(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI Machine Learning Top Startups Building Generative AI on AWS. This is season 3, episode 1 of the ongoing series covering the exciting startup from the AWS ecosystem to talk about AI and machine learning. I'm your host, John Furrier. I'm joined by two great guests here, Adam Wenchel, who's the CEO of Arthur, and Chief Scientist of Arthur, John Dickerson. Talk about how they help people build better LLM AI systems to get them into the market faster. Gentlemen, thank you for coming on. >> Yeah, thanks for having us, John. >> Well, I got to say I got to temper my enthusiasm because the last few months explosion of interest in LLMs with ChatGPT, has opened the eyes to everybody around the reality of that this is going next gen, this is it, this is the moment, this is the the point we're going to look back and say, this is the time where AI really hit the scene for real applications. So, a lot of Large Language Models, also known as LLMs, foundational models, and generative AI is all booming. This is where all the alpha developers are going. This is where everyone's focusing their business model transformations on. This is where developers are seeing action. So it's all happening, the wave is here. So I got to ask you guys, what are you guys seeing right now? You're in the middle of it, it's hitting you guys right on. You're in the front end of this massive wave. >> Yeah, John, I don't think you have to temper your enthusiasm at all. I mean, what we're seeing every single day is, everything from existing enterprise customers coming in with new ways that they're rethinking, like business things that they've been doing for many years that they can now do an entirely different way, as well as all manner of new companies popping up, applying LLMs to everything from generating code and SQL statements to generating health transcripts and just legal briefs. Everything you can imagine. And when you actually sit down and look at these systems and the demos we get of them, the hype is definitely justified. It's pretty amazing what they're going to do. And even just internally, we built, about a month ago in January, we built an Arthur chatbot so customers could ask questions, technical questions from our, rather than read our product documentation, they could just ask this LLM a particular question and get an answer. And at the time it was like state of the art, but then just last week we decided to rebuild it because the tooling has changed so much that we, last week, we've completely rebuilt it. It's now way better, built on an entirely different stack. And the tooling has undergone a full generation worth of change in six weeks, which is crazy. So it just tells you how much energy is going into this and how fast it's evolving right now. >> John, weigh in as a chief scientist. I mean, you must be blown away. Talk about kid in the candy store. I mean, you must be looking like this saying, I mean, she must be super busy to begin with, but the change, the acceleration, can you scope the kind of change you're seeing and be specific around the areas you're seeing movement and highly accelerated change? >> Yeah, definitely. And it is very, very exciting actually, thinking back to when ChatGPT was announced, that was a night our company was throwing an event at NeurIPS, which is maybe the biggest machine learning conference out there. And the hype when that happened was palatable and it was just shocking to see how well that performed. And then obviously over the last few months since then, as LLMs have continued to enter the market, we've seen use cases for them, like Adam mentioned all over the place. And so, some things I'm excited about in this space are the use of LLMs and more generally, foundation models to redesign traditional operations, research style problems, logistics problems, like auctions, decisioning problems. So moving beyond the already amazing news cases, like creating marketing content into more core integration and a lot of the bread and butter companies and tasks that drive the American ecosystem. And I think we're just starting to see some of that. And in the next 12 months, I think we're going to see a lot more. If I had to make other predictions, I think we're going to continue seeing a lot of work being done on managing like inference time costs via shrinking models or distillation. And I don't know how to make this prediction, but at some point we're going to be seeing lots of these very large scale models operating on the edge as well. So the time scales are extremely compressed, like Adam mentioned, 12 months from now, hard to say. >> We were talking on theCUBE prior to this session here. We had theCUBE conversation here and then the Wall Street Journal just picked up on the same theme, which is the printing press moment created the enlightenment stage of the history. Here we're in the whole nother automating intellect efficiency, doing heavy lifting, the creative class coming back, a whole nother level of reality around the corner that's being hyped up. The question is, is this justified? Is there really a breakthrough here or is this just another result of continued progress with AI? Can you guys weigh in, because there's two schools of thought. There's the, "Oh my God, we're entering a new enlightenment tech phase, of the equivalent of the printing press in all areas. Then there's, Ah, it's just AI (indistinct) inch by inch. What's your guys' opinion? >> Yeah, I think on the one hand when you're down in the weeds of building AI systems all day, every day, like we are, it's easy to look at this as an incremental progress. Like we have customers who've been building on foundation models since we started the company four years ago, particular in computer vision for classification tasks, starting with pre-trained models, things like that. So that part of it doesn't feel real new, but what does feel new is just when you apply these things to language with all the breakthroughs and computational efficiency, algorithmic improvements, things like that, when you actually sit down and interact with ChatGPT or one of the other systems that's out there that's building on top of LLMs, it really is breathtaking, like, the level of understanding that they have and how quickly you can accelerate your development efforts and get an actual working system in place that solves a really important real world problem and makes people way faster, way more efficient. So I do think there's definitely something there. It's more than just incremental improvement. This feels like a real trajectory inflection point for the adoption of AI. >> John, what's your take on this? As people come into the field, I'm seeing a lot of people move from, hey, I've been coding in Python, I've been doing some development, I've been a software engineer, I'm a computer science student. I'm coding in C++ old school, OG systems person. Where do they come in? Where's the focus, where's the action? Where are the breakthroughs? Where are people jumping in and rolling up their sleeves and getting dirty with this stuff? >> Yeah, all over the place. And it's funny you mentioned students in a different life. I wore a university professor hat and so I'm very, very familiar with the teaching aspects of this. And I will say toward Adam's point, this really is a leap forward in that techniques like in a co-pilot for example, everybody's using them right now and they really do accelerate the way that we develop. When I think about the areas where people are really, really focusing right now, tooling is certainly one of them. Like you and I were chatting about LangChain right before this interview started, two or three people can sit down and create an amazing set of pipes that connect different aspects of the LLM ecosystem. Two, I would say is in engineering. So like distributed training might be one, or just understanding better ways to even be able to train large models, understanding better ways to then distill them or run them. So like this heavy interaction now between engineering and what I might call traditional machine learning from 10 years ago where you had to know a lot of math, you had to know calculus very well, things like that. Now you also need to be, again, a very strong engineer, which is exciting. >> I interviewed Swami when he talked about the news. He's ahead of Amazon's machine learning and AI when they announced Hugging Face announcement. And I reminded him how Amazon was easy to get into if you were developing a startup back in 2007,8, and that the language models had that similar problem. It's step up a lot of content and a lot of expense to get provisioned up, now it's easy. So this is the next wave of innovation. So how do you guys see that from where we are right now? Are we at that point where it's that moment where it's that cloud-like experience for LLMs and large language models? >> Yeah, go ahead John. >> I think the answer is yes. We see a number of large companies that are training these and serving these, some of which are being co-interviewed in this episode. I think we're at that. Like, you can hit one of these with a simple, single line of Python, hitting an API, you can boot this up in seconds if you want. It's easy. >> Got it. >> So I (audio cuts out). >> Well let's take a step back and talk about the company. You guys being featured here on the Showcase. Arthur, what drove you to start the company? How'd this all come together? What's the origination story? Obviously you got a big customers, how'd get started? What are you guys doing? How do you make money? Give a quick overview. >> Yeah, I think John and I come at it from slightly different angles, but for myself, I have been a part of a number of technology companies. I joined Capital One, they acquired my last company and shortly after I joined, they asked me to start their AI team. And so even though I've been doing AI for a long time, I started my career back in DARPA. It was the first time I was really working at scale in AI at an organization where there were hundreds of millions of dollars in revenue at stake with the operation of these models and that they were impacting millions of people's financial livelihoods. And so it just got me hyper-focused on these issues around making sure that your AI worked well and it worked well for your company and it worked well for the people who were being affected by it. At the time when I was doing this 2016, 2017, 2018, there just wasn't any tooling out there to support this production management model monitoring life phase of the life cycle. And so we basically left to start the company that I wanted. And John has a his own story. I'll let let you share that one, John. >> Go ahead John, you're up. >> Yeah, so I'm coming at this from a different world. So I'm on leave now from a tenured role in academia where I was leading a large lab focusing on the intersection of machine learning and economics. And so questions like fairness or the response to the dynamism on the underlying environment have been around for quite a long time in that space. And so I've been thinking very deeply about some of those more like R and D style questions as well as having deployed some automation code across a couple of different industries, some in online advertising, some in the healthcare space and so on, where concerns of, again, fairness come to bear. And so Adam and I connected to understand the space of what that might look like in the 2018 20 19 realm from a quantitative and from a human-centered point of view. And so booted things up from there. >> Yeah, bring that applied engineering R and D into the Capital One, DNA that he had at scale. I could see that fit. I got to ask you now, next step, as you guys move out and think about LLMs and the recent AI news around the generative models and the foundational models like ChatGPT, how should we be looking at that news and everyone watching might be thinking the same thing. I know at the board level companies like, we should refactor our business, this is the future. It's that kind of moment, and the tech team's like, okay, boss, how do we do this again? Or are they prepared? How should we be thinking? How should people watching be thinking about LLMs? >> Yeah, I think they really are transformative. And so, I mean, we're seeing companies all over the place. Everything from large tech companies to a lot of our large enterprise customers are launching significant projects at core parts of their business. And so, yeah, I would be surprised, if you're serious about becoming an AI native company, which most leading companies are, then this is a trend that you need to be taking seriously. And we're seeing the adoption rate. It's funny, I would say the AI adoption in the broader business world really started, let's call it four or five years ago, and it was a relatively slow adoption rate, but I think all that kind of investment in and scaling the maturity curve has paid off because the rate at which people are adopting and deploying systems based on this is tremendous. I mean, this has all just happened in the few months and we're already seeing people get systems into production. So, now there's a lot of things you have to guarantee in order to put these in production in a way that basically is added into your business and doesn't cause more headaches than it solves. And so that's where we help customers is where how do you put these out there in a way that they're going to represent your company well, they're going to perform well, they're going to do their job and do it properly. >> So in the use case, as a customer, as I think about this, there's workflows. They might have had an ML AI ops team that's around IT. Their inference engines are out there. They probably don't have a visibility on say how much it costs, they're kicking the tires. When you look at the deployment, there's a cost piece, there's a workflow piece, there's fairness you mentioned John, what should be, I should be thinking about if I'm going to be deploying stuff into production, I got to think about those things. What's your opinion? >> Yeah, I'm happy to dive in on that one. So monitoring in general is extremely important once you have one of these LLMs in production, and there have been some changes versus traditional monitoring that we can dive deeper into that LLMs are really accelerated. But a lot of that bread and butter style of things you should be looking out for remain just as important as they are for what you might call traditional machine learning models. So the underlying environment of data streams, the way users interact with these models, these are all changing over time. And so any performance metrics that you care about, traditional ones like an accuracy, if you can define that for an LLM, ones around, for example, fairness or bias. If that is a concern for your particular use case and so on. Those need to be tracked. Now there are some interesting changes that LLMs are bringing along as well. So most ML models in production that we see are relatively static in the sense that they're not getting flipped in more than maybe once a day or once a week or they're just set once and then not changed ever again. With LLMs, there's this ongoing value alignment or collection of preferences from users that is often constantly updating the model. And so that opens up all sorts of vectors for, I won't say attack, but for problems to arise in production. Like users might learn to use your system in a different way and thus change the way those preferences are getting collected and thus change your system in ways that you never intended. So maybe that went through governance already internally at the company and now it's totally, totally changed and it's through no fault of your own, but you need to be watching over that for sure. >> Talk about the reinforced learnings from human feedback. How's that factoring in to the LLMs? Is that part of it? Should people be thinking about that? Is that a component that's important? >> It certainly is, yeah. So this is one of the big tweaks that happened with InstructGPT, which is the basis model behind ChatGPT and has since gone on to be used all over the place. So value alignment I think is through RLHF