find a good story from data. Like it's not, when you have a data set, well it's important for you to come up with methodologies, what are you going to do with the dataset? But I think it's even more important to kind of like getting the context of the dataset. Like think about it like what is the story behind this dataset? What is the thing that you can get out of it and what is the meaning behind? How can we use it to help use it in a useful way. To have in some certain use case. So I always have that like curiosity and encouragement in myself. Like every time someone handed me a data set, I always think about that. So it's helped me to like build up this kind of like passion for me. And then yeah. And then become a data scientist. >> So you had that internal drive. I think it's in your DNA as well. When you were one of five. You were 5% women in your computer science undergrad in Vietnam. Yet as Hannah was asking you, you found a lot of motivation from within. You embrace that, which is so key. When we look at some of the statistics, speaking of data, of women in technical roles. We've seen it hover around 25% the last few years, probably five to 10. I was reading some data from anitab.org over the weekend, and it shows that it's now, in 2022, the number of women in technical roles rose slightly, but it rose, 27.6%. So we're seeing the needle move slowly. But one of the challenges that still remains is attrition. Women who are leaving the role. You've got your PhD. You have a 10 month old, you've got more than one child. What would you advise to women who might be at that crossroads of not knowing should I continue my career in climbing the ladder, or do I just go be with my family or do something else? What's your advice to them in terms of staying the path? >> I think it's really down to that you need to follow your passion. Like in any kind of job, not only like in data science right? If you want to be a baker, or you want to be a chef, or you want to be a software engineer. It's really like you need to ask yourself is it something that you're really passionate about? Because if you really passionate about something, regardless how difficult it is, like regardless like you have so many kids to take care of, you have the whole family to take care of. You have this and that. You still can find your time to spend on it. So it's really like let yourself drive your own passion. Drive the way where you leading to. I guess that's my advice. >> Kind of like following your own North Star, right? Is what you're suggesting. >> Yeah. >> What role have mentors played in your career path, to where you are now? Have you had mentors on the way or people who inspired you? >> Well, I did. I certainly met quite a lot of women who inspired me during my journey. But right now, at this moment, one person, particular person that I just popped into my mind is my current manager. She's also data scientist. She's originally from Caribbean and then came to the US, did her PhDs too, and now led a group, all women. So believe it or not, I am in a group of all women working in data science. So she's really like someone inspire me a lot, like someone I look up to in this career. >> I love that. You went from being one of five females in a class of 100, to now having a PhD in information sciences, and being on an all female data science team. That's pretty cool. >> It's great. Yeah, it's great. And then you see how fascinating that, how things shift right? And now today we are here in a conference that all are women in data science. >> Yeah. >> It's extraordinary. >> So this year we're fortunate to have WIDS coincide this year with the actual International Women's Day, March 8th which is so exciting. Which is always around this time of year, but it's great to have it on the day. The theme of this International Women's Day this year is embrace equity. When you think of that theme, and your career path, and what you're doing now, and who inspires you, how can companies like Gilead benefit from embracing equity? What are your thoughts on that as a theme? >> So I feel like I'm very lucky to get my first job at Gilead. Not only because the work that we are doing here very close to my research at school, but also because of the working environment at Gilead. Inclusion actually is one of the five core values of Gilead. >> Nice. >> So by that, we means we try to create and creating a working environment that all of the differences are valued. Like regardless your background, your gender. So at Gilead, we have women at Gilead which is a global network of female employees, that help us to strengthen our inclusion culture, and also to influence our voices into the company cultural company policy and practice. So yeah, I'm very lucky to work in the environment nowadays. >> It's impressive to not only hear that you're on an all female data science team, but what Gilead is doing and the actions they're taking. It's one thing, we've talked about this Hannah, for companies, and regardless of industry, to say we're going to have 50% women in our workforce by 2030, 2035, 2040. It's a whole other ballgame for companies like Gilead to actually be putting pen to paper. To actually be creating a strategy that they're executing on. That's awesome. And it must feel good to be a part of a company who's really adapting its culture to be more inclusive, because there's so much value that comes from inclusivity, thought diversity, that ultimately will help Gilead produce better products and services. >> Yeah. Yes. Yeah. Actually this here is the first year Gilead is a sponsor of the WIDS Conference. And we are so excited to establish this relationship, and looking forward to like having more collaboration with WIDS in the future. >> Excellent. Kelly we've had such a pleasure having you on the program. Thank you for sharing your linear path. You are definitely a unicorn. We appreciate your insights and your advice to those who might be navigating similar situations. Thank you for being on theCUBE today. >> Thank you so much for having me. >> Oh, it was our pleasure. For our guests, and Hannah Freytag this is Lisa Martin from theCUBE. Coming to you from WIDS 2023, the eighth annual conference. Stick around. Our final guest joins us in just a minute.

(gentle upbeat music) >> Hey, friends of theCUBE. It's Lisa Martin live at Stanford University covering the Eighth Annual Women In Data Science. But you've been a Cube fan for a long time. So you know that we've been here since the beginning of WiDS, which is 2015. We always loved to come and cover this event. We learned great things about data science, about women leaders, underrepresented minorities. And this year we have a special component. We've got two grad students from Stanford's Master's program and Data Journalism joining. One of my them is here with me, Hannah Freitag, my co-host. Great to have you. And we are pleased to welcome from Intuit for the first time, Shir Meir Lador Group Manager at Data Science. Shir, it's great to have you. Thank you for joining us. >> Thank you for having me. >> And I was just secrets girl talking with my boss of theCUBE who informed me that you're in great company. Intuit's Chief Technology Officer, Marianna Tessel is an alumni of theCUBE. She was on at our Supercloud event in January. So welcome back into it. >> Thank you very much. We're happy to be with you. >> Tell us a little bit about what you're doing. You're a data science group manager as I mentioned, but also you've had you've done some cool things I want to share with the audience. You're the co-founder of the PyData Tel Aviv Meetups the co-host of the unsupervised podcast about data science in Israel. You give talks, about machine learning, about data science. Tell us a little bit about your background. Were you always interested in STEM studies from the time you were small? >> So I was always interested in mathematics when I was small, I went to this special program for youth going to university. So I did my test in mathematics earlier and studied in university some courses. And that's when I understood I want to do something in that field. And then when I got to go to university, I went to electrical engineering when I found out about algorithms and how interested it is to be able to find solutions to problems, to difficult problems with math. And this is how I found my way into machine learning. >> Very cool. There's so much, we love talking about machine learning and AI on theCUBE. There's so much potential. Of course, we have to have data. One of the things that I love about WiDS and Hannah and I and our co-host Tracy, have been talking about this all day is the impact of data in everyone's life. If you break it down, I was at Mobile World Congress last week, all about connectivity telecom, and of course we have these expectation that we're going to be connected 24/7 from wherever we are in the world and we can do whatever we want. I can do an Uber transaction, I can watch Netflix, I can do a bank transaction. It all is powered by data. And data science is, some of the great applications of it is what it's being applied to. Things like climate change or police violence or health inequities. Talk about some of the data science projects that you're working on at Intuit. I'm an intuit user myself, but talk to me about some of those things. Give the audience really a feel for what you're doing. >> So if you are a Intuit product user, you probably use TurboTax. >> I do >> In the past. So for those who are not familiar, TurboTax help customers submit their taxes. Basically my group is in charge of getting all the information automatically from your documents, the documents that you upload to TurboTax. We extract that information to accelerate your tax submission to make it less work for our customers. So- >> Thank you. >> Yeah, and this is why I'm so proud to be working at this team because our focus is really to help our customers to simplify all the you know, financial heavy lifting with taxes and also with small businesses. We also do a lot of work in extracting information from small business documents like bill, receipts, different bank statements. Yeah, so this is really exciting for me, the opportunity to work to apply data science and machine learning to solution that actually help people. Yeah >> Yeah, in the past years there have been more and more digital products emerging that needs some sort of data security. And how did your team, or has your team developed in the past years with more and more products or companies offering digital services? >> Yeah, so can you clarify the question again? Sorry. >> Yeah, have you seen that you have more customers? Like has your team expanded in the past years with more digital companies starting that need kind of data security? >> Well, definitely. I think, you know, since I joined Intuit, I joined like five and a half years ago back when I was in Tel Aviv. I recently moved to the Bay Area. So when I joined, there were like a dozens of data scientists and machine learning engineers on Intuit. And now there are a few hundreds. So we've definitely grown with the year and there are so many new places we can apply machine learning to help our customers. So this is amazing, so much we can do with machine learning to get more money in the pocket of our customers and make them do less work. >> I like both of those. More money in my pocket and less work. That's awesome. >> Exactly. >> So keep going Intuit. But one of the things that is so cool is just the the abstraction of the complexity that Intuit's doing. I upload documents or it scans my receipts. I was just in Barcelona last week all these receipts and conversion euros to dollars and it takes that complexity away from the end user who doesn't know all that's going on in the background, but you're making people's lives simpler. Unfortunately, we all have to pay taxes, most of us should. And of course we're in tax season right now. And so it's really cool what you're doing with ML and data science to make fundamental processes to people's lives easier and just a little bit less complicated. >> Definitely. And I think that's what's also really amazing about Intuit it, is how it combines human in the loop as well as AI. Because in some of the tax situation it's very complicated maybe to do it yourself. And then there's an option to work with an expert online that goes on a video with you and helps you do your taxes. And the expert's work is also accelerated by AI because we build tools for those experts to do the work more efficiently. >> And that's what it's all about is you know, using data to be more efficient, to be faster, to be smarter, but also to make complicated processes in our daily lives, in our business lives just a little bit easier. One of the things I've been geeking out about recently is ChatGPT. I was using it yesterday. I was telling everyone I was asking it what's hot in data science and I didn't know would it know what hot is and it did, it gave me trends. But one of the things that I was so, and Hannah knows I've been telling this all day, I was so excited to learn over the weekend that the the CTO of OpenAI is a female. I didn't know that. And I thought why are we not putting her on a pedestal? Because people are likening ChatGPT to like the launch of the iPhone. I mean revolutionary. And here we have what I think is exciting for all of us females, whether you're in tech or not, is another role model. Because really ultimately what WiDS is great at doing is showcasing women in technical roles. Because I always say you can't be what you can't see. We need to be able to see more role models, female role role models, underrepresented minorities of course men, because a lot of my sponsors and mentors are men, but we need more women that we can look up to and see ah, she's doing this, why can't I? Talk to me about how you stay the course in data science. What excites you about the potential, the opportunities based on what you've already accomplished what inspires you to continue and be one of those females that we say oh my God, I could be like Shir. >> I think that what inspires me the most is the endless opportunities that we have. I think we haven't even started tapping into everything that we can do with generative AI, for example. There's so much that can be done to further help you know, people make more money and do less work because there's still so much work that we do that we don't need to. You know, this is with Intuit, but also there are so many other use cases like I heard today you know, with the talk about the police. So that was really exciting how you can apply machine learning and data to actually help people, to help people that been through wrongful things. So I was really moved by that. And I'm also really excited about all the medical applications that we can have with data. >> Yeah, yeah. It's true that data science is so diverse in terms of what fields it can cover but it's equally important to have diverse teams and have like equity and inclusion in your teams. Where is Intuit at promoting women, non-binary minorities in your teams to progress data science? >> Yeah, so I have so much to say on this. >> Good. >> But in my work in Tel Aviv, I had the opportunity to start with Intuit women in data science branch in Tel Aviv. So that's why I'm super excited to be here today for that because basically this is the original conference, but as you know, there are branches all over the world and I got the opportunity to lead the Tel Aviv branch with Israel since 2018. And we've been through already this year it's going to be it's next week, it's going to be the sixth conference. And every year our number of submission to make talk in the conference doubled itself. >> Nice. >> We started with 20 submission, then 50, then 100. This year we have over 200 submissions of females to give talk at the conference. >> Ah, that's fantastic. >> And beyond the fact that there's so much traction, I also feel the great impact it has on the community in Israel because one of the reason we started WiDS was that when I was going to conferences I was seeing so little women on stage in all the technical conferences. You know, kind of the reason why I guess you know, Margaret and team started the WiDS conference. So I saw the same thing in Israel and I was always frustrated. I was organizing PyData Meetups as you mentioned and I was always having such a hard time to get female speakers to talk. I was trying to role model, but that's not enough, you know. We need more. So once we started WiDS and people saw you know, so many examples on the stage and also you know females got opportunity to talk in a place for that. Then it also started spreading and you can see more and more female speakers across other conferences, which are not women in data science. So I think just the fact that Intuits started this conference back in Israel and also in Bangalore and also the support Intuit does for WiDS in Stanford here, it shows how much WiDS values are aligned with our values. Yeah, and I think that to chauffeur that I think we have over 35% females in the data science and machine learning engineering roles, which is pretty amazing I think compared to the industry. >> Way above average. Yeah, absolutely. I was just, we've been talking about some of the AnitaB.org stats from 2022 showing that 'cause usually if we look at the industry to you point, over the last, I don't know, probably five, 10 years we're seeing the number of female technologists around like a quarter, 25% or so. 2022 data from AnitaB.org showed that that number is now 27.6%. So it's very slowly- >> It's very slowly increasing. >> Going in the right direction. >> Too slow. >> And that representation of women technologists increase at every level, except intern, which I thought was really interesting. And I wonder is there a covid relation there? >> I don't know. >> What do we need to do to start opening up the the top of the pipeline, the funnel to go downstream to find kids like you when you were younger and always interested in engineering and things like that. But the good news is that the hiring we've seen improvements, but it sounds like Intuit is way ahead of the curve there with 35% women in data science or technical roles. And what's always nice and refreshing that we've talked, Hannah about this too is seeing companies actually put action into initiatives. It's one thing for a company to say we're going to have you know, 50% females in our organization by 2030. It's a whole other ball game to actually create a strategy, execute on it, and share progress. So kudos to Intuit for what it's doing because that is more companies need to adopt that same sort of philosophy. And that's really cultural. >> Yeah. >> At an organization and culture can be hard to change, but it sounds like you guys kind of have it dialed in. >> I think we definitely do. That's why I really like working and Intuit. And I think that a lot of it is with the role modeling, diversity and inclusion, and by having women leaders. When you see a woman in leadership position, as a woman it makes you want to come work at this place. And as an evidence, when I build the team I started in Israel at Intuit, I have over 50% women in my team. >> Nice. >> Yeah, because when you have a woman in the interviewers panel, it's much easier, it's more inclusive. That's why we always try to have at least you know, one woman and also other minorities represented in our interviews panel. Yeah, and I think that in general it's very important as a leader to kind of know your own biases and trying to have defined standard and rubrics in how you evaluate people to avoid for those biases. So all of that inclusiveness and leadership really helps to get more diversity in your teams. >> It's critical. That thought diversity is so critical, especially if we talk about AI and we're almost out of time, I just wanted to bring up, you brought up a great point about the diversity and equity. With respect to data science and AI, we know in AI there's biases in data. We need to have more inclusivity, more representation to help start shifting that so the biases start to be dialed down and I think a conference like WiDS and it sounds like someone like you and what you've already done so far in the work that you're doing having so many females raise their hands to want to do talks at events is a good situation. It's a good scenario and hopefully it will continue to move the needle on the percentage of females in technical roles. So we thank you Shir for your time sharing with us your story, what you're doing, how Intuit and WiDS are working together. It sounds like there's great alignment there and I think we're at the tip of the iceberg with what we can do with data science and inclusion and equity. So we appreciate all of your insights and your time. >> Thank you very much. >> All right. >> I enjoyed very, very much >> Good. We hope, we aim to please. Thank you for our guests and for Hannah Freitag. This is Lisa Martin coming to you live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. Stick around, next guest will be here in just a minute.