like you mentioned is a very interesting space to get into and it's one that you need to watch over. Like, you're asking humans for feedback over outputs from a model and then you're updating the model with respect to that human feedback. And now you've thrown humans into the loop here in a way that is just going to complicate things. And it certainly helps in many ways. You can ask humans to, let's say that you're deploying an internal chat bot at an enterprise, you could ask humans to align that LLM behind the chatbot to, say company values. And so you're listening feedback about these company values and that's going to scoot that chatbot that you're running internally more toward the kind of language that you'd like to use internally on like a Slack channel or something like that. Watching over that model I think in that specific case, that's a compliance and HR issue as well. So while it is part of the greater LLM stack, you can also view that as an independent bit to watch over. >> Got it, and these are important factors. When people see the Bing news, they freak out how it's doing great. Then it goes off the rails, it goes big, fails big. (laughing) So these models people see that, is that human interaction or is that feedback, is that not accepting it or how do people understand how to take that input in and how to build the right apps around LLMs? This is a tough question. >> Yeah, for sure. So some of the examples that you'll see online where these chatbots go off the rails are obviously humans trying to break the system, but some of them clearly aren't. And that's because these are large statistical models and we don't know what's going to pop out of them all the time. And even if you're doing as much in-house testing at the big companies like the Go-HERE's and the OpenAI's of the world, to try to prevent things like toxicity or racism or other sorts of bad content that might lead to bad pr, you're never going to catch all of these possible holes in the model itself. And so, again, it's very, very important to keep watching over that while it's in production. >> On the business model side, how are you guys doing? What's the approach? How do you guys engage with customers? Take a minute to explain the customer engagement. What do they need? What do you need? How's that work? >> Yeah, I can talk a little bit about that. So it's really easy to get started. It's literally a matter of like just handing out an API key and people can get started. And so we also offer alternative, we also offer versions that can be installed on-prem for models that, we find a lot of our customers have models that deal with very sensitive data. So you can run it in your cloud account or use our cloud version. And so yeah, it's pretty easy to get started with this stuff. We find people start using it a lot of times during the validation phase 'cause that way they can start baselining performance models, they can do champion challenger, they can really kind of baseline the performance of, maybe they're considering different foundation models. And so it's a really helpful tool for understanding differences in the way these models perform. And then from there they can just flow that into their production inferencing, so that as these systems are out there, you have really kind of real time monitoring for anomalies and for all sorts of weird behaviors as well as that continuous feedback loop that helps you make make your product get better and observability and you can run all sorts of aggregated reports to really understand what's going on with these models when they're out there deciding. I should also add that we just today have another way to adopt Arthur and that is we are in the AWS marketplace, and so we are available there just to make it that much easier to use your cloud credits, skip the procurement process, and get up and running really quickly. >> And that's great 'cause Amazon's got SageMaker, which handles a lot of privacy stuff, all kinds of cool things, or you can get down and dirty. So I got to ask on the next one, production is a big deal, getting stuff into production. What have you guys learned that you could share to folks watching? Is there a cost issue? I got to monitor, obviously you brought that up, we talked about the even reinforcement issues, all these things are happening. What is the big learnings that you could share for people that are going to put these into production to watch out for, to plan for, or be prepared for, hope for the best plan for the worst? What's your advice? >> I can give a couple opinions there and I'm sure Adam has. Well, yeah, the big one from my side is, again, I had mentioned this earlier, it's just the input data streams because humans are also exploring how they can use these systems to begin with. It's really, really hard to predict the type of inputs you're going to be seeing in production. Especially, we always talk about chatbots, but then any generative text tasks like this, let's say you're taking in news articles and summarizing them or something like that, it's very hard to get a good sampling even of the set of news articles in such a way that you can really predict what's going to pop out of that model. So to me, it's, adversarial maybe isn't the word that I would use, but it's an unnatural shifting input distribution of like prompts that you might see for these models. That's certainly one. And then the second one that I would talk about is, it can be hard to understand the costs, the inference time costs behind these LLMs. So the pricing on these is always changing as the models change size, it might go up, it might go down based on model size, based on energy cost and so on, but your pricing per token or per a thousand tokens and that I think can be difficult for some clients to wrap their head around. Again, you don't know how these systems are going to be used after all so it can be tough. And so again that's another metric that really should be tracked. >> Yeah, and there's a lot of trade off choices in there with like, how many tokens do you want at each step and in the sequence and based on, you have (indistinct) and you reject these tokens and so based on how your system's operating, that can make the cost highly variable. And that's if you're using like an API version that you're paying per token. A lot of people also choose to run these internally and as John mentioned, the inference time on these is significantly higher than a traditional classifi, even NLP classification model or tabular data model, like orders of magnitude higher. And so you really need to understand how that, as you're constantly iterating on these models and putting out new versions and new features in these models, how that's affecting the overall scale of that inference cost because you can use a lot of computing power very quickly with these profits. >> Yeah, scale, performance, price all come together. I got to ask while we're here on the secret sauce of the company, if you had to describe to people out there watching, what's the secret sauce of the company? What's the key to your success? >> Yeah, so John leads our research team and they've had a number of really cool, I think AI as much as it's been hyped for a while, it's still commercial AI at least is really in its infancy. And so the way we're able to pioneer new ways to think about performance for computer vision NLP LLMs is probably the thing that I'm proudest about. John and his team publish papers all the time at Navs and other places. But I think it's really being able to define what performance means for basically any kind of model type and give people really powerful tools to understand that on an ongoing basis. >> John, secret sauce, how would you describe it? You got all the action happening all around you. >> Yeah, well I going to appreciate Adam talking me up like that. No, I. (all laughing) >> Furrier: Robs to you. >> I would also say a couple of other things here. So we have a very strong engineering team and so I think some early hires there really set the standard at a very high bar that we've maintained as we've grown. And I think that's really paid dividends as scalabilities become even more of a challenge in these spaces, right? And so that's not just scalability when it comes to LLMs, that's scalability when it comes to millions of inferences per day, that kind of thing as well in traditional ML models. And I think that's compared to potential competitors, that's really... Well, it's made us able to just operate more efficiently and pass that along to the client. >> Yeah, and I think the infancy comment is really important because it's the beginning. You really is a long journey ahead. A lot of change coming, like I said, it's a huge wave. So I'm sure you guys got a lot of plannings at the foundation even for your own company, so I appreciate the candid response there. Final question for you guys is, what should the top things be for a company in 2023? If I'm going to set the agenda and I'm a customer moving forward, putting the pedal to the metal, so to speak, what are the top things I should be prioritizing or I need to do to be successful with AI in 2023? >> Yeah, I think, so number one, as we talked about, we've been talking about this entire episode, the things are changing so quickly and the opportunities for business transformation and really disrupting different applications, different use cases, is almost, I don't think we've even fully comprehended how big it is. And so really digging in to your business and understanding where I can apply these new sets of foundation models is, that's a top priority. The interesting thing is I think there's another force at play, which is the macroeconomic conditions and a lot of places are, they're having to work harder to justify budgets. So in the past, couple years ago maybe, they had a blank check to spend on AI and AI development at a lot of large enterprises that was limited primarily by the amount of talent they could scoop up. Nowadays these expenditures are getting scrutinized more. And so one of the things that we really help our customers with is like really calculating the ROI on these things. And so if you have models out there performing and you have a new version that you can put out that lifts the performance by 3%, how many tens of millions of dollars does that mean in business benefit? Or if I want to go to get approval from the CFO to spend a few million dollars on this new project, how can I bake in from the beginning the tools to really show the ROI along the way? Because I think in these systems when done well for a software project, the ROI can be like pretty spectacular. Like we see over a hundred percent ROI in the first year on some of these projects. And so, I think in 2023, you just need to be able to show what you're getting for that spend. >> It's a needle moving moment. You see it all the time with some of these aha moments or like, whoa, blown away. John, I want to get your thoughts on this because one of the things that comes up a lot for companies that I talked to, that are on my second wave, I would say coming in, maybe not, maybe the front wave of adopters is talent and team building. You mentioned some of the hires you got were game changing for you guys and set the bar high. As you move the needle, new developers going to need to come in. What's your advice given that you've been a professor, you've seen students, I know a lot of computer science people want to shift, they might not be yet skilled in AI, but they're proficient in programming, is that's going to be another opportunity with open source when things are happening. How do you talk to that next level of talent that wants to come in to this market to supplement teams and be on teams, lead teams? Any advice you have for people who want to build their teams and people who are out there and want to be a coder in AI? >> Yeah, I've advice, and this actually works for what it would take to be a successful AI company in 2023 as well, which is, just don't be afraid to iterate really quickly with these tools. The space is still being explored on what they can be used for. A lot of the tasks that they're used for now right? like creating marketing content using a machine learning is not a new thing to do. It just works really well now. And so I'm excited to see what the next year brings in terms of folks from outside of core computer science who are, other engineers or physicists or chemists or whatever who are learning how to use these increasingly easy to use tools to leverage LLMs for tasks that I think none of us have really thought about before. So that's really, really exciting. And so toward that I would say iterate quickly. Build things on your own, build demos, show them the friends, host them online and you'll learn along the way and you'll have somebody to show for it. And also you'll help us explore that space. >> Guys, congratulations with Arthur. Great company, great picks and shovels opportunities out there for everybody. Iterate fast, get in quickly and don't be afraid to iterate. Great advice and thank you for coming on and being part of the AWS showcase, thanks. >> Yeah, thanks for having us on John. Always a pleasure. >> Yeah, great stuff. Adam Wenchel, John Dickerson with Arthur. Thanks for coming on theCUBE. I'm John Furrier, your host. Generative AI and AWS. Keep it right there for more action with theCUBE. Thanks for watching. (upbeat music)

(bright orchestral music) >> Hello everyone, and welcome to theCUBE Conversations here in Palo Alto Studios. For theCUBE, I'm John Furrier, the co-founder of SiliconANGLE Media inc. My next guest is Bruce Arthur, who's the Vice President of engineering at Banter.ai. Good friend, we've known each other for years, VP of engineering, developer, formerly at Apple. >> Yes. >> Worked on all the big products; the iPad-- had the the tin foil on your windows back in the day during Steve Jobs' awesome run there. Welcome to theCUBE. >> Thank you, it's good to be here. >> Yeah, great, you've got a ton of experience and I want to get your perspective as a developer, VP of engineering, entrepreneur, you're doing a startup around AI. Let's have a little banter. >> Sure. >> Banter.ai is a little bit a chat bot, but the rage is DevOps. Software really models change, infrastructure as code, cloud computing. Really a renaissance of software development going on right now. >> It is, it's changing a lot. >> What's your view on this? >> Well, so, years and years ago you would work really hard on your software. You would package it up in a box and you'd send it over the wall and you hope it works. And that seems very quaint now because now you write your software, you deploy it the first day, and you change it six times that day, and you're A/B-testing it, you're driving it forward, it's so much more interactive. It does require a different skillset. It also doesn't, how do I say this carefully? It used to be very easy to be craft, to have high craft and make a very polished product, but you didn't know if it was going to work. Today you know if it's going to work, but you often don't get to making sure it's high quality, high craft, high value. >> John: So, the iteration >> Exactly, the iteration runs so fast, which is highly valuable, but you sort of just a little bit of you miss the is this really something I am proud of and I can really work with it because you know, now the product definition can change so quickly, which is awesome but it is a big change. >> And that artisan crafting thing is interesting, but now some are saying that the UX side is interesting because, if you get the back end working, and you're iterating, you can still bring that artisan flavor back. We heard that cloud computing vendors like Amazon, and I was just in China for Alibaba, they're trying to bring this whole design artisan culture back. Your thoughts on the whole artisan craft in software, because now you have two stages, you have deploy, iterate, and then ultimately polish. >> Right, so, I think it's interesting, it used to be, engineering is so expensive and time-consuming. You have to design it upfront and you make one version of it and you're done. That has changed now that engineering has gotten easier. You have better tools, we have better things, you can make six versions and that used to be, so back in the day at Apple, you would make six versions, five of which Steve would hate and throw out, and eventually they would get better and better and better and then you would have something you're proud of. Now those are just exposed. Now everybody sees those, it's a very different process. So you, I think, the idea that you. Engineering used to be this scarce resource. It's becoming easier now to have many versions and have more engineers working on stuff, so now it is much more can I have three design teams, can they compete, can they make all good ideas, and then who's going to be the editor? Who evaluates them and decides I like this from this one, I like that, and now let's put this together to make the right product. >> So, at Apple, you mentioned Steve would reject, well, that's well-documented. >> Sure. >> It's publicly out there that he would like, really look at the design-side. Was it Waterfall-based, was it Agile, Scrum, did you guys, was it like, do you lay it all out in front of him and he points at it? What were some of the work flows like with Steve Jobs? >> So, when he was really excited about something he would want to meet with them every week. He'd want to see progress every week. He'd give lots of feedback every week, there'd be new ideas. It was very Steve-focused. I think the more constructive side of it was the design teams were always thinking about What can we build, how do we put it in front of him, and I remember there was a great quote from a designer that said. It's not that Steve designs great things, it's that you show him three things, and if you throw him three bad things, he'll pick the least bad. If you show him three great things, he'll pick the most great, But it's not, it was more about the, you've got to iterate in the process, you've got to try ideas, you take ideas from different people and some of them, like, they sound like a great idea. When we talk, it sounds really good. You build it, and you're like, that's just not, that's just not right. So, you want, how do I say this? You don't want to lock yourself in up front. You want to imagine them, you want to build them, you want to try 'em. >> And that's, I mean, I've gotten to know the family over the years, too, through some of the Palo Alto interactions, and that's the kind of misperception of Steve Jobs, was that he was the guy. He enabled people, he had that ethos that-- >> He was the editor, it's an old school journalism metaphor, which is, he had ideas, he wanted, but he also, he ran the team. He wanted to have people bring their ideas and come in. And then he decided, this is good, this is not. That's better, you can do better, let's try this. Or, sometimes, this whole thing stinks. It's just not going anywhere. So, like, it was much more of that. Now it's applied to software, and he was a marketing genius, about sort of knowing what people were going to go for, but there was a little bit of a myth for it, that there's one man designing everything. That is a very saleable marketing story. >> The mythical man. (laughs) >> Well, it's powerful, but no, there's a lot of people, and getting the best work of all those people. >> I mean, he's said on some of the great videos I've watched on YouTube over the years, Hire the best people, only work with the best, and they'll bring good stuff to the table. Now, I want to bring that kind of metaphor, one step further for this great learning lesson, again it's all well-documented on YouTube. Plenty of Steve videos there, but now when you go to DevOps, you mention the whole quality thing and you got to ship fast, iterate, you know there's a lot of moving fast break stuff as Zuckerberg would say, of Facebook, although he's edited his tune to say move fast and be reliable. (laughing) Welcome to the enterprise, welcome to software and operations. This is now a scale game at the enterprise side 'cause, you know, you start seeing open source software grow so much now, where a lot of the intellectual property might be only 10% of software. >> Right. >> You might be using other pieces. You're packaging it so that when you get it to the market, how do bring that culture? How do you get that innovation of, Okay, I'm iterating fast, how do I maintain the quality. What are some of your thoughts on that? Because you've got machine learning out there, you've got these cool things happening. >> Yup. So, you want, how do I say this? You just, you really need to leave time to schedule it. It needs to be in your list. There's a lot of figuring out what are we going to build and you have to try things, iterate things, see if they resonate with consumers. See if they resonate with people who want to pay. See if they resonate with investors. You have to figure than out fast, but then you have to know that, okay, this is a good prototype. Now I have to make it work better because the first version wouldn't scale well, now it has to scale, now it has to work right for people, now you have to have a review of: here's the bugs, here's the things that are not working. Why does this chatbot stop responding sometimes? What is causing that? Now, the great story is, with good DevOps, you actually have a system that's very good at finding and tracking those problems. In the old world, so the old world with the shrink-wrap software, you'd throw it over the fence. If it misbehaves, you will never know. Today you know. You've got alerts, you've got pagers going off, you've got logs, >> It's instrumented big-time. >> Yeah, exactly, you can find that stuff. So, since you can actually make, you can make very high-quality software because you have so much more data about what's going on with it, it's nice. And actually, chatbot software has this fascinating little side effect, with, because it's all chats and it's all text, there are no irreproducible bugs. You can go back and look at exactly what happened. I have a recording, I know exactly what happened, I know exactly what came in, I know what came out, and then I know that this failure happened. So, it's very reproducible, sort of, it's nice you can, it doesn't always work this way, but it's very easy to track down problems. >> It's event-based, it's really easy to manage. >> Exactly, and it's just text. You can just read it. It's not like I have to debug hacks, it's just these things were said and this thing died. >> No core dumps. (laughs) >> No, there's nothing that requires sophisticated analysis, well the code is one thing, but like, the sequence of events is very human-readable, very understandable. >> Alright, so let's talk about the younger generation. So, we've been around the block, you and I. We've talked, certainly many times around town, about the shifts, and we love these new waves. A lot of great waves coming in, we've seen many waves. What's going on, in your mind, with the younger generation? Because this is a, some exciting things happening. Decentralized internet. >> Bruce: Yup. >> There's blockchain, getting all the attention. Outside of the hype, Alpha VCs, Alpha engineers, Alpha entrepreneurs are really honing in on blockchain because they see the potential. >> Sure. >> Early people are seeing it. Then you've got cloud, obviously unlimited compute potentially, the new, you know, kind of agile market. All these young guys, they never shipped, actually never loaded Linux on a server. (laughing) So, like, what are you seeing for the younger guys? And what do you see as someone who's experienced, looking down at the next, you know, 20 year run we see. >> So, I think what I see that's most exciting is that we now have people solving very non-technical problems with technology. I think it used to be, you could build a computer, you could write code, but then, like, your space was limited to the computer in front of you. Like, I can do input and outputs. I can put things on the screen, I can make a video game, but it's in this box. Now everyone's thinking of much bigger, Solving bigger problems. >> John: Yeah, healthcare, we're seeing verticals. >> Yeah, healthcare's a massive one. You can, operation things, shipping products. I mean, who would've thought Amazon was going to be delivering things, basically. I mean, they're using technology to solve the physical delivery of objects. That is, the space of what people are tackling is massive. It' no longer just about silicon and programming, it's sort of, any problem out there, there's someone trying to apply technology, which is awesome and I think that's because these people these youngsters, they're digital natives. >> Yeah. >> They've come to expect that, of course video conferencing works, of course all these other items work. That I just need to figure out how to solve problems with them, and I'm hopeful we're going to see more human-sized problems solved. I think, you know, we have, technology has maybe exacerbated a few things and dislocated, cost a lot of people jobs. Disconnected some people from other sort of stabilizing forces, >> Fake news. (laughs) >> Fake news, you know, we need-- >> John: It's consequences, side effects. >> I hope we get people solving those problems because fake news should now be hard to solve. They'll figure it out, I think, but, like, the idea is, we need to, technology does have a bit of a responsibility to solve, fix some of the crap that it broke. Actually, there's things that need, old structures, journalism is an old profession. >> Yeah. >> And it used to actually have all these wonderful benefits, but when the classified business went down the tubes, it took all that stuff down. >> Yeah. >> And there needs to be a venue for that. There needs to be new outlets for people to sort of do research, look things up, and hold people to account. >> Yeah, and hopefully some of our tools we'll be >> I hope so. >> pulling out at Silicon Angle you'll be seeing some new stuff. Let's talk about, like just in general, some of the fashionable coolness around engineering. Machine learning, AI obviously tops the list. Something that's not as sexy, or as innovative things. >> Sure. >> Because you have machines and industrial manufacturing plant equipment to people's devices. Obviously you worked at Apple, so you understand that piece, with the watch and everything. >> Yup, >> So you've got, that's an internet, we're things, people are things too. So, machines and people are at the edge of the network. So, you've got this new kind of concept. What gets you excited? Talk about how you feel about those trends. >> So, there's a ton going on there. I think what's amazing is the idea that all these sensors and switches and all the remote pieces can start to have smarts on them. I think the downside of that is some of the early IoT stuff, you know, has a whole open SSL stack in it. And, you know, that can be out of date, and when you have security problems with that now your light switch has access to your tax returns and that's not really what you want. So, I think there's definitely, there's a world coming, I think, at a technical level, we need to make operating systems and tools and networking protocols that aren't general purpose because general purpose tools are hackable. >> John: Yeah. >> I need to have a sensor and a switch that know how to talk to each other, and that's it. They can't rewrite code, they can't rewrite their firmware, they can't, like, I want to be able to know that, you have a nice office here, if somebody came in and tried to hack your switches, would you ever know? And the answer's like, you'd have no idea, but when you have things that are on your network and that serve you, if they're a general, if they're a little general purpose computing device, they're a mess. Like, you know, a switch is simple. A microphone, a microphone is simple. There's an output from it, it needs, I think we, >> So differentiated software for device. >> Well, let's get back to old school. You studied operating systems back in the day. >> Yeah. >> A process can do whatever the hell it wants. It can read from memory, it can write to disk, it can talk to all these buses. It's a very, it can do, it's very general purpose. I don't want that in my switch. I want my switch to be sort of, much more of these old little micro-controller. >> Bounded. >> Yeah, it's in a little box. I mean, so the phone and the Mac have something called Sandbox, which sort of says, you get a smaller view of the world. You get a little piece of the disk, you can't see everything else, and those are parts of it, but I think you need even more. You need, sort of, this really, I don't want a general purpose thing, I want a very specific thing that says I'm allowed to do this and I'm allowed to talk to that server; I don't have access to the internet. I've got access to that server. >> You mentioned operating systems. I mean, obviously I grew up in the computer science genre of the '80s and you did as well. That was a revolution around Unix. >> Yes. >> And then Berkeley, BSD, and all that stuff that happened around the systems world, operating systems, was really the pioneers in computing at that time. It's interesting with cloud, it's almost a throwback now to systems thinking. >> Bruce: It's true, yeah. >> You know, people looking at, and you're discussing it. >> Bruce: Yeah, Yeah. >> It's a systems problem. >> Yeah, it is. >> It's just not in a box. >> Right, and I think we witnessed the, let's get everyone a general purpose computer and see what they can do. And that was amazing, but now you're like I don't want everything to be a general I want very specific, I want very little thing, dedicated things that do this really well. I don't want my thermostat actually tracking when I'm in the house. You know, I want it to know, eh, maybe there's someone in the house, but I don't want it to know it's me. I don't want it reporting to Google what's going on. I want it to track my temperature and manage that. >> Our Wikibon team calls the term Unigrid, I call it hypergrid because essentially it's grid computer; there's no differentiation between on-premise and cloud. >> Right. >> It's one pool of resource of compute and things processes. >> It is, although I think, and that's interesting, you want that, but again you want it, how do I say this? I get a little nervous when all of my data goes to some cloud that I can't control. Like, I would love if, I'll put it this way. If I have a camera in my house, and imagine I put security cameras up, I want that to sort of see what's going on, I don't want it to publish the video to anywhere that's out of my control. If it publishes a summary that says, oh, like, someone came to your door, I'm like, okay, that's a good, reasonable thing to know and I would want to get that. So, Palo Alto recently added, there's traffic cameras that are looking at traffic, and they record video, but everyone's very nervous about that fact. They don't want to be recorded on video. So, the camera, this is actually really good, the camera only reports number of cars, number of bikes, number of pedestrians, just raw numbers. So you're pushing the processing down to the end and you only get these very anonymous statistics out of it and that's the right model. I've got a device, it can do a lot of sophisticated processing, but it gives nice summary data that is very public, I don't think anyone's really >> There's a privacy issue there that they've factored into the design? >> Yes, exactly. It's privacy and it's also the appropriateness of the data, you don't want, yeah, people don't want a camera watching them when they go by, but they're happy and they're like, oh, yeah, that street has a big increase in traffic, And there's a lot of, there were accidents here and there's people running red lights. That's valuable knowledge, not the fact that it's you in your Tesla and you almost hit me. No. (laughs) >> Yeah, or he's speeding, slow down. >> Exactly, yeah, or actually if you recorded speeders the fact that there's a lot of speeding is very interesting. Who's doing it, okay, people get upset if that's recorded. >> Yeah, I'm glad that Palo Alto is solving their traffic problem, Palo Alto problems, as we say. In general, security's been a huge issue. We were talking before we came on, about just the