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

(ambient music) >> Good morning, everyone. Lisa Martin with theCUBE, live at the eighth Annual Women in Data Science Conference. This is one of my absolute favorite events of the year. We engage with tons of great inspirational speakers, men and women, and what's happening with WiDS is a global movement. I've got two fabulous co-hosts with me today that you're going to be hearing and meeting. Please welcome Tracy Zhang and Hannah Freitag, who are both from the sata journalism program, master's program, at Stanford. So great to have you guys. >> So excited to be here. >> So data journalism's so interesting. Tracy, tell us a little bit about you, what you're interested in, and then Hannah we'll have you do the same thing. >> Yeah >> Yeah, definitely. I definitely think data journalism is very interesting, and in fact, I think, what is data journalism? Is definitely one of the big questions that we ask during the span of one year, which is the length of our program. And yeah, like you said, I'm in this data journalism master program, and I think coming in I just wanted to pivot from my undergrad studies, which is more like a traditional journalism, into data. We're finding stories through data, so that's why I'm also very excited about meeting these speakers for today because they're all, they have different backgrounds, but they all ended up in data science. So I think they'll be very inspirational and I can't wait to talk to them. >> Data in stories, I love that. Hannah, tell us a little bit about you. >> Yeah, so before coming to Stanford, I was a research assistant at Humboldt University in Berlin, so I was in political science research. And I love to work with data sets and data, but I figured that, for me, I don't want this story to end up in a research paper, which is only very limited in terms of the audience. And I figured, okay, data journalism is the perfect way to tell stories and use data to illustrate anecdotes, but to make it comprehensive and accessible for a broader audience. So then I found this program at Stanford and I was like, okay, that's the perfect transition from political science to journalism, and to use data to tell data-driven stories. So I'm excited to be in this program, I'm excited for the conference today and to hear from these amazing women who work in data science. >> You both brought up great points, and we were chatting earlier that there's a lot of diversity in background. >> Tracy: Definitely. >> Not everyone was in STEM as a young kid or studied computer science. Maybe some are engineering, maybe some are are philosophy or economic, it's so interesting. And what I find year after year at WiDS is it brings in so much thought diversity. And that's what being data-driven really demands. It demands that unbiased approach, that diverse, a spectrum of diverse perspectives, and we definitely get that at WiDS. There's about 350 people in person here, but as I mentioned in the opening, hundreds of thousands will engage throughout the year, tens of thousands probably today at local events going on across the globe. And it just underscores the importance of every organization, whether it's a bank or a grocer, has to be data-driven. We have that expectation as consumers in our consumer lives, and even in our business lives, that I'm going to engage with a business, whatever it is, and they're going to know about me, they're going to deliver me a personalized experience that's relevant to me and my history. And all that is powered by data science, which is I think it's fascinating. >> Yeah, and the great way is if you combine data with people. Because after all, large data sets, they oftentimes consist of stories or data that affects people. And to find these stories or advanced research in whatever fields, maybe in the financial business, or in health, as you mentioned, the variety of fields, it's very powerful, powerful tool to use. >> It's a very power, oh, go ahead Tracy. >> No, definitely. I just wanted to build off of that. It's important to put a face on data. So a dataset without a name is just some numbers, but if there's a story, then I think it means something too. And I think Margot was talking about how data science is about knowing or understanding the past, I think that's very interesting. That's a method for us to know who we are. >> Definitely. There's so many opportunities. I wanted to share some of the statistics from AnitaB.org that I was just looking at from 2022. We always talk at events like WiDS, and some of the other women in tech things, theCUBE is very much pro-women in tech, and has been for a very long, since the beginning of theCUBE. But we've seen the numbers of women technologists historically well below 25%, and we see attrition rates are high. And so we often talk about, well, what can we do? And part of that is raising the awareness. And that's one of the great things about WiDS, especially WiDS happening on International Women's Day, today, March 8th, and around event- >> Tracy: A big holiday. >> Exactly. But one of the nice things I was looking at, the AnitaB.org research, is that representation of tech women is on the rise, still below pre-pandemic levels, but it's actually nearly 27% of women in technical roles. And that's an increase, slow increase, but the needle is moving. We're seeing much more gender diversity across a lot of career levels, which is exciting. But some of the challenges remain. I mean, the representation of women technologists is growing, except at the intern level. And I thought that was really poignant. We need to be opening up that pipeline and going younger. And you'll hear a lot of those conversations today about, what are we doing to reach girls in grade school, 10 year olds, 12 year olds, those in high school? How do we help foster them through their undergrad studies- >> And excite them about science and all these fields, for sure. >> What do you think, Hannah, on that note, and I'll ask you the same question, what do you think can be done? The theme of this year's International Women's Day is Embrace Equity. What do you think can be done on that intern problem to help really dial up the volume on getting those younger kids interested, one, earlier, and two, helping them stay interested? >> Yeah. Yeah, that's a great question. I think it's important to start early, as you said, in school. Back in the day when I went to high school, we had this one day per year where we could explore as girls, explore a STEM job and go into the job for one day and see how it's like to work in a, I dunno, in IT or in data science, so that's a great first step. But as you mentioned, it's important to keep girls and women excited about this field and make them actually pursue this path. So I think conferences or networking is very powerful. Also these days with social media and technology, we have more ability and greater ways to connect. And I think we should even empower ourselves even more to pursue this path if we're interested in data science, and not be like, okay, maybe it's not for me, or maybe as a woman I have less chances. So I think it's very important to connect with other women, and this is what WiDS is great about. >> WiDS is so fantastic for that network effect, as you talked about. It's always such, as I was telling you about before we went live, I've covered five or six WiDS for theCUBE, and it's always such a day of positivity, it's a day of of inclusivity, which is exactly what Embrace Equity is really kind of about. Tracy, talk a little bit about some of the things that you see that will help with that hashtag Embrace Equity kind of pulling it, not just to tech. Because we're talking and we saw Meta was a keynote who's going to come to talk with Hannah and me in a little bit, we see Total Energies on the program today, we see Microsoft, Intuit, Boeing Air Company. What are some of the things you think that can be done to help inspire, say, little Tracy back in the day to become interested in STEM or in technology or in data? What do you think companies can and should be doing to dial up the volume for those youngsters? >> Yeah, 'cause I think somebody was talking about, one of the keynote speakers was talking about how there is a notion that girls just can't be data scientists. girls just can't do science. And I think representation definitely matters. If three year old me see on TV that all the scientists are women, I think I would definitely have the notion that, oh, this might be a career choice for me and I can definitely also be a scientist if I want. So yeah, I think representation definitely matters and that's why conference like this will just show us how these women are great in their fields. They're great data scientists that are bringing great insight to the company and even to the social good as well. So yeah, I think that's very important just to make women feel seen in this data science field and to listen to the great woman who's doing amazing work. >> Absolutely. There's a saying, you can't be what you can't see. >> Exactly. >> And I like to say, I like to flip it on its head, 'cause we can talk about some of the negatives, but there's a lot of positives and I want to share some of those in a minute, is that we need to be, that visibility that you talked about, the awareness that you talked about, it needs to be there but it needs to be sustained and maintained. And an organization like WiDS and some of the other women in tech events that happen around the valley here and globally, are all aimed at raising the profile of these women so that the younger, really, all generations can see what they can be. We all, the funny thing is, we all have this expectation whether we're transacting on Uber ride or we are on Netflix or we're buying something on Amazon, we can get it like that. They're going to know who I am, they're going to know what I want, they're going to want to know what I just bought or what I just watched. Don't serve me up something that I've already done that. >> Hannah: Yeah. >> Tracy: Yeah. >> So that expectation that everyone has is all about data, though we don't necessarily think about it like that. >> Hannah: Exactly. >> Tracy: Exactly. >> But it's all about the data that, the past data, the data science, as well as the realtime data because we want to have these experiences that are fresh, in the moment, and super relevant. So whether women recognize it or not, they're data driven too. Whether or not you're in data science, we're all driven by data and we have these expectations that every business is going to meet it. >> Exactly. >> Yeah. And circling back to young women, I think it's crucial and important to have role models. As you said, if you see someone and you're younger and you're like, oh I want to be like her. I want to follow this path, and have inspiration and a role model, someone you look up to and be like, okay, this is possible if I study the math part or do the physics, and you kind of have a goal and a vision in mind, I think that's really important to drive you. >> Having those mentors and sponsors, something that's interesting is, I always, everyone knows what a mentor is, somebody that you look up to, that can guide you, that you admire. I didn't learn what a sponsor was until a Women in Tech event a few years ago that we did on theCUBE. And I was kind of, my eyes were open but I didn't understand the difference between a mentor and a sponsor. And then it got me thinking, who are my sponsors? And I started going through LinkedIn, oh, he's a sponsor, she's a sponsor, people that help really propel you forward, your recommenders, your champions, and it's so important at every level to build that network. And we have, to your point, Hannah, there's so much potential here for data drivenness across the globe, and there's so much potential for women. One of the things I also learned recently , and I wanted to share this with you 'cause I'm not sure if you know this, ChatGPT, exploding, I was on it yesterday looking at- >> Everyone talking about it. >> What's hot in data science? And it was kind of like, and I actually asked it, what was hot in data science in 2023? And it told me that it didn't know anything prior to 2021. >> Tracy: Yes. >> Hannah: Yeah. >> So I said, Oh, I'm so sorry. But everyone's talking about ChatGPT, it is the most advanced AI chatbot ever released to the masses, it's on fire. They're likening it to the launch of the iPhone, 100 million-plus users. But did you know that the CTO of ChatGPT is a woman? >> Tracy: I did not know, but I learned that. >> I learned that a couple days ago, Mira Murati, and of course- >> I love it. >> She's been, I saw this great profile piece on her on Fast Company, but of course everything that we're hearing about with respect to ChatGPT, a lot on the CEO. But I thought we need to help dial up the profile of the CTO because she's only 35, yet she is at the helm of one of the most groundbreaking things in our lifetime we'll probably ever see. Isn't that cool? >> That is, yeah, I completely had no idea. >> I didn't either. I saw it on LinkedIn over the weekend and I thought, I have to talk about that because it's so important when we talk about some of the trends, other trends from AnitaB.org, I talked about some of those positive trends. Overall hiring has rebounded in '22 compared to pre-pandemic levels. And we see also 51% more women being hired in '22 than '21. So the data, it's all about data, is showing us things are progressing quite slowly. But one of the biggest challenges that's still persistent is attrition. So we were talking about, Hannah, what would your advice be? How would you help a woman stay in tech? We saw that attrition last year in '22 according to AnitaB.org, more than doubled. So we're seeing women getting into the field and dropping out for various reasons. And so that's still an extent concern that we have. What do you think would motivate you to stick around if you were in a technical role? Same question for you in a minute. >> Right, you were talking about how we see an increase especially in the intern level for women. And I think if, I don't know, this is a great for a start point for pushing the momentum to start growth, pushing the needle rightwards. But I think if we can see more increase in the upper level, the women representation in the upper level too, maybe that's definitely a big goal and something we should work towards to. >> Lisa: Absolutely. >> But if there's more representation up in the CTO position, like in the managing level, I think that will definitely be a great factor to keep women in data science. >> I was looking at some trends, sorry, Hannah, forgetting what this source was, so forgive me, that was showing that there was a trend in the last few years, I think it was Fast Company, of more women in executive positions, specifically chief operating officer positions. What that hasn't translated to, what they thought it might translate to, is more women going from COO to CEO and we're not seeing that. We think of, if you ask, name a female executive that you'd recognize, everyone would probably say Sheryl Sandberg. But I was shocked to learn the other day at a Women in Tech event I was doing, that there was a survey done by this organization that showed that 78% of people couldn't identify. So to your point, we need more of them in that visible role, in the executive suite. >> Tracy: Exactly. >> And there's data that show that companies that have women, companies across industries that have women in leadership positions, executive positions I should say, are actually more profitable. So it's kind of like, duh, the data is there, it's telling you this. >> Hannah: Exactly. >> Right? >> And I think also a very important point is work culture and the work environment. And as a woman, maybe if you enter and you work two or three years, and then you have to oftentimes choose, okay, do I want family or do I want my job? And I think that's one of the major tasks that companies face to make it possible for women to combine being a mother and being a great data scientist or an executive or CEO. And I think there's still a lot to be done in this regard to make it possible for women to not have to choose for one thing or the other. And I think that's also a reason why we might see more women at the entry level, but not long-term. Because they are punished if they take a couple years off if they want to have kids. >> I think that's a question we need to ask to men too. >> Absolutely. >> How to balance work and life. 