security nightmare. >> Bruce: Yes. >> A lot of companies are out there scratching their heads. There's so much of digital transformation happening, that's the buzzword in the industry. What does that mean from your standpoint? Because engineers are now moving to the front lines. Developers, engineering, because now there's a visibility to not just the software, it's an end goal. They call it outcome. Do you talk to customers a lot around, through your entrepreneurial venture, around trying to back requirements into product and yet deliver value? Do you get any insight from the field of kind of problems, you know, businesses are generally tryna solve with tech? >> So, that's interesting, I think when we try to start tech companies, we usually have ideas and then we go test that premise on customers. Perhaps I'm not as adaptable as I should be. We're not actually going to customers and asking them what they want. We're asking them if this is the kind of thing that would solve their problems. And usually they're happy to talk to us. The tough one, then, is then are they going to become paying customers, there's talking and there's paying, and they're different lines. >> I mean, certainly is validation. >> Exactly, that's when you really know that they care. It is, it's a tough question. I think there's always, there's a category of entrepreneur that's always very knowledgable about a small number of customers and they solve their problems, and those people are successful and they're often, They often are more services-based, but they're solving problems because they know people. They know a lot of people, they know what their paying point are. >> Alright, so here's the real question I want to know is, have you been back to Apple in the new building? >> Have I been to, I have not been in the spaceship. (laughing) I have not been in the spaceship yet. I actually understand that in order to have the event there, they actually had to stop work on the rest of the building because the construction process makes everything so dirty; and they did not want everyone to see dirty windows, so they actually halted the construction, they scrubbed down the trees, they had the event, and now it's, but now it's back. >> Now it's back to, >> So, I'll get there at some point. >> Bruce Arthur it the Vice President of Banter.ai, entrepreneur, formerly of Apple, good friend, Final question for you, just what are you excited about these days and as you look out at the tooling and the computer science and the societal impact that is seen with cloud and all these technologies, and open source, what do you, what are you excited about? >> I'm most excited, I think we actually have now enough computing resources and enough tools at hand that we can actually go back and tackle some harder computer science problems. I think there's things that used to be so big that you're like, well, that's just not, That's too much data, we could never solve that. That's too much, that would take, you know, that would take a hundred computers a hundred years to figure out. Those are problems now that are becoming very tractable, and I think it's been the rise of, yeah, it starts with Google, but some other companies that sort of really made these very large problems are now tractable, and they're now solvable. >> And open source, your opinion on open source these days? >> Open source is great. >> Who doesn't love more code? (laughs) >> Well, I should back this up, Open source is the fastest way to share and to make progress. There are times where you need what's called proprietary, but in other words valuable, when you need valuable engineers to work on something and, you know, not knowing the providence or where something comes from is a little sticky, I think there's going to be space for both. I think open source is big, but there's going to be-- >> If you have a core competency, you really want to code it. >> Exactly, you want to write that up and you-- >> You can still participate in the communities. >> Right, and I think open source is also, it's awesome when it's following. If there's something else in front, it follows very fast, it does a very good job. It's very thorough, sometimes it doesn't know where to go and it sort of meanders, and that's when other people have advantages. >> Collective intelligence. >> Exactly. >> Bruce, thanks for coming on. I really appreciate it, good to see you. This is a Cube Conversation here in the Palo Alto studio, I'm John Furrier, thanks for watching. (light electronic music)

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

(soft music) >> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Spain, everybody. We're here at the Fira in MWC23. Is just an amazing day. This place is packed. They said 80,000 people. I think it might even be a few more walk-ins. I'm Dave Vellante, Lisa Martin is here, David Nicholson. But right now we have the Analyst Hot Takes with three friends of theCUBE. Chris Lewis is back again with me in the co-host seat. Zeus Kerravala, analyst extraordinaire. Great to see you, Z. and Sarbjeet SJ Johal. Good to see you again, theCUBE contributor. And that's my new name for him. He says that is his nickname. Guys, thanks for coming back on. We got the all male panel, sorry, but it is what it is. So Z, is this the first time you've been on it at MWC. Take aways from the show, Hot Takes. What are you seeing? Same wine, new bottle? >> In a lot of ways, yeah. I mean, I was talking to somebody this earlier that if you had come from like MWC five years ago to this year, a lot of the themes are the same. Telco transformation, cloud. I mean, 5G is a little new. Sustainability is certainly a newer theme here. But I think it highlights just the difficulty I think the telcos have in making this transformation. And I think, in some ways, I've been unfair to them in some degree 'cause I've picked on them in the past for not moving fast enough. These are, you know, I think these kind of big transformations almost take like a perfect storm of things that come together to happen, right? And so, in the past, we had technologies that maybe might have lowered opex, but they're hard to deploy. They're vertically integrated. We didn't have the software stacks. But it appears today that between the cloudification of, you know, going to cloud native, the software stacks, the APIs, the ecosystems, I think we're actually in a position to see this industry finally move forward. >> Yeah, and Chris, I mean, you have served this industry for a long time. And you know, when you, when you do that, you get briefed as an analyst, you actually realize, wow, there's a lot of really smart people here, and they're actually, they have challenges, they're working through it. So Zeus was saying he's been tough on the industry. You know, what do you think about how the telcos have evolved in the last five years? >> I think they've changed enormously. I think the problem we have is we're always looking for the great change, the big step change, and there is no big step change in a way. What telcos deliver to us as individuals, businesses, society, the connectivity piece, that's changed. We get better and better and more reliable connectivity. We're shunting a load more capacity through. What I think has really changed is their attitude to their suppliers, their attitude to their partners, and their attitude to the ecosystem in which they play. Understanding that connectivity is not the end game. Connectivity is part of the emerging end game where it will include storage, compute, connect, and analytics and everything else. So I think the realization that they are not playing their own game anymore, it's a much more open game. And some things they will continue to do, some things they'll stop doing. We've seen them withdraw from moving into adjacent markets as much as we used to see. So a lot of them in the past went off to try and do movies, media, and a lot went way way into business IT stuff. They've mainly pulled back from that, and they're focusing on, and let's face it, it's not just a 5G show. The fixed environment is unbelievably important. We saw that during the pandemic. Having that fixed broadband connection using wifi, combining with cellular. We love it. But the problem as an industry is that the users often don't even know the connectivity's there. They only know when it doesn't work, right? >> If it's not media and it's not business services, what is it? >> Well, in my view, it will be enabling third parties to deliver the services that will include media, that will include business services. So embedding the connectivity all the way into the application that gets delivered or embedding it so the quality mechanism deliver the gaming much more accurately or, I'm not a gamer, so I can't comment on that. But no, the video quality if you want to have a high quality video will come through better. >> And those cohorts will pay for that value? >> Somebody will pay somewhere along the line. >> Seems fuzzy to me. >> Me too. >> I do think it's use case dependent. Like you look at all the work Verizon did at the Super Bowl this year, that's a perfect case where they could have upsold. >> Explain that. I'm not familiar with it. >> So Verizon provided all the 5G in the Super Bowl. They provided a lot of, they provided private connectivity for the coaches to talk to the sidelines. And that's a mission critical application, right? In the NFL, if one side can't talk, the other side gets shut down. You can't communicate with the quarterback or the coaches. There's a lot of risk at that. So, but you know, there's a case there, though, I think where they could have even made that fan facing. Right? And if you're paying 2000 bucks to go to a game, would you pay 50 bucks more to have a higher tier of bandwidth so you can post things on social? People that go there, they want people to know they were there. >> Every football game you go to, you can't use your cell. >> Analyst: Yeah, I know, right? >> All right, let's talk about developers because we saw the eight APIs come out. I think ISVs are going to be a big part of this. But it's like Dee Arthur said. Hey, eight's better than zero, I guess. Okay, so, but so the innovation is going to come from ISVs and developers, but what are your hot takes from this show and now day two, we're a day and a half in, almost two days in. >> Yeah, yeah. There's a thing that we have talked, I mentioned many times is skills gravity, right? Skills have gravity, and also, to outcompete, you have to also educate. That's another theme actually of my talks is, or my research is that to puts your technology out there to the practitioners, you have to educate them. And that's the only way to democratize your technology. What telcos have been doing is they have been stuck to the proprietary software and proprietary hardware for too long, from Nokia's of the world and other vendors like that. So now with the open sourcing of some of the components and a few others, right? And they're open source space and antenna, you know? Antennas are becoming software now. So with the invent of these things, which is open source, it helps us democratize that to the other sort of skirts of the practitioners, if you will. And that will bring in more applications first into the IOT space, and then maybe into the core sort of California, if you will. >> So what does a telco developer look like? I mean, all the blockchain developers and crypto developers are moving into generative AI, right? So maybe those worlds come together. >> You'd like to think though that the developers would understand everything's network centric today. So you'd like to think they'd understand that how the network responds, you know, you'd take a simple app like Zoom or something. If it notices the bandwidth changes, it should knock down the resolution. If it goes up it, then you can add different features and things and you can make apps a lot smarter that way. >> Well, G2 was saying today that they did a deal with Mercedes, you know this probably better than I do, where they're going to embed WebEx in the car. And if you're driving, it'll shut off the camera. >> Of course. >> I'm like, okay. >> I'll give you a better example though. >> But that's my point. Like, isn't there more that we can do? >> You noticed down on the SKT stand the little helicopter. That's a vertical lift helicopter. So it's an electric vertical lift helicopter. Just think of that for a second. And then think of the connectivity to control that, to securely control that. And then I was recently at an event with Zeus actually where we saw an air traffic control system where there was no people manning the tower. It was managed by someone remotely with all the cameras around them. So managing all of those different elements, we call it IOT, but actually it's way more than what we thought of as IOT. All those components connecting, communicating securely and safely. 'Cause I don't want that helicopter to come down on my head, do you? (men laugh) >> Especially if you're in there. (men laugh) >> Okay, so you mentioned sustainability. Everybody's talking about power. I don't know if you guys have a lot of experience around TCO, but I'm trying to get to, well, is this just because energy costs are so high, and then when the energy becomes cheap again, nobody's going to pay any attention to it? Or is this the real deal? >> So one of the issues around the, if we want to experience all that connectivity locally or that helicopter wants to have that connectivity, we have to ultimately build denser, more reliable networks. So there's a CapEx, we're going to put more base stations in place. We need more fiber in the ground to support them. Therefore, the energy consumption will go up. So we need to be more efficient in the use of energy. Simple as that. >> How much of the operating expense is energy? Like what percent of it? Is it 10%? Is it 20%? Is it, does anybody know? >> It depends who you ask and it depends on the- >> I can't get an answer to that. I mean, in the enterprise- >> Analyst: The data centers? >> Yeah, the data centers. >> We have the numbers. I think 10 to 15%. >> It's 10 to 12%, something like that. Is it much higher? >> I've got feeling it's 30%. >> Okay, so if it's 30%, that's pretty good. >> I do think we have to get better at understanding how to measure too. You know, like I was talking with John Davidson at Sysco about this that every rev of silicon they come out with uses more power, but it's a lot more dense. So at the surface, you go, well, that's using a lot more power. But you can consolidate 10 switches down to two switches. >> Well, Intel was on early and talking about how they can intelligently control the cores. >> But it's based off workload, right? That's the thing. So what are you running over it? You know, and so, I don't think our industry measures that very well. I think we look at things kind of boxed by box versus look at total consumption. >> Well, somebody else in theCUBE was saying they go full throttle. That the networks just say just full throttle everything. And that obviously has to change from the power consumption standpoint. >> Obviously sustainability and sensory or sensors from IOT side, they go hand in hand. Just simple examples like, you know, lights in the restrooms, like in public areas. Somebody goes in there and just only then turns. The same concept is being applied to servers and compute and storage and every aspects and to networks as well. >> Cell tower. >> Yeah. >> Cut 'em off, right? >> Like the serverless telco? (crosstalk) >> Cell towers. >> Well, no, I'm saying, right, but like serverless, you're not paying for the compute when you're not using it, you know? >> It is serverless from the economics point of view. Yes, it's like that, you know? It goes to the lowest level almost like sleep on our laptops, sleep level when you need more power, more compute. >> I mean, some of that stuff's been in networking equipment for a long time, it just never really got turned on. >> I want to ask you about private networks. You wrote a piece, Athenet was acquired by HPE right after Dell announced a relationship with Athenet, which was kind of, that was kind of funny. And so a good move, good judo move by by HP. I asked Dell about it, and they said, look, we're open. They said the right things. We'll see, but I think it's up to HP. >> Well, and the network inside Dell is. >> Yeah, okay, so. Okay, cool. So, but you said something in that article you wrote on Silicon Angle that a lot of people feel like P5G is going to basically replace wireless or cannibalize wireless. You said you didn't agree with that. Explain why? >> Analyst: Wifi. >> Wifi, sorry, I said wireless. >> No, that's, I mean that's ridiculous. Pat Gelsinger said that in his last VMware, which I thought was completely irresponsible. >> That it was going to cannibalize? >> Cannibalize wifi globally is what he said, right? Now he had Verizon on stage with him, so. >> Analyst: Wifi's too inexpensive and flexible. >> Wifi's cheap- >> Analyst: It's going to embed really well. Embedded in that. >> It's reached near ubiquity. It's unlicensed. So a lot of businesses don't want to manage their own spectrum, right? And it's great for this, right? >> Analyst: It does the job. >> For casual connectivity. >> Not today. >> Well, it does for the most part. Right now- >> For the most part. But never at these events. >> If it's engineered correctly, it will. Right? Where you need private 5G is when reliability is an absolute must. So, Chris, you and I visited the Port of Rotterdam, right? So they're putting 5G, private 5G there, but there's metal containers everywhere, right? And that's going to disrupt it. And so there are certain use cases where it makes sense. >> I've been in your basement, and you got some pretty intense equipment in there. You have private 5G in there. >> But for carpeted offices, it does not make sense to bring private. The economics don't make any sense. And you know, it runs hot. >> So where's it going to be used? Give us some examples of where we should be looking for. >> The early ones are obviously in mining, and you say in ports, in airports. It broadens cities because you've got so many moving parts in there, and always think about it, very expensive moving parts. The cranes in the port are normally expensive piece of kits. You're moving that, all that logistics around. So managing that over a distance where the wifi won't work over the distance. And in mining, we're going to see enormous expensive trucks moving around trying to- >> I think a great new use case though, so the Cleveland Browns actually the first NFL team to use it for facial recognition to enter the stadium. So instead of having to even pull your phone out, it says, hey Dave Vellante. You've got four tickets, can we check you all in? And you just walk through. You could apply that to airports. You could do put that in a hotel. You could walk up and check in. >> Analyst: Retail. >> Yeah, retail. And so I think video, realtime video analytics, I think it's a perfect use case for that. >> But you don't need 5G to do that. You could do that through another mechanism, couldn't you? >> You could do wire depending on how mobile you want to do it. Like in a stadium, you're pulling those things in and out all the time. You're moving 'em around and things, so. >> Yeah, but you're coming in at a static point. >> I'll take the contrary view here. >> See, we can't even agree on that. (men laugh) >> Yeah, I love it. Let's go. >> I believe the reliability of connection is very important, right? And the moving parts. What are the moving parts in wifi? We have the NIC card, you know, the wifi card in these suckers, right? In a machine, you know? They're bigger in size, and the radios for 5G are smaller in size. So neutralization is important part of the whole sort of progress to future, right? >> I think 5G costs as well. Yes, cost as well. But cost, we know that it goes down with time, right? We're already talking about 60, and the 5G stuff will be good. >> Actually, sorry, so one of the big boom areas at the moment is 4G LTE because the component price has come down so much, so it is affordable, you can afford to bring it all together. People don't, because we're still on 5G, if 5G standalone everywhere, you're not going to get a consistent service. So those components are unbelievably important. The skillsets of the people doing integration to bring them all together, unbelievably important. And the business case within the business. So I was talking to one of the heads of one of the big retail outlets in the UK, and I said, when are you going to do 5G in the stores? He said, well, why would I tear out all the wifi? I've got perfectly functioning wifi. >> Yeah, that's true. It's already there. But I think the technology which disappears in front of you, that's the best technology. Like you don't worry about it. You don't think it's there. Wifi, we think we think about that like it's there. >> And I do think wifi 5G switching's got to get easier too. Like for most users, you don't know which is better. You don't even know how to test it. And to your point, it does need to be invisible where the user doesn't need to think about it, right? >> Invisible. See, we came back to invisible. We talked about that yesterday. Telecom should be invisible. >> And it should be, you know? You don't want to be thinking about telecom, but at the same time, telecoms want to be more visible. They want to be visible like Netflix, don't they? I still don't see the path. It's fuzzy to me the path of how they're not going to repeat what happened with the over the top providers if they're invisible. >> Well, if you think about what telcos delivers to consumers, to businesses, then extending that connectivity into your home to help you support secure and extend your connection into Zeus's basement, whatever it is. Obviously that's- >> His awesome setup down there. >> And then in the business environment, there's a big change going on from the old NPLS networks, the old rigid structures of networks to SD1 where the control point is moved outside, which can be under control of the telco, could be under the control of a third party integrator. So there's a lot changing. I think we obsess about the relative role of the telco. The demand is phenomenal for connectivity. So address that, fulfill that. And if they do that, then they'll start to build trust in other areas. >> But don't you think they're going to address that and fulfill that? I mean, they're good at it. That's their wheelhouse. >> And it's a 1.6 trillion market, right? So it's not to be sniffed at. That's fixed on mobile together, obviously. But no, it's a big market. And do we keep changing? As long as the service is good, we don't move away from it. >> So back to the APIs, the eight APIs, right? >> I mean- >> Eight APIs is a joke actually almost. I think they released it too early. The release release on the main stage, you know? Like, what? What is this, right? But of course they will grow into hundreds and thousands of APIs. But they have to spend a lot of time and effort in that sort of context. >> I'd actually like to see the GSMA work with like AWS and Microsoft and VMware and software companies and create some standardization across their APIs. >> Yeah. >> I spoke to them yes- >> We're trying to reinvent them. >> Is that not what they're doing? >> No, they said we are not in the business of a defining standards. And they used a different term, not standard. I mean, seriously. I was like, are you kidding me? >> Let's face it, there aren't just eight APIs out there. There's so many of them. The TM forum's been defining when it's open data architecture. You know, the telcos themselves are defining them. The standards we talked about too earlier with Danielle. There's a lot of APIs out there, but the consistency of APIs, so we can bring them together, to bring all the different services together that will support us in our different lives is really important. I think telcos will do it, it's in their interest to do it. >> All right, guys, we got to wrap. Let's go around the horn here, starting with Chris, Zeus, and then Sarbjeet, just bring us home. Number one hot take from Mobile World Congress MWC23 day two. >> My favorite hot take is the willingness of all the participants who have been traditional telco players who looked inwardly at the industry looking outside for help for partnerships, and to build an ecosystem, a more open ecosystem, which will address our requirements. >> Zeus? >> Yeah, I was going to talk about ecosystem. I think for the first time ever, when I've met with the telcos here, I think they're actually, I don't think they know how to get there yet, but they're at least aware of the fact that they need to understand how to build a big ecosystem around them. So if you think back like 50 years ago, IBM and compute was the center of everything in your company, and then the ecosystem surrounded it. I think today with digital transformation being network centric, the telcos actually have the opportunity to be that center of excellence, and then build an ecosystem around them. I think the SIs are actually in a really interesting place to help them do that 'cause they understand everything top to bottom that I, you know, pre pandemic, I'm not sure the telcos were really understand. I think they understand it today, I'm just not sure they know how to get there. . >> Sarbjeet? >> I've seen the lot of RN demos and testing companies and I'm amazed by it. Everything is turning into software, almost everything. The parts which are not turned into software. I mean every, they will soon. But everybody says that we need the hardware to run something, right? But that hardware, in my view, is getting miniaturized, and it's becoming smaller and smaller. The antennas are becoming smaller. The equipment is getting smaller. That means the cost on the physicality of the assets is going down. But the cost on the software side will go up for telcos in future. And telco is a messy business. Not everybody can do it. So only few will survive, I believe. So that's what- >> Software defined telco. So I'm on a mission. I'm looking for the monetization path. And what I haven't seen yet is, you know, you want to follow the money, follow the data, I say. So next two days, I'm going to be looking for that data play, that potential, the way in which this industry is going to break down the data silos I think there's potential goldmine there, but I haven't figured out yet. >> That's a subject for another day. >> Guys, thanks so much for coming on. You guys are extraordinary partners of theCUBE friends, and great analysts and congratulations and thank you for all you do. Really appreciate it. >> Analyst: Thank you. >> Thanks a lot. >> All right, this is a wrap on day two MWC 23. Go to siliconangle.com for all the news. Where Rob Hope and team are just covering all the news. John Furrier is in the Palo Alto studio. We're rocking all that news, taking all that news and putting it on video. Go to theCUBE.net, you'll see everything on demand. Thanks for watching. This is a wrap on day two. We'll see you tomorrow. (soft music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, husband. Welcome back to the cubes. Live coverage of AWS reinvent 2020. We're not in person this year. We're virtual This is the Cube Virtual. I'm John for your host of the Cube. Roger Barker, the General Manager AWS Robotics and Autonomous Service. And a lot of other cool stuff was on last year. Always. Speed Racer. You got the machines. Now you have real time Robotics hitting, hitting seen Andy Jassy laid out a huge vision and and data points and announcements around Industrial this I o t it's kind of coming together. Roger, great to see you. And thanks for coming on. I want to dig in and get your perspective. Thanks for joining the Cube. >>Good to be here with you again today. >>Alright, so give us your take on the announcements yesterday and how that relates to the work that you're doing on the robotic side at a w s. And where where does this go from? You know, fun to real world to societal impact. Take us through. What? You how you see that vision? >>Yeah, sure. So we continue to see the story of how processing is moving to the edge and cloud services, or augmenting that processing at the edge with unique and new services. And he talked about five new industrial machine learning services yesterday, which are very relevant to exactly what we're trying to do with AWS robot maker. Um, a couple of them monitor on, which is for equipment monitoring for anomalies. And it's a whole solution, from an edge device to a gateway to a service. But we also heard about look out for equipment, which is if a customer already has their own censors. It's a service that can actually back up that that sensor on their on the device to actually get identify anomalies or potential failures. And we saw look out for video, which allows customers to actually use their camera and and build a service to detect anomalies and potential failures. When A. W s robot maker, we have Ross Cloud Service extensions, which allow developers to connect their robot to these services and so increasingly, that combination of being able to put sensors and processing at the edge, connecting it back with the cloud where you could do intelligent processing and understand what's going on out in the environment. So those were exciting announcements. And that story is going to continue to unfold with new services. New sensors we can put on our robots to again intelligently process the data and control these robots and industrial settings. >>You know, this brings up a great point. And, you know, I wasn't kidding. Was saying fun to real world. I mean, this is what's happening. Um, the use cases air different. You look at you mentioned, um, you know, monitor on lookout. But those depend Panorama appliance. You had computer vision, machine learning. I mean, these are all new, cool, relevant use cases, but they're not like static. It's not like you're going to see them. Just one thing is like the edge has very diverse and sometimes mostly purpose built for the edge piece. So it's not like you could build a product. Okay, fits everywhere. Talk about that dynamic and why the robotics piece has to be agile. And what do you guys doing to make that workable? Because, you know, you want purpose built. The purpose built implies supply chain years. in advance. It implies slow and you know, how do you get the trust? How do you get the security? Take us through that, please. >>So to your point, um, no single service is going to solve all problems, which is why AWS has has released a number of just primitives. Just think about Kinesis video or Aiken. Stream my raw video from an edge device and build my own machine learning model in the cloud with sage maker that will process that. Or I could use recognition. So we give customers these basic building blocks. But we also think about working customer backward. What is the finished solution that we could give a customer that just works out of the box? And the new services we heard about we heard about yesterday were exactly in that latter category. Their purpose built. They're ready to be used or trained for developers to use and and with very little customization that necessary. Um, but the point is, is that is that these customers that are working these environments, the business questions change all the time, and so they need actually re program a robot on the fly, for example, with a new mission to address the new business need that just arose is a dynamic, which we've been very tuned into since we first started with a device robo maker. We have a feature for a fleet management, which allows a developer to choose any robot that's out in their fleet and take the software stack a new software stack tested in simulation and then redeploy it to that robot so it changes its mission. And this is a This is a dialogue we've been seeing coming up over the last year, where roboticists are starting to educate their company that a robot is a device that could be dynamically program. At any point in time, they contest their application and simulation while the robots out in the field verify it's gonna work correctly and simulation and then change the mission for that robot. Dynamically. One of my customers they're working with Woods Hole Institute is sending autonomous underwater robots out into the ocean to monitor wind