'Cause we never ask that. We just ask the woman. >> No, they just get it done, probably because there's a woman on the other end whose making it happen. >> Exactly. So yeah, another thing to think about, another thing to work towards too. >> Yeah, it's a good point you're raising that we have this conversation together and not exclusively only women, but we all have to come together and talk about how we can design companies in a way that it works for everyone. >> Yeah, and no slight to men at all. A lot of my mentors and sponsors are men. They're just people that I greatly admire who saw raw potential in me 15, 18 years ago, and just added a little water to this little weed and it started to grow. In fact, theCUBE- >> Tracy: And look at you now. >> Look at me now. And theCUBE, the guys Dave Vellante and John Furrier are two of those people that are sponsors of mine. But it needs to be diverse. It needs to be diverse and gender, it needs to include non-binary people, anybody, shouldn't matter. We should be able to collectively work together to solve big problems. Like the propaganda problem that was being discussed in the keynote this morning with respect to China, or climate change. Climate change is a huge challenge. Here, we are in California, we're getting an atmospheric river tomorrow. And Californians and rain, we're not so friendly. But we know that there's massive changes going on in the climate. Data science can help really unlock a lot of the challenges and solve some of the problems and help us understand better. So there's so much real-world implication potential that being data-driven can really lead to. And I love the fact that you guys are studying data journalism. You'll have to help me understand that even more. But we're going to going to have great conversations today, I'm so excited to be co-hosting with both of you. You're going to be inspired, you're going to learn, they're going to learn from us as well. So let's just kind of think of this as a community of men, women, everything in between to really help inspire the current generations, the future generations. And to your point, let's help women feel confident to be able to stay and raise their hand for fast-tracking their careers. >> Exactly. >> What are you guys, last minute, what are you looking forward to most for today? >> Just meeting these great women, I can't wait. >> Yeah, learning from each other. Having this conversation about how we can make data science even more equitable and hear from the great ideas that all these women have. >> Excellent, girls, we're going to have a great day. We're so glad that you're here with us on theCUBE, live at Stanford University, Women in Data Science, the eighth annual conference. I'm Lisa Martin, my two co-hosts for the day, Tracy Zhang, Hannah Freitag, you're going to be seeing a lot of us, we appreciate. Stick around, our first guest joins Hannah and me in just a minute. (ambient music)

(inspirational music) >> theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (uplifting electronic music) (crowd chattering in background) >> Hi everybody. We're back at the Fira in Barcelona. Winding up our four day wall-to-wall coverage of MWC23 theCUBE has been thrilled to cover the telco transformation. Dave Vellante with Dave Nicholson. Really excited to have NTT on. Shahid Ahmed is the Group EVP of New Ventures and Innovation at NTT in from Chicago. Welcome to Barcelona. Welcome to theCUBE. >> Thank you for having me over. >> So, really interesting title. You have, you know, people might not know NTT you know, huge Japan telco but a lot of other businesses, explain your business. >> So we do a lot of things. Most of us are known for our Docomo business in Japan. We have one of the largest wireless cellular carriers in the world. We serve most of Japan. Outside of Japan, we are B2B systems, integration, professional services company. So we offer managed services. We have data centers, we have undersea cables. We offer all kinds of outsourcing services. So we're a big company. >> So there's a narrative out there that says, you know, 5G, it's a lot of hype, not a lot of adoption. Nobody's ever going to make money at 5G. You have a different point of view, I understand. You're like leaning into 5G and you've actually got some traction there. Explain that. >> So 5G can be viewed from two lenses. One is just you and I using our cell phones and we get 5G coverage over it. And the other one is for businesses to use 5G, and we call that private 5G or enterprise grade 5G. Two very separate distinct things, but it is 5G in the end. Now the big debate here in Europe and US is how to monetize 5G. As a consumer, you and I are not going to pay extra for 5G. I mean, I haven't. I just expect the carrier to offer faster, cheaper services. And so would I pay extra? Not really. I just want a reliable network from my carrier. >> Paid up for the good camera though, didn't you? >> I did. (Dave and Dave laughing) >> I'm waiting for four cameras now. >> So the carriers are in this little bit of a pickle at the moment because they've just spent billions of dollars, not only on spectrum but the infrastructure needed to upgrade to 5G, yet nobody's willing to pay extra for that 5G service. >> Oh, right. >> So what do they do? And one idea is to look at enterprises, companies, industrial companies, manufacturing companies who want to build their own 5G networks to support their own use cases. And these use cases could be anything from automating the surveyor belt to cameras with 5G in it to AGVs. These are little carts running around warehouses picking up products and goods, but they have to be connected all the time. Wifi doesn't work all the time there. And so those businesses are willing to pay for 5G. So your question is, is there a business case for 5G? Yes. I don't think it's in the consumer side. I think it's in the business side. And that's where NTT is finding success. >> So you said, you know, how they going to make money, right? You very well described the telco dilemma. We heard earlier this week, you know, well, we could tax the OTT vendors, like Netflix of course shot back and said, "Well, we spent a lot of money on content. We're driving a lot of value. Why don't you help us pay for the content development?" Which is incredibly expensive. I think I heard we're going to tax the developers for API calls on the network. I'm not sure how well that's going to work out. Look at Twitter, you know, we'll see. And then yeah, there's the B2B piece. What's your take on, we heard the Orange CEO say, "We need help." You know, maybe implying we're going to tax the OTT vendors, but we're for net neutrality, which seems like it's completely counter-posed. What's your take on, you know, fair share in the network? >> Look, we've seen this debate unfold in the US for the last 10 years. >> Yeah. >> Tom Wheeler, the FCC chairman started that debate and he made great progress and open internet and net neutrality. The thing is that if you create a lane, a tollway, where some companies have to pay toll and others don't have to, you create an environment where the innovation could be stifled. Content providers may not appear on the scene anymore. And with everything happening around AI, we may see that backfire. So creating a toll for rich companies to be able to pay that toll and get on a faster speed internet, that may work some places may backfire in others. >> It's, you know, you're bringing up a great point. It's one of those sort of unintended consequences. You got to be be careful because the little guy gets crushed in that environment, and then what? Right? Then you stifle innovation. So, okay, so you're a fan of net neutrality. You think the balance that the US model, for a change, maybe the US got it right instead of like GDPR, who sort of informed the US on privacy, maybe the opposite on net neutrality. >> I think so. I mean, look, the way the US, particularly the FCC and the FTC has mandated these rules and regulation. I think it's a nice balance. FTC is all looking at big tech at the moment, but- >> Lena Khan wants to break up big tech. I mean for, you know, you big tech, boom, break 'em up, right? So, but that's, you know- >> That's a whole different story. >> Yeah. Right. We could talk about that too, if you want. >> Right. But I think that we have a balanced approach, a measured approach. Asking the content providers or the developers to pay for your innovative creative application that's on your phone, you know, that's asking for too much in my opinion. >> You know, I think you're right though. Government did do a good job with net neutrality in the US and, I mean, I'm just going to go my high horse for a second, so forgive me. >> Go for it. >> Market forces have always done a better job at adjudicating, you know, competition. Now, if a company's a monopoly, in my view they should be, you know, regulated, or at least penalized. Yeah, but generally speaking, you know the attack on big tech, I think is perhaps misplaced. I sat through, and the reason it's relevant to Mobile World Congress or MWC, is I sat through a Nokia presentation this week and they were talking about Bell Labs when United States broke up, you know, the US telcos, >> Yeah. >> Bell Labs was a gem in the US and now it's owned by Nokia. >> Yeah. >> Right? And so you got to be careful about, you know what you wish for with breaking up big tech. You got AI, you've got, you know, competition with China- >> Yeah, but the upside to breaking up Ma Bell was not just the baby Bells and maybe the stranded orphan asset of Bell Labs, but I would argue it led to innovation. I'm old enough to remember- >> I would say it made the US less competitive. >> I know. >> You were in junior high school, but I remember as an adult, having a rotary dial phone and having to pay for that access, and there was no such- >> Yeah, but they all came back together. The baby Bells are all, they got all acquired. And the cable company, it was no different. So I don't know, do you have a perspective of this? Because you know this better than I do. >> Well, I think look at Nokia, just they announced a whole new branding strategy and new brand. >> I like the brand. >> Yeah. And- >> It looks cool. >> But guess what? It's B2B oriented. >> (laughs) Yeah. >> It's no longer consumer, >> Right, yeah. >> because they felt that Nokia brand phone was sort of misleading towards a lot of business to business work that they do. And so they've oriented themselves to B2B. Look, my point is, the carriers and the service providers, network operators, and look, I'm a network operator, too, in Japan. We need to innovate ourselves. Nobody's stopping us from coming up with a content strategy. Nobody's stopping a carrier from building a interesting, new, over-the-top app. In fact, we have better control over that because we are closer to the customer. We need to innovate, we need to be more creative. I don't think taxing the little developer that's building a very innovative application is going to help in the long run. >> NTT Japan, what do they have a content play? I, sorry, I'm not familiar with it. Are they strong in content, or competitive like Netflix-like, or? >> We have relationships with them, and you remember i-mode? >> Yeah. Oh yeah, sure. >> Remember in the old days. I mean, that was a big hit. >> Yeah, yeah, you're right. >> Right? I mean, that was actually the original app marketplace. >> Right. >> And the application store. So, of course we've evolved from that and we should, and this is an evolution and we should look at it more positively instead of looking at ways to regulate it. We should let it prosper and let it see where- >> But why do you think that telcos generally have failed at content? I mean, AT&T is sort of the exception that proves the rule. I mean, they got some great properties, obviously, CNN and HBO, but generally it's viewed as a challenging asset and others have had to diversify or, you know, sell the assets. Why do you think that telcos have had such trouble there? >> Well, Comcast owns also a lot of content. >> Yeah. Yeah, absolutely. >> And I think, I think that is definitely a strategy that should be explored here in Europe. And I think that has been underexplored. I, in my opinion, I believe that every large carrier must have some sort of content strategy at some point, or else you are a pipe. >> Yeah. You lose touch with a customer. >> Yeah. And by the way, being a dump pipe is okay. >> No, it's a lucrative business. >> It's a good business. You just have to focus. And if you start to do a lot of ancillary things around it then you start to see the margins erode. But if you just focus on being a pipe, I think that's a very good business and it's very lucrative. Everybody wants bandwidth. There's insatiable demand for bandwidth all the time. >> Enjoy the monopoly, I say. >> Yeah, well, capital is like an organism in and of itself. It's going to seek a place where it can insert itself and grow. Do you think that the questions around fair share right now are having people wait in the wings to see what's going to happen? Because especially if I'm on the small end of creating content, creating services, and there's possibly a death blow to my fixed costs that could be coming down the line, I'm going to hold back and wait. Do you think that the answer is let's