farms, and they realized the mission may change may change based on what they find out. If the wind farm with the equipment with their autonomous robot, the robot itself may encounter an issue and that ability because they do have connective ity to change the mission dynamically. First Testament, of course, in simulation is completely changing the game for how they think about robots no longer a static program at once, and have to bring it back in the shop to re program it. It's now just this dynamic entity that could test and modify it any time. >>You know, I'm old enough to know how hard that really is to pull off. And this highlights really kind of how exciting this is, E. I mean, just think about the idea of hardware being dynamically updated with software in real time and or near real time with new stacks. I mean, just that's just unheard of, you know, because purpose built has always been kind of you. Lock it in, you deploy it. You send the tech out there this kind of break fixed kind of mindset. Let's changes everything, whether it's space or underwater. You've been seeing everything. It's software defined, software operated model, so I have to ask you First of all, that's super awesome. Anyway, what's this like for the new generation? Because Andy talked on stage and in in my one On one way I had with him. He talked about, um, and referring to land in some of these new things. There's a new generation of developer. So you gotta look at these young kids coming out of school to them. They don't understand what how hard this is. They just look at it as lingua frank with software defined stuff. So can you share some of the cutting edge things that are coming out of these new new the new talent or the new developers? Uh, I'm sure the creativity is off the charts. Can you share some cool, um, use cases? Share your perspective? >>Absolutely. I think there's a couple of interesting cases to look at. One is, you know, roboticists historically have thought about all the processing on the robot. And if you say cloud and cloud service, they just couldn't fathom that reality that all the processing has cannot has to be, you know, could be moved off of the robot. Now you're seeing developers who are looking at the cloud services that we're launching and our cloud service extensions, which give you a secure connection to the cloud from your robot. They're starting to realize they can actually move some of that processing off the robot that could lower the bomb or the building materials, the cost of the robot. And they can have this dynamic programming surface in the cloud that they can program and change the behavior of the robot. So that's a dialogue we've seen coming over the last couple years, that rethinking of where the software should live. What makes sense to run on the robot? And what should we push out to the cloud? Let alone the fact that if you're aggregating information from hundreds of robots, you can actually build machine learning models that actually identify mistakes a single robot might make across the fleet and actually use that insight to actually retrain the models. Push new applications down, pushing machine learning models down. That is a completely different mindset. It's almost like introducing distributed computing to roboticists that you actually think this fabric of robots and another, more recent trend we're seeing that were listening very closely to customers is the ability to use simulation and machine learning, specifically reinforcement. Learning for a robot actually try different tasks up because simulations have gotten so realistic with the physics engines and the rendering quality that is almost nearly realistic for a camera. The physics are actually real world physics, so that you can put a simulation of your robot into a three D simulated world and allow it to bumble around and make mistakes while trying to perform the task that you frankly don't know how to write the code for it so complex and through reinforcement, learning, giving rewards signals if it does something right or punishment or negative rewards signals. If it does something wrong, the machine learning algorithm will learn to perform navigation and manipulation tasks, which again the programmer simply didn't have to write a line of code for other than creating the right simulation in the right set of trials >>so that it's like reversing the debugging protocol. It's like, Hey, do the simulations. The code writes itself. Debug it on the front end. It rights itself rather than writing code, compiling it, debugging it, working through the use cases. I mean, it's pretty different. >>It is. It's really a new persona. When we started out, not only are you taking that roboticist persona and again introduced him to the cloud services and distributed computing what you're seeing machine learning scientists with robotics experience is actually rising. Is a new developer persona that we have to pay attention to him. We're talking to right now about what they what they need from our service. >>Well, Roger, I get I'm getting tight on time here. I want one final question before we break. How does someone get involved with Amazon? And I'll see you know, whether it's robotics and new areas like space, which is verging, there's a lot of action, a lot of interest. Um, how does someone engaged with Amazon to get involved, Whether I'm a student or whether I'm a professional, I want a code. What's what's the absolutely, >>absolutely, so certainly reinvent. We have several sessions that reinvent on AWS robo maker. Our team is there, presenting and talking about our road map and how people can get engaged. There is, of course, the remarks conference, which will be happening next year, hopefully to get engaged. Our team is active in the Ross Open Source Community and Ross Industrial, which is happening in Europe later in December but also happens in the Americas, where were present giving demos and getting hands on tutorials. We're also very active in the academic research in education arena. In fact, we just released open source curriculum that any developer could get access to on Get Hub for Robotics and Ross, as well as how to use robo maker that's freely available. Eso There's a number of touch points and, of course, I'd be welcome to a field. Any request for people to learn more or just engage with our team? >>Arthur Parker, general manager. It is robotics and also the Autonomous Systems Group at AWS Amazon Web services. Great stuff, and this is really awesome insight. Also, you know it za candy For the developers, it's the new generation of people who are going to get put their teeth into some new science and some new problems to solve. With software again, distributed computing meets robotics and hardware, and it's an opportunity to change the world literally. >>It is an exciting space. It's still Day one and robotics, and we look forward to seeing the car customers do with our service. >>Great stuff, of course. The Cube loves this country. Love robots. We love autonomous. We love space programming all this stuff, totally cutting edge cloud computing, changing the game at many levels with the digital transformation just a cube. Thanks for watching

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host Dave Vellante. >> Hello everyone and welcome to this CUBE conversation. You know, data protection, it used to be so easy. You'd have apps, they'd be running on a bunch of servers, you'd bolt on a little backup and boom! One size fit all. It was really easy peasy. Now, business disruptions at the time, they were certainly not desired, but they were definitely much more tolerated and they were certainly fairly common place. Today, business disruptions are still fairly common occurrence but the situation is different. First of all, digital imperatives have created so much more pressure for IT organizations to deliver services that are always available with great consumer experiences. The risks of downtime are so much higher but meeting expectations is far more complex. This idea of "one size fits all" it really no longer cuts it. You got physical, virtual, public cloud, on-prim, hybrid, edge, containers. Add to this cyber threats, AI, competition from digital disrupters. The speed of change is accelerating and it's stressing processes and taxing the people skills required to deliver business resilience. These and other factors are forcing organizations to rethink how they protect, manage, and secure data in the coming decade. And with me, to talk about the state of data protection today and beyond, is a thought leader from one of the companies in data protection, Arthur Lent is the Senior Vice President and CTO of the Data Protection Division at Dell EMC. Arthur, good to see you again. Thanks for coming in. >> Great to see you, Dave. >> So, I'm going to start right off. This is a hot space and everybody wants a piece of your hide because you're the leader. How are you guys responding to that competitive threat? >> Well, so the key thing that we're doing is we're taking our proven products and technologies and we've recognized the need to transform and really modernize them and invest in a new set of capabilities and changing workloads. And our core part of that, with some changes in leadership, have been to shift our processes in terms of how we do stuff internally and so we've moved from a very big batch waterfall-style approach where things need to be planned one, two, three years out in advance, to a very small batch agile approach where we're looking a couple of weeks, a couple of months in advance of what we're going to be delivering into product. And this is enabling us to be far more responsive to what we're learning in the market in very rapidly changing areas. And we're at the spot where we now have several successive releases that have been taking place with our products in this new model. >> So, that's a major cultural shift that you're really driving. I mean, that allows you to track you know, younger people, you guys are a global organization so I mean, how is that sort of dynamic change? You know, people sometimes maybe think of you as this stodgy, you know, company been around for 20 plus years. What's it like when you walk around the hallways? What's that dynamic like? >> It's like we're the largest start-up in the data protection industry but we've got the backing of a Fortune 50 company. >> Nice. All right, well let's get into it. I talked in my narrative upfront about business disruptions and I said there's still you know, kind of a common occurrence today, is that what you're seeing? >> Absolutely! So, our latest data protection index research has 82% of the people we surveyed experienced downtime or data loss within the last 12 months and this survey was just completed within the last month or two. So, this is still very much a real problem. >> Why do you think it's still a problem today? What are the factors? >> So I would say the problem's getting worse and it's because complexity is only increasing in IT environments. Complexity around multi-platform, between physical servers, virtual servers, cloud, various flavors of hybrid cloud, data distribution between the core, edge and the cloud, growing data volumes, the amount of data, and the data that companies need to run their business is ever increasing, and a growing risk around compliance, around security threats, and many customers have multi-vendor environments and multi-vendor environments also increase their complexity and risk and challenges. >> Who was that talking about cloud? Because you know, we entered last decade. Cloud was kind of this experimental, throw some dev out in the cloud, and now as we enter this decade it's kind of a fundamental part of IT strategies. Every CIO, he or she has a cloud strategy. But it's also becoming clear that it's a hybrid world. So, in thinking about data protection, how does hybrid affect how your customers are thinking about protecting their data in the coming decade? >> So it produces a bunch of changes in how you have to think about things and today, we have over a thousand customers protecting over 2.5 exabytes of data in the public cloud. And it goes across a variety of use cases from longterm retention in the cloud, backup to the cloud, disaster recovery to the cloud, a desire to leverage the cloud for analytics and dev test, as well as production workloads in the cloud and the need to protect data that is born in the cloud. And we're in an environment where IT is spanning from the edge to the core to the cloud and the need to have a cohesive ability and approach to protect that data across its lifecycle for where it's born and where it's being operated on and where value is being added to it. >> Yeah, and people don't want to buy a thousand products to do that or even a dozen products to do that, right? They want a single platform. I want to talk about containers because Kubernetes, specifically, the containers generally one of the hottest areas. It's funny, containers have been around forever (laughs) but now they're exploding, people are investing much more in containers. IT organizations and dev organizations see it as a way to drive some of the agility that you maybe talked about earlier. But I'm hearing a lot about you know, protection, data protection for containers, and I'm thinking, "Well, wait a minute... "You know, containers come and go. "They're ephemeral. Why do I need to protect them?" Help me understand that. >> So, first I want to say yeah, we're seeing a lot of interest in enterprises deploying containers. Our latest survey says 57% of enterprises are planning on deploying it next year. And in terms of the ephemerality and the importance of protection, I have to admit, I started this job about a year ago and I was thinking almost exactly the same thing you were. I came in, we had an advanced development project going on around how to protect Kubernetes environments, both to protect the data and the infrastructure. And I was like, "Yeah, I see this "as an important advanced development priority, "but why is this important "to productize in the near future?" And then I thought about it some more and was talking to folks where the Kubernetes technologies, there's two key things with it. One: It's Kubernetes as a DevOps CI/CD environment, well if that environment is down... Your business is down in terms of being able to develop. So, you have to think about the loss of productivity and the loss of business value as you're trying to get your developer environment back up and running. But also, even though there might not be stateful applications running in the containers, there's generally production usage in terms of delivering your service that's coming out of that cluster. So, if your clusters go down or your Kubernetes environment goes down, you got to be able to bring it back up in order to be able to get it up and running. And then the last thing is in the last year or two, there's been a lot of investment in the Kubernetes community around enabling Kubernetes containers to be stateful and to have persistence with them. And that will enable databases to run in containers and stateful applications to run in to containers. And we see a lot of enterprises that are interested in doing that but... Now they can have that persistence but it turns out they can't go into production with the persistence because they can't back it up. And so, there's this chicken and egg problem in order to do the production you need both the state and the data protection. And the nice thing about the transformation that we've done is as we saw this trend materializing we were able to rapidly take this advanced development project and turn it into productization. And we're able to get to a tech preview in the summer and a joint announcement with Pat Gelsinger around our work together in the Kubernetes environment and being able to get our first... Product release out to market a couple of weeks ago and we're going to be able to really rapidly enhance the capabilities of that as we're working with our customers on where do they need the features added most and being able to rapidly integrate in with VMware's management ecosystem for container environments. >> So, you got a couple things going on there. You're kind of describing the dynamic of the developer and developers set to key... Strategic linchpin now. Because the time between you developing function and you get it to market I mean, it used to be weeks or months or sometimes even years. Today, it's like nanoseconds, right? "Hey, we need this function today. "Something's happening in the market, go push it." And if you don't have your data, you don't have the containers. The data and the containers are not protected, you're in trouble, right? Okay so, that's one aspect of it. The other is the technical piece so help us understand like, how you do that. What's the secret sauce conceptually behind you know, protecting containers? >> So, there's really two parts of what one needs to do for protecting the containers. There's the container infrastructure itself and the container configuration and knowing what's involved in the environment so that if your Kubernetes cluster goes down being able to restart it and being able to get your appropriate application environment up and running So, the containers may not be stateful but you've got to be able to get your CI/CD operate environment up and running again. And then the second part is we are seeing people use stateful containers and put databases in containers in development and they want to roll that into production. And so for there we need to backup not just the container definitions but backup the data that's inside the container and be able to restore them. And those are some of the things that we're working on now. >> One of the things I've learned from being around this industry for a while is people who really understand technology, they'll ask questions about, "What happens when something goes wrong?" so it's all about the recovery is really what you're talking about is that's the key. How does machine intelligence fit in... Stay on containers for a minute. Is machine learning and machine intelligence allowing you to recover more quickly, does it fit in there? >> So a key part of the container environment that's different from some of the environments in the past is just how dynamic it is and just how frequently containers are going to come and go and workloads mix, expand, and contract their usage of IT resources and footprint. And that really increases the need for automation and using some AI and machine learning techniques so that one can discover what is an application as it's containerized and what are all the resources it needs so that in the event of an interruption of service you know, all of the pieces that you need to bring together and automate its recovery and bring back. And in these environments you can no longer be in a spot to have people handcraft and tailor exactly what to protect and exactly how to bring it back after protection. You need these things to be able to protect themselves automatically and recover themselves automatically. >> So, I want to sort of, double click on that. Again, it's 2020 so I'm always going back to last decade and thinking about what's different. Beginning of last decade people were afraid of automation, they wanted knobs to turn. Even exiting the decade recently and even now, people are afraid about losing jobs. But the reality is things are happening so fast, there's so much data that humans just can't keep up. So, maybe you could make some comments about automation generally and specifically applying to data protection and recovery. >> Okay, so with the increasing amounts of data to be protected and the increasing complexity of environments, more and more of the instances of downtime or challenges in performing a recovery, tend to be because of the complexity of having deployed them and having the recovery procedures write and insuring that the SLAs that are needed are met and it's just no longer realistic... To expect people to have to do all of those things in excruciating detail. And it's really just necessary, in order to meet the SLAs going forward, to have the environments be automatically discovered, automatically protected, and have automated workflows for the recovery scenarios. And because of the complexities of changing, we need to reach the point of having AI and machine learning technologies help guide the people owning the data protection on data criticality and what's the right SLA for this and what's the right SLA for that and really get a human-machine partnership. So, it's not people or machines, but it's rather the people and machines working together in tandem with each doing what they do best to get the best outcome. >> Now that's great, you'd be helping people prioritize and the criticality applications... I want to change the conversation and talk about the edge a little bit. You sponsor off like, IDC surveys on how big the market is in terms of just zettabytes and it's really interesting and thank you from the industry standpoint for doing that. I have no doubt edge is coming into play now because so much data is going to be created at the edge, there's all this analog data that's going to be digitized, and it's just a big component of the digital future. In thinking about data at the edge, a lot of the data is going to stay at the edge, maybe it's got to be persisted at the edge. And obviously if it's persisted it has to be protected. So, how are you thinking about the evolution of edge, specifically around data protection? >> Okay, so the... I think you kind of caught it in the beginning. There's going to be a huge amount of data in the edge. Our analysis has us seeing that there's going to be more data generated and stored in the edge than in all the public clouds combined. So, that's just a huge shift in that three to five to ten year timeframe. >> Lot of data. >> Lot of data. You're not going to be able to bring it all back. You're just going to have elements of physics. So, there's data that's going to need to be persisted there. Some of that data will be transitory. Some of that data is going to be critical and need to be recovered. And a key part of the strategy around the edge is really, again going back to that, AI and machine learning intelligence and having a centralized control and understanding of what is my data in the edge and having what are the right triggers and understanding of what's going on of when is it an event occurred where I really need to protect this data? You can't afford to protect everything all the time. You got to protect the right things at the right time and then move it around appropriately. And so, a key part of being successful with the edge is getting that distributed intelligence and distributed control and recognizing that applications are going to span from core to edge to cloud and have just specific features and functions and capabilities that implement into various spots and then that intelligence to do the right thing at the right time with central policy control. >> So this is a good discussion. We've spanned a lot of territories but let's bring it back to the practical you know, uses for the IT person today saying, "Okay, Arthur, look. "Yeah, I'm doing cloud. I'm playing around with AI. "I've got my feet in containers "and my dev staff is doing that. "Yeah, edge. I see that coming. "But I just got some problems today that I have to solve." So, my question to you is, how do you address those really tactical day-to-day problems that your customers are facing today and still help them you know, plan for the future and make sure that they've got a platform that's going to be there for them and they're not going to just have to rip and replace in three or four years? >> Okay, and so that's like the $100,000 question as we look at ourselves in this situation. And the key is really taking our proven technologies and proven products and solutions and taking the agile approach for adding the most critical modern capabilities for new workloads, new deployment scenarios alongside them as we modernize those solutions themselves and really bringing our customers along in the journey with that and having a very smooth path for that customer transition experience on that path to our powered up portfolio. >> I mean, that's key because if you get that wrong and your customers get that wrong then maybe now it's a $100,000 problem it's going to be billions of dollars of problems. >> Fair. >> So, I want to talk a little bit about alternative use cases for data protection. We've kind of changed the parlance, we used to call it "backup". I've often said people want to get more out of their backup, they want to do other things with their backup 'cause they don't want just to pay for insurance, the CFO wants ROI. What are you seeing in terms of alternative use cases and the sort of expanding TAM, if you will, of backup and data protection? >> So, a core part of our strategy is to recognize that there is all of this data that we have as part of the data protection solutions and there's a desire on our customer's parts to get additional business value out of it and additional use cases from there. And we've explored and are investing in a variety of ways of doing that and the one that we see that's really hit a key problem of the here-and-now is around security and malware. And we are having multiple customers that are under attack for a variety of threats and it's hitting front page news. And a very large fraction of enterprises are having some amount of downtime due to malware or cyber attacks. And a key focus that we've had is around our cyber recovery solutions of really enabling a protected air gap solution so that in the event of some hidden malware or an intrusion, having a protected copy of that data to be able to restore from. And we've got customers who otherwise would have been brought down but were able to be brought back up very, very quickly by recovering out of our cyber vault. >> Yeah, I mean, it's a huge problem. Cyber has become a board-level issue, people are you know, scared to death of getting hit with ransomware, getting their entire data corpus encrypted so that air gap is obviously critical and increasingly it's becoming a fundamental requirement from a compliance standpoint. All right, I'll give you last word. Bring us home. >> Okay, so, the most important thing about the evolving and rapidly changing space of data protection at this point is that need for enterprises to have a coherent approach across their old and new workloads, across their emerging technologies, across their deployments in core, edge, and cloud, to be able to identify and manage that data and protect and manage that data throughout its lifecycle and to have a single coherent way to do that and single set of policies and controls across the data in all of those places. And that's one key part of our strategy of bringing that coherence across all of those environments and not just in the data protection domain, but there's also a need for this cross-domain coherence and getting your automation and simplification, not just in the data protection domain but up into higher levels of your infrastructure. And so we've got automation's taking place with our PowerOne Converged Infrastructure and we're looking across our Dell Technologies portfolio of how can we together, with our partners in Dell Technologies, solve more of our customer problems by doing things jointly. And so for example, doing data management that spans not just your protection storage but your primary storage as well. Your AI and ML techniques for full stack automation. Working with VMware around the full end to end Kubernetes management for VMware environments. And those are just a couple of examples of where we're looking to both be full across the data protection, but then expand into broader IT collaborations. >> You're seeing this across the industry. I mean, Arthur, you mentioned PowerOne. You're talking about microservices, API-based platform increasing, we're seeing infrastructure as a code which means more speed, more agility, and that's how the industry is dealing with all this complexity. Arthur, thank you so much for coming on theCUBE. Really appreciate it. >> Thank you. >> And thank you for watching, everybody. This is Dave Vellante and we'll see you next time. (electronic music)

>> Narrator: Live from New York, it's the Cube. Covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. >> And welcome back here to New York City. We're at IBM's Machine Learning Everywhere: Build Your Ladder to AI, along with Dave Vellante, John Walls, and we're now joined by Sam Lightstone, who is an IBM fellow in analytics. And Sam, good morning. Thanks for joining us here once again on the Cube. >> Yeah, thanks a lot. Great to be back. >> Yeah, great. Yeah, good to have you here on kind of a moldy New York day here in late February. So we're talking, obviously data is the new norm, is what certainly, have heard a lot about here today and of late here from IBM. Talk to me about, in your terms, of just when you look at data and evolution and to where it's now become so central to what every enterprise is doing and must do. I mean, how do you do it? Give me a 30,000-foot level right now from your prism. >> Sure, I mean, from a super, if you just stand back, like way far back, and look at what data means to us today, it's really the thing that is separating companies one from the other. How much data do they have and can they make excellent use of it to achieve competitive advantage? And so many companies today are about data and only data. I mean, I'll give you some like really striking, disruptive examples of companies that are tremendously successful household names and it's all about the data. So the world's largest transportation company, or personal taxi, can't call it taxi, but (laughs) but, you know, Uber-- >> Yeah, right. >> Owns no cars, right? The world's largest accommodation company, Airbnb, owns no hotels, right? The world's largest distributor of motion pictures owns no movie theaters. So these companies are disrupting because they're focused on data, not on the material stuff. Material stuff is important, obviously. Somebody needs to own a car, somebody needs to own a way to view a motion picture, and so on. But data is what differentiates companies more than anything else today. And can they tap into the data, can they make sense of it for competitive advantage? And that's not only true for companies that are, you know, cloud companies. That's true for every company, whether you're a bricks and mortars organization or not. Now, one level of that data is to simply look at the data and ask questions of the data, the kinds of data that you already have in your mind. Generating reports, understanding who your customers are, and so on. That's sort of a fundamental level. But the deeper level, the exciting transformation that's going on right now, is the transformation from reporting and what we'll call business intelligence, the ability to take those reports and that insight on data and to visualize it in the way that human beings can understand it, and go much deeper into machine learning and AI, cognitive computing where we can start to learn from this data and learn at the pace of machines, and to drill into the data in a way that a human being cannot because we can't look at bajillions of bytes of data on our own, but machines can do that and they're very good at doing that. So it is a huge, that's one level. The other level is, there's so much more data now than there ever was because there's so many more devices that are now collecting data. And all of us, you know, every one of our phones is collecting data right now. Your cars are collecting data. I think there's something like 60 sensors on every car that rolls of the manufacturing line today. 