solve this sooner than later? What are your thoughts? >> I think in Europe the opinion has been always to go after the big tech. I mean, we've seen a lot of moves either through antitrust, or other means. >> Or the guillotine! >> That's right. (all chuckle) A guillotine. Yes. And I've heard those directly. I think, look, in the end, EU has to decide what's right for their constituents, the countries they operate, and the economy. Frankly, with where the economy is, you got recession, inflation pressures, a war, and who knows what else might come down the pipe. I would be very careful in messing with this equilibrium in this economy. Until at least we have gone through this inflation and recessionary pressure and see what happens. >> I, again, I think I come back to markets, ultimately, will adjudicate. I think what we're seeing with chatGPT is like a Netscape moment in some ways. And I can't predict what's going to happen, but I can predict that it's going to change the world. And there's going to be new disruptors that come about. That just, I don't think Amazon, Google, Facebook, Apple are going to rule the world forever. They're just, I guarantee they're not, you know. They'll make it through. But there's going to be some new companies. I think it might be open AI, might not be. Give us a plug for NTT at the show. What do you guys got going here? Really appreciate you coming on. >> Thank you. So, you know, we're showing off our private 5G network for enterprises, for businesses. We see this as a huge opportunities. If you look around here you've got Rohde & Schwarz, that's the industrial company. You got Airbus here. All the big industrial companies are here. Automotive companies and private 5G. 5G inside a factory, inside a hospital, a warehouse, a mining operation. That's where the dollars are. >> Is it a meaningful business for you today? >> It is. We just started this business only a couple of years ago. We're seeing amazing growth and I think there's a lot of good opportunities there. >> Shahid Ahmed, thanks so much for coming to theCUBE. It was great to have you. Really a pleasure. >> Thanks for having me over. Great questions. >> Oh, you're welcome. All right. For David Nicholson, Dave Vellante. We'll be back, right after this short break, from the Fira in Barcelona, MWC23. You're watching theCUBE. (uplifting electronic music)

(gentle upbeat music) >> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (bright upbeat music) >> We're back in Barcelona at the Fira. My name is Dave Vellante. I'm here with David Nicholson. We're live at MWC23, day four of the coverage. The show is still rocking. You walk the floor, it's jamming. People are lined up to get in the copter, in the right. It's amazing. Planes, trains, automobiles, digitization of analog businesses. We're going to talk private wireless here with Dell. Sarvesh Sharma, the Global Director for Edge and Private Mobility Solutions practice at Dell. And John McCready is a Senior Director for 5G Solutions and product management at Dell Technologies. Guys, good to see you. >> Likewise, likewise. >> Good to see you too. >> Private wireless. It's the buzz of the show. Everybody's talking about it. What's Dell's point of view on that? >> So Dell is, obviously, interested entering the private wireless game, as it's a good part of the overall enterprise IT space. As you move more and more into the different things. What we announced here, is sort of our initial partnerships with some key players like Airspan and expedo and AlphaNet. Players that are important in the space. Dell's going to provide an overall system integration solution wrap along with our Edge BU as well. And we think that we can bring really good solutions to our enterprise customers. >> Okay, I got to ask you about AlphaNet. So HPE pulled a little judo move they waited till you announced your partnership and then they bought the company. What, you know, what's your opinion on that? You going to, you going to dump AlphaNet, you're going to keep 'em? >> No. >> We're open Ecosystem. >> Yeah, it's an open ecosystem. We announce these are our initial partners, you know we're going to announce additional partners that was always the case. You know, there's a lot of good players in this space that bring different pros and cons. We got to be able to match the solution requirements of all our customers. And so we'll continue to partner with them and with others. >> Good, good answer, I like that. So some of these solutions are sort of out of the box, others require more integration. Can you talk about your, the spectrum of your portfolio? >> So I'm glad you brought up the integration part, right? I mean, if you look at private wireless, private mobility it is not a sell by itself. At the end of the day what the enterprise wants is not just private mobility. They're looking for an outcome. Which means from an integration perspective, you need somebody who can integrate the infrastructure stack. But that's not enough. You need somebody who can bring in the application stack to play and integrate that application stack with the enterprises IT OT. And that's not enough. You need somebody to put those together. And Dell is ideally suited to do all of this, right? We have strong partners that can bring the infrastructure stack to play. We have a proven track record of managing the IT and the enterprise stack. So we are very excited to say, "Hey, this is the sweet spot for us. And if there was a right to win the edge, we have it." >> Can you explain, I mean, people might be saying, well, why do I even need private wireless? I got Wi-Fi. I know it's kind of a dumb question for people who are in the business, but explain to folks in the audience who may not understand the intersection of the two. >> So, yeah, so I think, you know, wireless is a great techno- pardon me, Wi-Fi is a great technology for taking your laptop to the conference room. You know, it's effectively wireless LAN Where private 5G and before that private LTE had come into play is where there's a number of attributes of your application, what you're using it for, for which Wi-Fi is not as well suited. And so, you know, that plays out in different verticals in different ways. Either maybe you need a much higher capacity than Wi-Fi, better security than Wi-Fi, wider coverage like outdoor, and in many cases a more predictable reliability. So cellular is just a different way of handling the wireless interface that provides those attributes. So, you know, I think at the beginning, the first several years, you know Wi-Fi and 5G are going to live side by side in the enterprise for their different roles. How that plays out in the long term? We'll see how they each evolve. >> But I think anybody can relate to that. I mean, Wi-Fi's fine, you know, we have our issues with Wi-Fi. I'm having a lot of issues with Wi-Fi this week, but generally speaking, it works just fine. It's ubiquitous, it's cheap, okay. But I would not want to run my factory on it and rely on it for my robots that are shipping products, right? So that really is kind of the difference. It's really an industry 4.0 type. >> Yeah, exactly. So I mean, manufacturing's an important vertical, but things of energy and mining and things like that they're all outdoor, right? So you actually need the scale that comes, with a higher power technology, and even, you know just basic things like running cameras in a retail store and using AI to watch for certain things. You get a much better latency performance on private 5G and therefore are able to run more sophisticated applications. >> So I could be doing realtime inference. I can imagine Dave, I got an arm processor I'm doing some realtime inference AI at the Edge. You know, you need something like 5G to be able to do that, you can't be doing that over Wi-Fi. >> Yeah >> You nailed it. I mean that's exactly the difference, right? I mean if you look at Wi-Fi, it grow out from a IT enabled mode, right? You got to replace an ethernet. It was an IT extension. A LAN extension. Cellular came up from the mode of, "Hey, when I have that call, I need for it to be consistent and I need for it to be always available," right? So it's a different way of looking at it. Not to say one is better, the other is not better. It's just a different philosophy behind the technologies and they're going to coexist because they meet diverse needs. >> Now you have operators who embrace the idea of 5G obviously, and even private 5G. But the sort of next hurdle to overcome for some, is the idea of open standards. What does the landscape look like right now in terms of those conversations? Are you still having to push people over that hump, to get them beyond the legacy of proprietary closed stacks? >> Yeah, so I think I look, there are still people who are advocating that. And I think in the carrier's core networks it's going to take a little longer their main, you know macro networks that they serve the general public. In the private network though, the opportunity to use open standard and open technology is really strong because that's how you bring the innovation. And that's what we need in order to be able to solve all these different business problems. You know, the problems in retail, and healthcare and energy, they're different. And so you need to be able to use this open stack and be able to bring different elements of technology and blend it together in order to serve it. Otherwise we won't serve it. We'll all fail. So that's why I think it's going to have a quicker path in private. >> And the only thing to add to that is if you look at private 5G and the deployment of private LTE or private 5G, right? There is no real technology debt that you carry. So it's easy for us to say, "Hey, the operators are not listening, they're not going open." But hey, they have a technical debt, they have 2G, 3G, 4G, 5G, systems, right? >> Interviewer: Sure. >> But the reason we are so excited about private 5G and private 4G, is right off the bat when we go into an enterprise space, we can go open. >> So what exactly is Dell's role here? How do you see, obviously you make hardware and you have solutions, but you got to open ecosystems. You got, you know, you got labs, what do you see your role in the ecosystem? Kind of a disruptor here in this, when I walk around this show. >> Well a disruptor, also a solution provider, and system integrator. You know, Sarvesh and I are part of the telecom practice. We have a big Edge practice in Dell as well. And so for this space around private 5G, we're really teamed up with our cohort in the Edge business unit. And think about this as, it's not just private 5G. It's what are you doing with it? That requires storage, it requires compute, it requires other applications. So Dell brings that entire package. There definitely are players who are just focused on the connectivity, but our view is, that's not enough. To ask the enterprise to integrate that all themself. I don't think that's going to work. You need to bring the connectivity and the application to storage compute the whole solution. >> Explain Telecom and and Edge. They're different but they're like cousins in the Dell organization. Where do you guys divide the two? >> You're saying within Dell? >> Yeah, within Dell. >> Yeah, so if you look at Dell, right? Telecom is one of our most newest business units. And the way it has formed is like we talk Edge all the time, right? It's not new. Edge has always been around. So our enterprise Edge has always been around. What has changed with 5G is now you can seamlessly move between the enterprise Edge and the telecom Edge. And for that happen you had to bring in a telecom systems business unit that can facilitate that evolution. The next evolution of seamless Edge that goes across from enterprise all the way into the telco and other places where Edge needs to be. >> Same question for the market, because I remember at Dell Tech World last year, I interviewed Lowe's and the discussion was about the Edge. >> John: Yep. >> What they're doing in their Edge locations. So that's Edge. That's cool. But then I had, I had another discussion with an agriculture firm. They had like the massive greenhouses and they were growing these awesome tomatoes. Well that was Edge too. It was actually further Edge. So I guess those are both Edge, right? >> Sarvesh: Yeah, yeah, yeah. >> Spectrum there, right? And then the telecom business, now you're saying is more closely aligned with that? >> Right. >> Depending on what you're trying to do. The appropriate place for the Edge is different. You, you nailed it exactly, right. So if you need wide area, low latency, the Edge being in the telecom network actually makes a lot of sense 'cause they can serve wide area low latency. If you're just doing your manufacturing plant or your logistics facility or your agricultural growing site, that's the Edge. So that's exactly right. And the tech, the reason why they're close cousins between telecom and that is, you're going to need some kind of connectivity, some kind of connectivity from that Edge, in order to execute whatever it's you're trying to do with your business. >> Nature's Fresh was the company. I couldn't think of Nature's Fresh. They're great. Keith awesome Cube guest. >> You mentioned this mix of Wi-Fi and 5G. I know it's impossible to predict with dates certain, you know, when this, how's this is going to develop. But can you imagine a scenario where at some point in time we don't think in terms of Wi-Fi because everything is essentially enabled by a SIM or am I missing a critical piece there, in terms of management of spectrum and the complicated governmental? >> Yeah, there is- >> Situation, am I missing something? It seems like a logical progression to me, but what am I missing? >> Well, there is something to be said about spectrum, right? If you look at Wi-Fi, as I said, the driver behind the technology is different. However, I fully agree with you that at some point in time, whether it's Wi-Fi behind, whether it's private 5G behind becomes a moot point. It's simply a matter of, where is my data being generated? What is the best technology for me to use to ingest that data so I can derive value out of that data. If it means Wi-Fi, so be it. If it means cellular, so be it. And if you look at cellular right? The biggest thing people talk about SIMs. Now if you look at 5G standard. In 5G standard, you have EAPTLS, which means there is a possibility that SIMs in the future go away for IoT devices. I'm not saying they need to go away for consumer devices, they probably need to be there. But who's to say going ahead for IoT devices, they all become SIM free. So at that point, whether you Wi-Fi or 5G doesn't matter. >> Yeah, by the way, on the spectrum side people are starting to think about the concept. You might have heard this NRU, new radio unlicensed. So it's running the Wi-Fi standard, but in the unlicensed bands like Wi-Fi. So, and then the last piece is of course you know, the cost, the reality it stays 5G still new technology, the endpoints, you know, what would go in your laptop or a sensor et cetera. Today that's more expensive than Wi-Fi. So we need to get the volume curve down a little bit for that to really hit every application. I would guess your vision is correct. >> David: Yep >> But who can predict? >> Yeah, so explain more about what the unlicensed piece means for organizations. What does that for everybody? >> That's more of a future thing. So you know, just- >> No, right, but let's put on our telescope. >> Okay, so it's true today that Wi-Fi traditionally runs in the bands that have been licensed by the government and it's a country by country thing, right? >> Dave: Right. >> What we did in the United States was CBRS, is different than what they've done in Germany where they took part of the Zurich C-band and gave it to the enterprises. The telco's not involved. And now that's been copied in Japan and Korea. So it's one of the complications unfortunately in the market. Is that you have this different approach by regulators in different countries. Wi-Fi, the unlicensed band is a nice global standard. So if you could run NR just as 5G, right? It's another name for 5G, run that in the unlicensed bands, then you solve the spectrum problem that Dave was asking about. >> Which means that the market really opens up and now. >> It would be a real enabler >> Innovation. >> Exactly. >> And the only thing I would add to that is, right, there are some enterprises who have the size and scale to kind of say, "Hey, I'm going the unlicensed route. I can do things on my own." There are some enterprises that still are going to rely on the telcos, right? So I don't want to make a demon out of the telcos that you own the spectrum, no. >> David: Sure. >> They will be offering a very valuable service to a massive number of small, medium enterprises and enterprises that span regional boundaries to say, hey we can bring that consistent experience to you. >> But the primary value proposition has been connectivity, right? >> Yes. >> I mean, we can all agree on that. And you hear different monetization models, we can't allow the OTT vendors to do it again. You know, we want to tax Netflix. Okay, we've been talking about that all week. But there may be better models. >> Sarvesh: Yes. >> Right, and so where does private network fit into the monetization models? Let's follow the money here. >> Actually you've brought up an extremely important point, right? Because if you look at why haven't 5G networks taken off, one of the biggest things people keep contrasting is what is the cost of a Wi-Fi versus the cost of deploying a 5G, right? And a portion of the cost of deploying a 5G is how do you commercialize that spectrum? What is going to be the cost of that spectrum, right? So the CSPs will have to eventually figure out a proper commercialization model to say, hey listen, I can't just take what I've been doing till date and say this is how I make. Because if you look at 5G, the return of investment is incremental. Any use case you take, unless, let's take smart manufacturing, unless the factory decides I'm going to rip and replace everything by a 5G, they're going to introduce a small use case. You look at the investment for that use case, you'll say Hmm, I'm not making money. But guess what? Once you've deployed it and you bring use case number two, three, four, five, now it starts to really add value. So how can a CSP acknowledge that and create commercial models to enable that is going to be key. Like one of the things that Dell does in terms of as a service solution that we offer. I think that is a crucial way of really kick starting 5G adoption. >> It's Metcalfe's Law in this world, right? The first telephone, not a lot of value, second, I can call one person, but you know if I can call a zillion now it's valuable. >> John: Now you got data. >> Yeah, right, you used a phrase, rip and replace. What percentage of the market that you are focusing on is the let's go in and replace something, versus the let's help you digitally transform your business. And this is a networking technology that we can use to help you digitally transform? The example that you guys have with the small breweries, a perfect example. >> Sarvesh: Yeah. >> You help digitize, you know, digitally transform their business. You weren't going in and saying, I see that you have these things connected via Wi-Fi, let's rip those out and put SIMs in. >> No. >> Nope, so you know- >> That's exactly right. It's enabling new things that either couldn't be achieved before or weren't. So from a private 5G perspective, it's not going to be rip and replaced. As I said, I think we'll coexist with Wi-Fi, it's still got a great role. It's enabling those, solving those business problems that either hadn't been solved before or could not be solved with other technology. >> How are you guys using AI? Everybody's talking about ChatGPT. I love ChatGPT, we use it all the time. Love it, hate it, you know, whatever. It's a fun topic. But AI generally is here in a way that it wasn't when the enterprise disaggregated. >> John: Right. >> So there's AI, there's automation, there's opportunities there. How do they fit into private 5G? >> So if you look at it, right, AI, AI/ML is actually crucial to value extraction from that data, because all private 5G is doing is giving you access to that precious data. But that data by itself means nothing, right? You get access to the data, extracting value out of the data that bring in business value is all going to be AI/ML. Whether it's computer vision, whether it's data analytics on the fly so that you can, you know do your closed loop controls or what have you. All of these are going to be AI/ML models. >> Dave: Does it play into automation as well? >> Absolutely, 'cause they drive the automation, right? You learn your AI models, drive their automation. Control, closed loop control systems are a perfect example of their automation. >> Explain that further. Like give us an example. >> So for example, let's say we're talking about a smart manufacturing, right? So you have widgets coming down the pipe, right? You have your computer vision, you have your AI/ML model that says, "Hey, I'm starting to detect a consistent error in the product being manufactured. I'm going to close loop that automation and either tweak the settings of the machine, shut down the machine, open a workflow, escalate it for human intervention." All that automation is facilitated by the AI/ML models >> And that, and by the way, there's real money in that, right? If you're making your power and you're making it wrong, you don't detect it for hours, there's real money in fixing that >> Right. >> So I've got a, I've got an example albeit a slight, not even slightly, but a tragic one. Let's say you have a train that's rolling down the tracks at every several miles or so, temperature readings are taken from bearings in the train. >> Sarvesh: Yes, yes. >> Wouldn't it be nice to have that be happening in real time? >> Sarvesh: Yes. >> So it doesn't reach that critical point >> Yes. >> Where then you have a derailment. >> Yes. >> Yeah, absolutely. >> I mean, those are, it's doesn't sound sexy in terms of "Hey, what a great business use case that we can monetize." >> John: Yeah. >> But I'll bet you in hindsight that operator would've loved to have that capability. >> John: Yeah. >> Sarvesh: Right. >> To be able to shut the train down and not run. >> That's a great example where the carrier is actually, probably in a good position, right? Cause you got wide area, you want low latency. So the traditional carriers would be able in great position to provide that exact service. Telemetry is another great example. We've been talking about other kinds of automation, but just picking up measurements and so on. The other example of that is in oil and gas, right? As you've got pipelines running around you're measuring pressure, temperature, you detect a leak, >> David: Right. >> in minutes, not weeks. >> David: Right. >> So there's a lot of good examples of things like that >> To pick up in a point, Dave. You know, it's like you look at these big huge super tankers, right? They have big private networks on that super tanker to monitor everything. If on this train we had, you know, we hear about so many Edges, let's call one more the rolling Edge. >> Yeah. >> Right, that, that Edge is right on that locomotive tracking everything with AI/ML models, detecting things, warning people ahead of time shutting it down as needed. And that connectivity doesn't have to be wired. It can be a rolling wireless. It potentially could be a spectrum that's you know, open spectrum in the future. Or as you said, an operator could facilitate that. So many options, right? >> Yeah, got to double down on this. Look, I know 'cause I've been involved in some of these projects. Amusement park operators are doing this for rides. >> John: Yes. >> Sarvesh: Yep. >> So that they can optimize the amount of time the ride is up, so they can shorten lines >> Yes. >> So that they can get people into shops to buy food and souvenirs. >> John: Yes. >> Certainly we should be able to do it to protect infrastructure. >> Sarvesh: Absolutely. >> Right, so- >> But I think the ultimate point you're making is, it's actually quite finally segmented. There's so many different applications. And so that's why again, we come back to what we started with is at Dell, we're bringing the solution from Edge, compute, application, connectivity, and be able to bring that across all these different verticals and these different solutions. The other amusement park example, by the way, is as the rides start to invest in virtual reality, so you're moving, but you're seeing something, you need some technology like 5G to have low latency and keep that in sync and have a good experience on the ride. >> To 5G and beyond, gents. Thanks so much for coming on theCUBE. >> All right, thank you Dave. >> It was great to have you. >> Thank, thank you guys. >> Great to meet you guys. Thank you very much. >> Great, all right. Keep it right there. For David Nicholson and Dave Vellante, This is theCUBE's coverage of MWC23. Check out siliconangle.com for all the news. theCUBE.net is where all these videos live. John Furrier is in our Palo Alto office, banging out that news. Keep it right there. Be right back after this short break. (gentle upbeat music)

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

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

>> theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Lisa Martin: Good afternoon everyone. theCUBE live in Barcelona, Spain at MWC23. Lisa Martin with Dave Nicholson. Day three of our wall-to-wall coverage of four days of CUBE content. I know, it's amazing. We're going to have a great conversation next with DISH and Dell, talking about the value of automation and telecom for 5G. Please welcome Song Toh, Senior Director of Product Management Infrastructure Automation at Dell. And Sidd Chenumolu, VP of Technology Development at DISH. Guys, great to have you on theCUBE. >> Thanks for having us. >> Thank you, it's a pleasure. >> So let's go ahead and start with you. We know that DISH is developing its own open cloud native 5G network from the ground up. Talk to us about before you were working with Dell, what the situation was like and why you brought Dell in to help drive the innovation. >> Ah, that's a good question. So, three years ago we started the journey, and one thing that was very clear to us is that we want to work with the partners who are going to be the leaders in this space. And it was very clear we are going to be in the cloud side- we are going to be in hybrid cloud, we are going to have our own data centers. Everything that we built is going to replicate a cloud model. 'Cause it was very, like we said, what is 5G? Fundamentally, if you think about 5G, right? Everyone says people talk about speeds. Okay, get it. But it's also about vertical industries. It's about customization of a network, application driven network. That's the way I call it, because if you walk around the floor right now, everyone's talking about monetization of 5G, everyone keeps doing enterprise. So you put two and two together, what do you get? That means you have to work with the leaders who have been serving enterprises forever, who know the enterprises' pain, they know all the problem statements. So we said, "okay, let's see who's out there and who can help us." And then obviously, Dell comes to the picture. So we had a good conversation, there was an alignment in where Dell wanted to go long-term, so we saw synergies. So we had a vision, we needed their help. They wanted to get into this space too. So there was an agreement, let's do it together. And it's been a good partnership since then. >> What were some of the challenges that you had at that time? Going, "we've got some challenges here, some risks, we want to move DISH forward and automate." Talk about some of those challenges that helped you understand, "Yeah, Dell's the right partner for this." >> Oh, first is when we started this, right? I'll be honest, I don't think we anticipated the complexity. We didn't know what we didn't know. So initially it was learning from Dell, who was more like teaching us, "this is what you're going to see, this is how it's going to look like". And then we started bringing the telco aspects on top of it. So it was not like, "I'm going to build a 5G". We said, "no, Dell, tell me what does the data center look like? Tell me the day-to-day challenges. How do you bring a server in? How the rack looks like, what are the connectivity?" So, learning, then you bring the telco as an application, it was not like a telco first. It was like a software first, infrastructure second, now you bring in the telco part of it. So, I mean, challenges I would say, right? Everything was new, pretty much across the board for us. It was not just one thing. We were doing Open RAN, which was a brand new cloud native, was completely new. 5G standalone was new. No one had done that before, and (mumbles) was hybrid cloud. So I think we were on a stool sitting on the, with the four legs, all were wobbly. (laughs) We made it. So, automation was definitely the key. We knew we had to go at a scale, because we are in FCC deployment, we are meeting like- we will be at covering 230 million pops by June of this year. So, aggressive timelines- >> Dave Nicholson: Wait, say that again. How many, so say that ag- how many? >> 230 million. >> And, pops being points of presence? >> No, sorry, population. U. S.- >> Oh, population. Okay, I'm sorry, I'm sorry. Okay, okay. >> Okay. I'm sorry, I'm- >> So, very aggressive buildout for us. >> Wow. >> And automation has to be the key for it, because we just cannot- first is, we cannot scale a company. We are a startup. This wireless is a startup. That's how we started with a handful of people. We obviously hired a lot of people since then, but we said, "we will never be at the scale of the existing CSPs today." We can't. Time is not on our side, and we don't want to be at that scale anyway, 'cause we want to be nimble, move fast. So what do you need? Automation. Automation at every layer. And it's a journey. Never stops. >> No, it doesn't stop. >> Oh yeah, I'm sorry. Yeah, she's- >> Go ahead and get a question in. I don't want to hog. >> So when most people hear DISH, they think of streaming content, they think of alternative to cable provider. >> Sidd: Yeah. >> In that space. But just clarify for us all of the things that DISH is involved with today, and what DISH aspires to be involved with as we move forward. >> Good question. We want to be in the connectivity space. We want to connect everything. That's our goal, our mission statement. We started with the satellite, since then we moved on to the IPTV Sling, which is a leader. So we are not afraid to take risks, right? So what we own- we own satellites, we know content delivery very well. I think we are done there for many years. We agreed to that. Now we said, "now we understand wireless". What we want to do is, we want to deliver the data to the customers, and whether it could be videos, it could be audio data, like voice, anything, or it could be a machine. We just want to be in the connectivity space of connecting everything, and based on- you look around, right? It's all about connectivity. Everything requires connectivity. It's all about data monetization, and we want to be there in every aspect of it. >> Connectivity is almost the lifeblood these days of everything that we do, right? >> Sidd: Yep. >> Song: Indeed. >> And of every industry. Song, talk a little bit about the DISH Wireless use case. How some of their challenges in telco really maybe helped even Dell accelerate its presence in telco. >> Absolutely, right. I think one thing that Sidd mentioned, right, 230 million populations, but what does that translate to in terms of infrastructure deployment? 'Cause he said it's a startup. They started from not a whole lot in terms of coverage. So, in terms of 5G deployment, whether it's virtualized or open RAN, there needs to be distributed infrastructure that covers the entire United States, right? A certain percentage of the population is still a huge amount of coverage. So deploy tens of thousands, hundreds of thousands of servers around the country, get them set up, get 'em configured, and maintain and monitor and meter all of that. We help DISH to essentially roll that out, get it going, and then they deploy their RAN workload on top. I mean, that's a very significant undertaking. We were very proud to be able to offer our Bare Metal Orchestrator to facilitate that, but ultimately their success is their success. We are there to help, right? We are partnered, and we- happy to definitely be able to say we got to a point that we are happy, you know, in claims of success there. >> Well, that's why we selected Dell. >> Thank you. (chuckles) >> Let's unpack a little bit of some of the successes, some of the outcomes that you've achieved so far, working with Dell. >> Let me give an example. Today we have an ability to upgrade, update, even swap a RAN vendor overnight. Entire market, unheard of overnight. Give me hours, I'll do the entire thing for you from scratch. We can instantiate entire data center racks remotely in a matter of minutes. Cannot do that without automation, and with the help- >> Lisa Martin: Couldn't do it before. >> We have curated an extraordinary, what you call orchestration mechanism of finely tuned data sets and pipelines. It's like a machine. It keeps spinning. It's very good. And again, not something that happened overnight. Took us several months to get there with a lot of our partners, and Dell was there. >> Song: Right. >> I'd be curious to get your perspectives, each of you, on some of the buzz that was going on around the show where the telecom, "plumbing providers"- >> Sidd: Yep. >> have complained about the content streaming through, and maybe they need to charge more for access, and Netflix hit back and said, "well, hold on a minute. You wouldn't have anything to deliver to your customers if it weren't for the content we are producing. Maybe we need licensing fees from you." >> Song: (chuckles) >> What is your view on that, in terms of this whole over the top conversation? DISH seems sort of, kind of in a hybrid position there. >> Well, it's a very complex question. I think everyone is struggling with it, so I'm not sure if I have the right answer for it. We are definitely unique because we own the content too. We want to offer- we probably may offer our own content over the wireless service. There is a pros and cons. I mean, purely from a, as a M&O service provider, it's a lot of effort and cost for us to deliver huge amount of bandwidth. But again, the networks are being built to handle huge capacity. So if you don't have video, what do we do? That's also a realistic question. I think there is a mechanism where the cost and the value both have to be shared. So that it's a win-win for everyone. It's not lopsided to one. And said, "you carry most of the cost", and I'm transparent, it doesn't work that way long term. And especially given the 5G side, with all the slicing capabilities and ability to offer QoS, better quality of experience, I think there's a value to be created here. >> If you look at the infrastructure necessary to drive all of these things- >> Right. >> We've seen, just go back to our own consumer experience with the internet. We've gone from text to images to video. >> Song: Right. >> To high definition video. >> Sidd: Yeah. >> To, is 8K video absurd? Do we really need to be able to handle that? What are the things that need to be supported as we move forward? Is it that we scale out into this world of billions and billions of things that are connected? Or are there these much bigger, fatter pipelines for things like 8K video or it a combination of the two? What is Dell thinking of when it thinks of the infrastructure that it builds and how you customize that- >> Song: Right. >> to address those things? What's on the horizon? >> Dave, I think that's a very good question, right? Certainly communication service providers like DISH has built out the capacity to handle the customers that they want to serve. But there's another aspect of this I think I'd like to add on top of the question you posed, it's not about say, 10, say a thousand streams of 8K. I would need to be able to handle that. I think the present challenge right now is really, say there's a sports stadium that you need to activate so that, not about everyone filming that sports game, it's about, "Hey, I got to tell my, whoever- I got a 10 seconds video clip that I got to share with my friends." It's also not about copyright. It's more about- >> Dave Nicholson: (laughs) >> can you as a provider- >> The NFL is listening. >> Exactly. Can you as a provider handle that service? Because otherwise your customers say, "oh, I got into the sports stadium, every time I could not even text my daughter." >> Dave Nicholson: Yeah. >> So, how do you then scale up the infrastructure as needed, the bandwidth as needed, and scale back down when it's not? Maybe, because the infrastructure can potentially be repurposed for a different workload too. That requires automation, right? From bottom to top, all the way, infrastructure - all the way up to the workload. And that's I think a question that people are starting to ask. I'm not sure. I mean, still you guys have thought about that too as a- >> I mean, instant gratification is the new thing, right? Everyone wants instant response, everyone feedback, everything. So connectivity is given. I do think there is a space for both billions of devices and the 8K and probably 16K in the future. It's going to happen in the technology walls. That's why I like, say, the 5G, and especially the way we architect our own network. We call it network of networks. I'm not designing a network that is only for one monolithic application or one stack only. We are actually programmable network, because I know network A is for 8K. Network B is for IOT, network C is for regular, network D is for something else. And the list can keeps on growing. I don't think we can stifle innovation at any level and said, "well you can't do this because we are not ready." World is going to move too fast. Technology is too fast for all of us. >> But do you have to prioritize? >> If there is a business for- it's all top-down driven, not much of a technology driven. If there's a business, someone said there's a value to be made, it's prioritized. >> What's your - Sidd, we'll stick with you, your observations. This is day three of MWC 23. Lot of talk here on disaggregation. A lot of talk about open RAN, a lot of talk about private 5G wireless networks, but also some controversy. You brought up the Netflix controversy. >> Dave: Yep. >> What are some of the messages that you've heard so far from this event - and then, Song, we'll ask you the same question - that excite you about the direction that the industry's going and where DISH Wireless stands within it? >> Yeah. I mean, I didn't have a chance to walk the floor, but for wherever I have been in the last two and a half days, one thing that stood out is people are no longer talking about gigabits capacity anymore. They're talking about monetization of the network. Everyone is talking APIs now. >> Lisa Martin: Yeah. >> That's the buzzword. If I said monetization, API- I got a beautiful network not tell me how to make money off it and how do I work with each other? It's no longer about "I want to own it all." What can I do to partner with A, partner with B? How can we all grow together? I think that's the theme that I see this year compared to the previous years, where it was always about like, "hey, build the best 5G network with the high speeds, big radios." I don't even see radios, by the way. >> Lisa Martin: (chuckles) >> Interesting. Yeah. So the actual, it's almost fascinating when you toil in obscurity to build these critical components and then people ignore you. So I feel for the radio people. >> Song: (laughs) Being a long-term infrastructure guy, what have you been seeing here that's interesting? >> Well, a few things that I feel quite excited about from the conversation I've had. One is, on the private mobility side, Lisa, as you said, I'm seeing certainly customers, partners, and even in the booth talking about what the use cases are, right? Rather than, "Hey, here's a cool technology." But actually, people talking about use cases now. And, with the communication service providers and the operators, I'm hearing - of course, I mean, Sidd's doesn't have that problem because it's building whole new, >> (chuckles) >> but there are other providers that are saying, "Hey, we acknowledge that we need to transform and we are on the way too", rather than saying, "can I not do it and still, you know, live with the modern world." So I feel that we always need to be ready to change, because the world, the market, and all other factors will cause us to either change or really to change. And I think we are changing. Open system is becoming more of a expected, you know, future. It's just how do we get there, right? What do we need to learn as we get there? And we're happy to provide the support as a partner, the automation technology, and even the solutions to enable that, from Dell's perspective. >> So DISH in particular? DISH Wireless. >> Yep. >> Despite the fact that everyone's heard of DISH. >> Song: Yeah. >> DISH has been around for a long time. Where you sit within DISH Wireless, you described it as a startup. You don't feel encumbered by a lot of the legacy things that maybe some other providers do. Is that a fair statement or are you having to navigate that? We call it ambidextrous management >> (laughs) >> in the CTO world, where it's like, got to keep the lights on, got to keep the existing revenue flowing, also got to innovate. How do you blend the two? Is that a challenge? >> Well, probably not a challenge for me. I'm on the wireless technology and architecture side, so I get to do the cool stuff. >> Dave Nicholson: Okay. >> Don't have to worry about day to day operations, some complexity or revenue. Someone else is managing that complex part. They let me play with my toys. >> Well played, well played. >> Guys, it's been great having you on the program talking about what DISH Wireless is doing with Dell. Thanks to Dell. We're going to be watching this space to see how you continue to innovate. Thank you so much for joining us on the program. >> Thanks for having us. >> Thank you. >> Our pleasure. >> For our guests and for Dave Nicholson. I'm Lisa Martin. You're watching theCUBE live from MWC 23. Stick around. Our next guest joins Dave and me in just a minute. (upbeat music) (upbeat music) (upbeat music)

>> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (intro music) >> Good afternoon, everyone. Welcome back to "theCUBE's" day three coverage of MWC23. Lisa Martin here in Spain, Barcelona, Spain with Dave Nicholson. We're going to have a really interesting conversation next. We're going to really dig into MWC, it's history, where it's going, some of the controversy here. Please welcome our guests. We have Jim Harris, International Best Selling Author of "Blindsided." And Carolina Milanese is here, President and Principle Analyst of creative strategies. Welcome to "theCUBE" guys. Thank you. >> Thanks. So great to be here. >> So this is day three. 80,000 people or so. You guys have a a lot of history up at this event. Caroline, I want to start with you. Talk a little bit about that. This obviously the biggest one in, in quite a few years. People are ready to be back, but there's been some, a lot of news here, but some controversy going on. Give us the history, and your perspective on some of the news that's coming out from this week's event. >> It feels like a very different show. I don't know if I would say growing up show, because we are still talking about networks and mobility, but there's so much more now around what the networks actually empower, versus the network themselves. And a little bit of maybe that's where some of the controversy is coming from, carriers still trying to find their identity, right, of, of what their role is in all there is to do with a connected world. I go back a long way. I go back to when Mobile World Congress was called, was actually called GSM, and it was in Khan. So, you know, we went from France to Spain. But just looking at the last full Mobile World Congress here in Barcelona, in pre-pandemic to now, very different show. We went from a show that was very much focused on mobility and smartphones, to a show that was all about cars. You know, we had cars everywhere, 'cause we were talking about smart cities and connected cars, to now a show this year that is very much focused on B2B. And so a lot of companies that are here to either work with the carriers, or also talk about sustainability for instance, or enable what is the next future evolution of computing with XR and VR. >> So Jim, talk to us a little bit about your background. You, I was doing a little sleuthing on you. You're really focusing on disruptive innovation. We talk about disruption a lot in different industries. We're seeing a lot of disruption in telco. We're seeing a lot of frenemies going on. Give us your thoughts about what you're seeing at this year's event. >> Well, there's some really exciting things. I listened to the keynote from Orange's CEO, and she was complaining that 55% of the traffic on her network is from five companies. And then the CEO of Deutsche Telecom got up, and he was complaining that 60% of the traffic on his network is from six entities. So do you think they coordinated pre, pre-show? But really what they're saying is, these OTT, you know, Netflix and YouTube, they should be paying us for access. Now, this is killer funny. The front page today of the show, "Daily," the CO-CEO of Netflix says, "Hey, we make less profit than the telcos, "so you should be paying us, "not the other way around." You know, we spend half of the money we make just on developing content. So, this is really interesting. The orange CEO said, "We're not challenging net neutrality. "We don't want more taxes." But boom. So this is disruptive. Huge pressure. 67% of all mobile traffic is video, right? So it's a big hog bandwidth wise. So how are they going to do this? Now, I look at it, and the business model for the, the telcos, is really selling sim cards and smartphones. But for every dollar of revenue there, there's five plus dollars in apps, and consulting and everything else. So really, but look at how they're structured. They can't, you know, take somebody who talks to the public and sells sim cards, and turn 'em in, turn 'em in to an app developer. So how are they going to square this circle? So I see some, they're being disrupted because they're sticking to what they've historically done. >> But it's interesting because at the end of the day, the conversation that we are having right now is the conversation that we had 10 years ago, where carriers don't want to just be a dumb pipe, right? And that's what they are now returning to. They tried to be media as well, but that didn't work out for most carriers, right? It is a little bit better in the US. We've seen, you know, some success there. But, but here has been more difficult. And I think that's the, the concern, that even for the next, you know, evolution, that's the, their role. >> So how do they, how do they balance this dumb pipe idea, with the fact that if you make the toll high enough, being a dumb pipe is actually a pretty good job. You know, sit back, collect check, go to the beach, right? So where, where, where, where does this end up? >> Well, I think what's going to happen is, if you see five to 15 X the revenue on top of a pipe, you know, the hyperscalers are going to start going after the business. The consulting companies like PWC, McKinsey, the app developers, they're... So how do you engage those communities as a telco to get more revenue? I think this is a question that they really need to look at. But we tend to stick within our existing business model. I'll just give you one stat that blows me away. Uber is worth more than every taxi cab company in North America added together. And so the taxi industry owns billions in assets in cars and limousines. Uber doesn't own a single vehicle. So having a widely distributed app, is a huge multiplier on valuation. And I look to a company like Safari in Kenya, which developed M-Pesa, which Pesa means mo, it's mobile money in Swahili. And 25% of the country's GDP is facilitated by M-Pesa. And that's not even on smartphones. They're feature phones, Nokia phones. I call them dumb phones, but Nokia would call them "feature phones." >> Yeah. >> So think about that. Like 25, now transactions are very small, and the cut is tiny. But when you're facilitating 25% of a country's GDP, >> Yeah. >> Tiny, over billions of transactions is huge. But that's not the way telcos have historically thought or worked. And so M-Pesa and Safari shows the way forward. What do you think on that? >> I, I think that the experience, and what they can layer on top from a services perspective, especially in the private sector, is also important. I don't, I never believe that a carrier, given how they operate, is the best media company in the world, right? It is a very different world. But I do think that there's opportunity, first of all, to, to actually tell their story in a different way. If you're thinking about everything that a network actually empowers, there's a, there's a lot there. There's a lot that is good for us as, as society. There's a lot that is good for business. What can they do to start talking about differently about their services, and then layer on top of what they offer? A better way to actually bring together private and public network. It's not all about cellular, wifi and cellular coming together. We're talking a lot about satellite here as well. So, there's definitely more there about quality of service. Is, is there though, almost a biological inevitability that prevents companies from being able to navigate that divide? >> Hmm. >> Look at, look at when, when, when we went from high definition 720P, very exciting, 1080P, 4K. Everybody ran out and got a 4K TV. Well where was the, where was the best 4K content coming from? It wasn't, it wasn't the networks, it wasn't your cable operator, it was YouTube. It was YouTube. If you had suggested that 10 years before, that that would happen, people would think that you were crazy. Is it possible for folks who are now leading their companies, getting up on stage, and daring to say, "This content's coming over, "and I want to charge you more "for using my pipes." It's like, "Really? Is that your vision? "That's the vision that you want to share with us here?" I hear the sound of dead people walking- (laughing) when I hear comments like that. And so, you know, my students at Wharton in the CTO program, who are constantly looking at this concept of disruption, would hear that and go, "Ooh, gee, did the board hear what that person said?" I, you know, am I being too critical of people who could crush me like a bug? (laughing) >> I mean, it's better that they ask the people with money than not consumers to pay, right? 'Cause we've been through a phase where the carriers were actually asking for more money depending on critical things. Like for instance, if you're doing business email, then were going to charge you more than if you were a consumer. Or if you were watching video, they would charge you more for that. Then they understood that a consumer would walk away and go somewhere else. So they stopped doing that. But to your point, I think, and, and very much to what you focus from a disruption perspective, look at what Chat GTP and what Microsoft has been doing. Not much talk about this here at the show, which is interesting, but the idea that now as a consumer, I can ask new Bing to get me the 10 best restaurants in Barcelona, and I no longer go to Yelp, or all the other businesses where I was going to before, to get their recommendation, what happens to them? You're, you're moving away, and you're taking eyeballs away from those websites. And, and I think that, that you know, your point is exactly right. That it's, it's about how, from a revenue perspective, you are spending a lot of money to facilitate somebody else, and what's in it for you? >> Yeah. And to be clear, consumers pay for everything. >> Always. Always. (laughs) >> Taxpayers and consumers always pay for everything. So there is no, "Well, we're going to make them pay, so you don't have to pay." >> And if you are not paying, you are the product. Exactly. >> Yes. (laughing) >> Carolina, talk a little bit about what you're seeing at the event from some of the infrastructure players, the hyperscalers, obviously a lot of enterprise focus here at this event. What are some of the things that you're seeing? Are you impressed with, with their focus in telco, their focus to partner, build an ecosystem? What are you seeing? >> I'm seeing also talk about sustainability, and enabling telco to be more sustainable. You know, there, there's a couple of things that are a little bit different from the US where I live, which is that telcos in Europe, have put money into sustainability through bonds. And so they use the money that they then get from the bonds that they create, to, to supply or to fuel their innovation in sustainability. And so there's a dollar amount on sustainability. There's also an opportunity obviously from a growth perspective. And there's a risk mitigation, right? Especially in Europe, more and more you're going to be evaluated based on how sustainable you are. So there are a lot of companies here, if you're thinking about the Ciscos of the world. Dell, IBM all talking about sustainability and how to help carriers measure, and then obviously be more sustainable with their consumption and, and power. >> Going to be interesting to see where that goes over the years, as we talk to, every company we talk to at whatever show, has an ESG sustainability initiative, and only, well, many of them only want to work with other companies who have the same types of initiative. So a lot of, great that there's focus on sustainability, but hopefully we'll see more action down the road. Wanted to ask you about your book, "Blind," the name is interesting, "Blindsided." >> Well, I just want to tag on to this. >> Sure. >> One of the most exciting things for me is fast charging technology. And Shalmie, cell phone, or a smartphone maker from China, just announced yesterday, a smartphone that charges from 0 to 100% in five minutes. Now this is using GAN FEST technology. And the leader in the market is a company called Navitas. And this has profound implications. You know, it starts with the smartphone, right? But then it moves to the laptops. And then it'll move to EV's. So, as we electrify the $10 trillion a year transportation industry, there's a huge opportunity. People want charging faster. There's also a sustainability story that, to Carolina's point, that it uses less electricity. So, if we electrify the grid in order to support transportation, like the Tesla Semi's coming out, there are huge demands over a period. We need energy efficiency technologies, like this GAN FEST technology. So to me, this is humongous. And it, we only see it here in the show, in Shalmie, saying, "Five minutes." And everybody, the consumers go, "Oh, that's cool." But let's look at the bigger story, which is electrifying transportation globally. And this is going to be big. >> Yeah. And, and to, and to double click on that a little bit, to be clear, when we talk about fast charging today, typically it's taking the battery from a, not a zero state of charge, but a relatively low state of charge to 80%. >> Yep. >> Then it tapers off dramatically. And that translates into less range in an EV, less usable time on any other device, and there's that whole linkage between the power in, and the battery's ability to be charged, and how much is usable. And from a sustainability perspective, we are going to have an avalanche of batteries going into secondary use cases over time. >> They don't get tossed into landfills contrary to what people might think. >> Yep. >> In fact, they are used in a variety of ways after their primary lifespan. But that, that is, that in and of itself is a revolutionary thing. I'm interested in each of your thoughts on the China factor. Glaringly absent here, from my perspective, as sort of an Apple fanboy, where are they? Why aren't they talking about their... They must, they must feel like, "Well we just don't need to." >> We don't need to. We just don't need to. >> Absolutely. >> And then you walk around and you see these, these company names that are often anglicized, and you don't necessarily immediately associate them with China, but it's like, "Wait a minute, "that looks better than what I have, "and I'm not allowed to have access to that thing." What happens in the future there geopolitically? >> It's a pretty big question for- >> Its is. >> For a short little tech show. (Caroline laughs) But what happens as we move forward? When is the entire world going to be able to leverage in a secure way, some of the stuff that's coming out of, if they're not the largest economy in the world yet, they shortly will be. >> What's the story there? >> Well, it's interesting that you mentioned First Apple that has never had a presence at Mobile World Congress. And fun enough, I'm part of the GSMA judges for the GLOMO Awards, and last night I gave out Best Mobile Phone for last year, and it was to the iPhone4 Team Pro. and best disruptive technology, which was for the satellite function feature on, on the new iPhone. So, Apple might not be here, but they are. >> Okay. >> And, and so that's the first thing. And they are as far as being top of mind to every competitor in the smartphone market still. So a lot of the things that, even from a design perspective that you see on some of the Chinese brands, really remind you of, of Apple. What is interesting for me, is how there wouldn't be, with the exception of Samsung and Motorola, there's no one else here that is non-Chinese from a smartphone point of view. So that's in itself, is something that changed dramatically over the years, especially for somebody like me that still remember Nokia being the number one in the market. >> Huh. >> So. >> Guys, we could continue this conversation. We are unfortunately out of time. But thank you so much for joining Dave and me, talking about your perspectives on the event, the industry, the disruptive forces. It's going to be really interesting to see where it goes. 'Cause at the end of the day, it's the consumers that just want to make sure I can connect wherever I am 24 by seven, and it just needs to work. Thank you so much for your insights. >> Thank you. >> Lisa, it's been great. Dave, great. It's a pleasure. >> Our pleasure. For our guests, and for Dave Nicholson, I'm Lisa Martin. You're watching, "theCUBE," the leader in live and emerging tech coverage coming to you day three of our coverage of MWC 23. Stick around. Our next guest joins us momentarily. (outro music)

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music intro) (logo background tingles) >> Hi everybody, welcome back to day three of MWC23, my name is Dave Vellante and we're here live at the Theater of Barcelona, Lisa Martin, David Nicholson, John Furrier's in our studio in Palo Alto. Lot of buzz at the show, the Mobile World Daily Today, front page, Netflix chief hits back in fair share row, Greg Peters, the co-CEO of Netflix, talking about how, "Hey, you guys want to tax us, the telcos want to tax us, well, maybe you should help us pay for some of the content. Your margins are higher, you have a monopoly, you know, we're delivering all this value, you're bundling Netflix in, from a lot of ISPs so hold on, you know, pump the brakes on that tax," so that's the big news. Lockheed Martin, FOSS issues, AI guidelines, says, "AI's not going to take over your job anytime soon." Although I would say, your job's going to be AI-powered for the next five years. We're going to talk about data, we've been talking about the disaggregation of the telco stack, part of that stack is a data layer. John Kreisa is here, the CMO of Couchbase, John, you know, we've talked about all week, the disaggregation of the telco stacks, they got, you know, Silicon and operating systems that are, you know, real time OS, highly reliable, you know, compute infrastructure all the way up through a telemetry stack, et cetera. And that's a proprietary block that's really exploding, it's like the big bang, like we saw in the enterprise 20 years ago and we haven't had much discussion about that data layer, sort of that horizontal data layer, that's the market you play in. You know, Couchbase obviously has a lot of telco customers- >> John: That's right. >> We've seen, you know, Snowflake and others launch telco businesses. What are you seeing when you talk to customers at the show? What are they doing with that data layer? >> Yeah, so they're building applications to drive and power unique experiences for their users, but of course, it all starts with where the data is. So they're building mobile applications where they're stretching it out to the edge and you have to move the data to the edge, you have to have that capability to deliver that highly interactive experience to their customers or for their own internal use cases out to that edge, so seeing a lot of that with Couchbase and with our customers in telco. >> So what do the telcos want to do with data? I mean, they've got the telemetry data- >> John: Yeah. >> Now they frequently complain about the over-the-top providers that have used that data, again like Netflix, to identify customer demand for content and they're mopping that up in a big way, you know, certainly Amazon and shopping Google and ads, you know, they're all using that network. But what do the telcos do today and what do they want to do in the future? They're all talking about monetization, how do they monetize that data? >> Yeah, well, by taking that data, there's insight to be had, right? So by usage patterns and what's happening, just as you said, so they can deliver a better experience. It's all about getting that edge, if you will, on their competition and so taking that data, using it in a smart way, gives them that edge to deliver a better service and then grow their business. >> We're seeing a lot of action at the edge and, you know, the edge can be a Home Depot or a Lowe's store, but it also could be the far edge, could be a, you know, an oil drilling, an oil rig, it could be a racetrack, you know, certainly hospitals and certain, you know, situations. So let's think about that edge, where there's maybe not a lot of connectivity, there might be private networks going in, in the future- >> John: That's right. >> Private 5G networks. What's the data flow look like there? Do you guys have any customers doing those types of use cases? >> Yeah, absolutely. >> And what are they doing with the data? >> Yeah, absolutely, we've got customers all across, so telco and transportation, all kinds of service delivery and healthcare, for example, we've got customers who are delivering healthcare out at the edge where they have a remote location, they're able to deliver healthcare, but as you said, there's not always connectivity, so they need to have the applications, need to continue to run and then sync back once they have that connectivity. So it's really having the ability to deliver a service, reliably and then know that that will be synced back to some central server when they have connectivity- >> So the processing might occur where the data- >> Compute at the edge. >> How do you sync back? What is that technology? >> Yeah, so there's, so within, so Couchbase and Couchbase's case, we have an autonomous sync capability that brings it back to the cloud once they get back to whether it's a private network that they want to run over, or if they're doing it over a public, you know, wifi network, once it determines that there's connectivity and, it can be peer-to-peer sync, so different edge apps communicating with each other and then ultimately communicating back to a central server. >> I mean, the other theme here, of course, I call it the software-defined telco, right? But you got to have, you got to run on something, got to have hardware. So you see companies like AWS putting Outposts, out to the edge, Outposts, you know, doesn't really run a lot of database to mind, I mean, it runs RDS, you know, maybe they're going to eventually work with companies like... I mean, you're a partner of AWS- >> John: We are. >> Right? So do you see that kind of cloud infrastructure that's moving to the edge? Do you see that as an opportunity for companies like Couchbase? >> Yeah, we do. We see customers wanting to push more and more of that compute out to the edge and so partnering with AWS gives us that opportunity and we are certified on Outpost and- >> Oh, you are? >> We are, yeah. >> Okay. >> Absolutely. >> When did that, go down? >> That was last year, but probably early last year- >> So I can run Couchbase at the edge, on Outpost? >> Yeah, that's right. >> I mean, you know, Outpost adoption has been slow, we've reported on that, but are you seeing any traction there? Are you seeing any nibbles? >> Starting to see some interest, yeah, absolutely. And again, it has to be for the right use case, but again, for service delivery, things like healthcare and in transportation, you know, they're starting to see where they want to have that compute, be very close to where the actions happen. >> And you can run on, in the data center, right? >> That's right. >> You can run in the cloud, you know, you see HPE with GreenLake, you see Dell with Apex, that's essentially their Outposts. >> Yeah. >> They're saying, "Hey, we're going to take our whole infrastructure and make it as a service." >> Yeah, yeah. >> Right? And so you can participate in those environments- >> We do. >> And then so you've got now, you know, we call it supercloud, you've got the on-prem, you've got the, you can run in the public cloud, you can run at the edge and you want that consistent experience- >> That's right. >> You know, from a data layer- >> That's right. >> So is that really the strategy for a data company is taking or should be taking, that horizontal layer across all those use cases? >> You do need to think holistically about it, because you need to be able to deliver as a, you know, as a provider, wherever the customer wants to be able to consume that application. So you do have to think about any of the public clouds or private networks and all the way to the edge. >> What's different John, about the telco business versus the traditional enterprise? >> Well, I mean, there's scale, I mean, one thing they're dealing with, particularly for end user-facing apps, you're dealing at a very very high scale and the expectation that you're going to deliver a very interactive experience. So I'd say one thing in particular that we are focusing on, is making sure we deliver that highly interactive experience but it's the scale of the number of users and customers that they have, and the expectation that your application's always going to work. >> Speaking of applications, I mean, it seems like that's where the innovation is going to come from. We saw yesterday, GSMA announced, I think eight APIs telco APIs, you know, we were talking on theCUBE, one of the analysts was like, "Eight, that's nothing," you know, "What do these guys know about developers?" But you know, as Daniel Royston said, "Eight's better than zero." >> Right? >> So okay, so we're starting there, but the point being, it's all about the apps, that's where the innovation's going to come from- >> That's right. >> So what are you seeing there, in terms of building on top of the data app? >> Right, well you have to provide, I mean, have to provide the APIs and the access because it is really, the rubber meets the road, with the developers and giving them the ability to create those really rich applications where they want and create the experiences and innovate and change the way that they're giving those experiences. >> Yeah, so what's your relationship with developers at Couchbase? >> John: Yeah. >> I mean, talk about that a little bit- >> Yeah, yeah, so we have a great relationship with developers, something we've been investing more and more in, in terms of things like developer relations teams and community, Couchbase started in open source, continue to be based on open source projects and of course, those are very developer centric. So we provide all the consistent APIs for developers to create those applications, whether it's something on Couchbase Lite, which is our kind of edge-based database, or how they can sync that data back and we actually automate a lot of that syncing which is a very difficult developer task which lends them to one of the developer- >> What I'm trying to figure out is, what's the telco developer look like? Is that a developer that comes from the enterprise and somebody comes from the blockchain world, or AI or, you know, there really doesn't seem to be a lot of developer talk here, but there's a huge opportunity. >> Yeah, yeah. >> And, you know, I feel like, the telcos kind of remind me of, you know, a traditional legacy company trying to get into the developer world, you know, even Oracle, okay, they bought Sun, they got Java, so I guess they have developers, but you know, IBM for years tried with Bluemix, they had to end up buying Red Hat, really, and that gave them the developer community. >> Yep. >> EMC used to have a thing called EMC Code, which was a, you know, good effort, but eh. And then, you know, VMware always trying to do that, but, so as you move up the stack obviously, you have greater developer affinity. Where do you think the telco developer's going to come from? How's that going to evolve? >> Yeah, it's interesting, and I think they're... To kind of get to your first question, I think they're fairly traditional enterprise developers and when we break that down, we look at it in terms of what the developer persona is, are they a front-end developer? Like they're writing that front-end app, they don't care so much about the infrastructure behind or are they a full stack developer and they're really involved in the entire application development lifecycle? Or are they living at the backend and they're really wanting to just focus in on that data layer? So we lend towards all of those different personas and we think about them in terms of the APIs that we create, so that's really what the developers are for telcos is, there's a combination of those front-end and full stack developers and so for them to continue to innovate they need to appeal to those developers and that's technology, like Couchbase, is what helps them do that. >> Yeah and you think about the Apples, you know, the app store model or Apple sort of says, "Okay, here's a developer kit, go create." >> John: Yeah. >> "And then if it's successful, you're going to be successful and we're going to take a vig," okay, good model. >> John: Yeah. >> I think I'm hearing, and maybe I misunderstood this, but I think it was the CEO or chairman of Ericsson on the day one keynotes, was saying, "We are going to monetize the, essentially the telemetry data, you know, through APIs, we're going to charge for that," you know, maybe that's not the best approach, I don't know, I think there's got to be some innovation on top. >> John: Yeah. >> Now maybe some of these greenfield telcos are going to do like, you take like a dish networks, what they're doing, they're really trying to drive development layers. So I think it's like this wild west open, you know, community that's got to be formed and right now it's very unclear to me, do you have any insights there? >> I think it is more, like you said, Wild West, I think there's no emerging standard per se for across those different company types and sort of different pieces of the industry. So consequently, it does need to form some more standards in order to really help it grow and I think you're right, you have to have the right APIs and the right access in order to properly monetize, you have to attract those developers or you're not going to be able to monetize properly. >> Do you think that if, in thinking about your business and you know, you've always sold to telcos, but now it's like there's this transformation going on in telcos, will that become an increasingly larger piece of your business or maybe even a more important piece of your business? Or it's kind of be steady state because it's such a slow moving industry? >> No, it is a big and increasing piece of our business, I think telcos like other enterprises, want to continue to innovate and so they look to, you know, technologies like, Couchbase document database that allows them to have more flexibility and deliver the speed that they need to deliver those kinds of applications. So we see a lot of migration off of traditional legacy infrastructure in order to build that new age interface and new age experience that they want to deliver. >> A lot of buzz in Silicon Valley about open AI and Chat GPT- >> Yeah. >> You know, what's your take on all that? >> Yeah, we're looking at it, I think it's exciting technology, I think there's a lot of applications that are kind of, a little, sort of innovate traditional interfaces, so for example, you can train Chat GPT to create code, sample code for Couchbase, right? You can go and get it to give you that sample app which gets you a headstart or you can actually get it to do a better job of, you know, sorting through your documentation, like Chat GPT can do a better job of helping you get access. So it improves the experience overall for developers, so we're excited about, you know, what the prospect of that is. >> So you're playing around with it, like everybody is- >> Yeah. >> And potentially- >> Looking at use cases- >> Ways tO integrate, yeah. >> Hundred percent. >> So are we. John, thanks for coming on theCUBE. Always great to see you, my friend. >> Great, thanks very much. >> All right, you're welcome. All right, keep it right there, theCUBE will be back live from Barcelona at the theater. SiliconANGLE's continuous coverage of MWC23. Go to siliconangle.com for all the news, theCUBE.net is where all the videos are, keep it right there. (cheerful upbeat music outro)