60. So it's just a wild time and a very exciting time because there's so much untapped potential. And that's what we're here about today, you know. Machine learning, tapping into that unbelievable potential that's there in that data. >> So you're absolutely right on. I mean the data is foundational, or must be foundational in order to succeed in this sort of data-driven world. But it's not necessarily the center of the universe for a lot of companies. I mean, it is for the big data, you know, guys that we all know. You know, the top market cap companies. But so many organizations, they're sort of, human expertise is at the center of their universe, and data is sort of, oh yeah, bolt on, and like you say, reporting. >> Right. >> So how do they deal with that? Do they get one big giant DB2 instance and stuff all the data in there, and infuse it with MI? Is that even practical? How do they solve this problem? >> Yeah, that's a great question. And there's, again, there's a multi-layered answer to that. But let me start with the most, you know, one of the big changes, one of the massive shifts that's been going on over the last decade is the shift to cloud. And people think of the shift to cloud as, well, I don't have to own the server. Someone else will own the server. That's actually not the right way to look at it. I mean, that is one element of cloud computing, but it's not, for me, the most transformative. The big thing about the cloud is the introduction of fully-managed services. It's not just you don't own the server. You don't have to install, configure, or tune anything. Now that's directly related to the topic that you just raised, because people have expertise, domains of expertise in their business. Maybe you're a manufacturer and you have expertise in manufacturing. If you're a bank, you have expertise in banking. You may not be a high-tech expert. You may not have deep skills in tech. So one of the great elements of the cloud is that now you can use these fully managed services and you don't have to be a database expert anymore. You don't have to be an expert in tuning SQL or JSON, or yadda yadda. Someone else takes care of that for you, and that's the elegance of a fully managed service, not just that someone else has got the hardware, but they're taking care of all the complexity. And that's huge. The other thing that I would say is, you know, the companies that are really like the big data houses, they got lots of data, they've spent the last 20 years working so hard to converge their data into larger and larger data lakes. And some have been more successful than others. But everybody has found that that's quite hard to do. Data is coming in many places, in many different repositories, and trying to consolidate, you know, rip the data out, constantly ripping it out and replicating into some data lake where you, or data warehouse where you can do your analytics, is complicated. And it means in some ways you're multiplying your costs because you have the data in its original location and now you're copying it into yet another location. You've got to pay for that, too. So you're multiplying costs. So one of the things I'm very excited about at IBM is we've been working on this new technology that we've now branded it as IBM Queryplex. And that gives us the ability to query data across all of these myriad sources as if they are in one place. As if they are a single consolidated data lake, and make it all look like (snaps) one repository. And not only to the application appear as one repository, but actually tap into the processing power of every one of those data sources. So if you have 1,000 of them, we'll bring to bear the power 1,000 data sources and all that computing and all that memory on these analytics problems. >> Well, give me an example why that matters, of what would be a real-world application of that. >> Oh, sure, so there, you know, there's a couple of examples. I'll give you two extremes, two different extremes. One extreme would be what I'll call enterprise, enterprise data consolidation or virtualization, where you're a large institution and you have several of these repositories. Maybe you got some IBM repositories like DB2. Maybe you've got a little bit of Oracle and a little bit of SQL Server. Maybe you've got some open source stuff like Postgres or MySQL. You got a bunch of these and different departments use different things, and it develops over decades and to some extent you can't even control it, (laughs) right? And now you just want to get analytics on that. You just, what's this data telling me? And as long as all that data is sitting in these, you know, dozens or hundreds of different repositories, you can't tell, unless you copy it all out into a big data lake, which is expensive and complicated. So Queryplex will solve that problem. >> So it's sort of a virtual data store. >> Yeah, and one of the terms, many different terms that are used, but one of the terms that's used in the industry is data virtualization. So that would be a suitable terminology here as well. To make all that data in hundreds, thousands, even millions of possible data sources, appear as one thing, it has to tap into the processing power of all of them at once. Now, that's one extreme. Let's take another extreme, which is even more extreme, which is the IoT scenario, Internet of Things, right? Internet of Things. Imagine you've, have devices, you know, shipping containers and smart meters on buildings. You could literally have 100,000 of these or a million of these things. They're usually small; they don't usually have a lot of data on them. But they can store, usually, couple of months of data. And what's fascinating about that is that most analytics today are really on the most recent you know, 48 hours or four weeks, maybe. And that time is getting shorter and shorter, because people are doing analytics more regularly and they're interested in, just tell me what's going on recently. >> I got to geek out here, for a second. >> Please, well thanks for the warning. (laughs) >> And I know you know things, but I'm not a, I'm not a technical person, but I've been a molt. I've been around a long time. A lot of questions on data virtualization, but let me start with Queryplex. The name is really interesting to me. When I, and you're a database expert, so I'm going to tap your expertise. When I read the Google Spanner paper, I called up my colleague David Floyer, who's an ex-IBM, I said, "This is like global Sysplex. "It's a global distributed thing," And he goes, "Yeah, kind of." And I got very excited. And then my eyes started bleeding when I read the paper, but the name, Queryplex, is it a play on Sysplex? Is there-- >> It's actually, there's a long story. I don't think I can say the story on-air, but we, suffice it to say we wanted to get a name that was legally usable and also descriptive. >> Dave: Okay. >> And we went through literally hundreds and hundreds of permutations of words and we finally landed on Queryplex. But, you know, you mentioned Google Spanner. I probably should spend a moment to differentiate how what we're doing is-- >> Great, if you would. >> A different kind of thing. You know, on Google Spanner, you put data into Google Spanner. With Queryplex, you don't put data into it. >> Dave: Don't have to move it. >> You don't have to move it. You leave it where it is. You can have your data in DB2, you can have it in Oracle, you can have it in a flat file, you can have an Excel spreadsheet, and you know, think about that. An Excel spreadsheet, a collection of text files, comma delimited text files, SQL Server, Oracle, DB2, Netezza, all these things suddenly appear as one database. So that's the transformation. It's not about we'll take your data and copy it into our system, this is about leave your data where it is, and we're going to tap into your (snaps) existing systems for you and help you see them in a unified way. So it's a very different paradigm than what others have done. Part of the reason why we're so excited about it is we're, as far as we know, nobody else is really doing anything quite like this. >> And is that what gets people to the 21st century, basically, is that they have all these legacy systems and yet the conversion is much simpler, much more economical for them? >> Yeah, exactly. It's economical, it's fast. (snaps) You can deploy this in, you know, a very small amount of time. And we're here today talking about machine learning and it's a very good segue to point out in order to get to high-quality AI, you need to have a really strong foundation of an information architecture. And for the industry to show up, as some have done over the past decade, and keep telling people to re-architect their data infrastructure, keep modifying their databases and creating new databases and data lakes and warehouses, you know, it's just not realistic. And so we want to provide a different path. A path that says we're going to make it possible for you to have superb machine learning, cognitive computing, artificial intelligence, and you don't have to rebuild your information architecture. We're going to make it possible for you to leverage what you have and do something special. >> This is exciting. I wasn't aware of this capability. And we were talking earlier about the cloud and the managed service component of that as a major driver of lowering cost and complexity. There's another factor here, which is, we talked about moving data-- >> Right. >> And that's one of the most expensive components of any infrastructure. If I got to move data and the transmission costs and the latency, it's virtually impossible. Speed of light's still up. I know you guys are working on speed of light, but (Sam laughs) you'll eventually get there. >> Right. >> Maybe. But the other thing about cloud economics, and this relates to sort of Queryplex. There's this API economy. You've got virtually zero marginal costs. When you were talking, I was writing these down. You got global scale, it's never down, you've got this network effect working for you. Are you able to, are the standards there? Are you able to replicate those sort of cloud economics the APIs, the standards, that scale, even though you're not in control of this, there's not a single point of control? Can you explain sort of how that magic works? >> Yeah, well I think the API economy is for real and it's very important for us. And it's very important that, you know, we talk about API standards. There's a beautiful quote I once heard. The beautiful thing about standards is there's so many to choose from. (All laugh) And the reality is that, you know, you have standards that are official standards, and then you have the de facto standards because something just catches on and nobody blessed it. It just got popular. So that's a big part of what we're doing at IBM is being at the forefront of adopting the standards that matter. We made a big, a big investment in being Spark compatible, and, in fact, even with Queryplex. You can issue Spark SQL against Queryplex even though it's not a Spark engine, per se, but we make it look and feel like it can be Spark SQL. Another critical point here, when we talk about the API economy, and the speed of light, and movement to the cloud, and these topics you just raised, the friction of the Internet is an unbelievable friction. (John laughs) It's unbelievable. I mean, you know, when you go and watch a movie over the Internet, your home connection is just barely keeping up. I mean, you're pushing it, man. So a gigabyte, you know, a gigabyte an hour or something like that, right? Okay, and if you're a big company, maybe you have a fatter pipe. But not a lot fatter. I mean, not orders of, you're talking incredible friction. And what that means is that it is difficult for people, for companies, to en masse, move everything to the cloud. It's just not happening overnight. And, again, in the interest of doing the best possible service to our customers, that's why we've made it a fundamental element of our strategy in IBM to be a hybrid, what we call hybrid data management company, so that the APIs that we use on the cloud, they are compatible with the APIs that we use on premises. And whether that's software or private cloud. You've got software, you've got private cloud, you've got public cloud. And our APIs are going to be consistent across, and applications that you code for one will run on the other. And you can, that makes it a lot easier to migrate at your leisure when you're ready. >> Makes a lot of sense. That way you can bring cloud economics and the cloud operating model to your data, wherever the data exists. Listening to you speak, Sam, it reminds me, do you remember when Bob Metcalfe who I used to work with at IDG, predicted the collapse of the Internet? He predicted that year after year after year, in speech after speech, that it was so fragile, and you're bringing back that point of, guys, it's still, you know, a lot of friction. So that's very interesting, (laughs) as an architect. >> You think Bob's going to be happy that you brought up that he predicted the Internet was going to be its own demise? (Sam laughs) >> Well, he did it in-- >> I'm just saying. >> I'm staying out of it, man. >> He did it as a lightning rod. >> As a talking-- >> To get the industry to respond, and he had a big enough voice so he could do that. >> That it worked, right. But so I want to get back to Queryplex and the secret sauce. Somehow you're creating this data virtualization capability. What's the secret sauce behind it? >> Yeah, so I think, we're not the first to try, by the way. Actually this problem-- >> Hard problem. >> Of all these data sources all over the place, you try to make them look like one thing. People have been trying to figure out how to do that since like the '70s, okay, so, but-- >> Dave: Really hasn't worked. >> And it hasn't worked. And really, the reason why it hasn't worked is that there's been two fundamental strategies. One strategy is, you have a central coordinator that tries to speak to each of these data sources. So I've got, let's say, 10,000 data sources. I want to have one coordinator tap into each of them and have a dialogue. And what happens is that that coordinator, a server, an agent somewhere, becomes a network bottleneck. You were talking about the friction of the Internet. This is a great example of friction. One coordinator trying to speak to, you know, and collaborators becomes a point of friction. And it also becomes a point of friction not only in the Internet, but also in the computation, because he ends up doing too much of the work. There's too many things that cannot be done at the, at these edge repositories, aggregations, and joins, and so on. So all the aggregations and joins get done by this one sucker who can't keep up. >> Dave: The queue. >> Yeah, so there's a big queue, right. So that's one strategy that didn't work. The other strategy that people tried was sort of an end squared topology where every data source tries to speak to every other data source. And that doesn't scale as well. So what we've done in Queryplex is something that we think is unique and much more organic where we try to organize the universe or constellation of these data sources so that every data source speaks to a small number of peers but not a large number of peers. And that way no single source is a bottleneck, either in network or in computation. That's one trick. And the second trick is we've designed algorithms that can truly be distributed. So you can do joins in a distributed manner. You can do aggregation in a distributed manner. These are things, you know, when I say aggregation, I'm talking about simple things like a sum or an average or a median. These are super popular in, in analytic queries. Everybody wants to do a sum or an average or a median, right? But in the past, those things were hard to do in a distributed manner, getting all the participants in this universe to do some small incremental piece of the computation. So it's really these two things. Number one, this organic, dynamically forming constellation of devices. Dynamically forming a way that is latency aware. So if I'm a, if I represent a data source that's joining this universe or constellation, I'm going to try to find peers who I have a fast connection with. If all the universe of peers were out there, I'll try to find ones that are fast. And the second is having algorithms that we can all collaborate on. Those two things change the game. >> We're getting the two minute sign, and this is fascinating stuff. But so, how do you deal with the data consistency problem? You hear about eventual consistency and people using atomic clocks and-- Right, so Queryplex, you know, there's a reason we call it Queryplex not Dataplex. Queryplex is really a read-only operation. >> Dave: Oh, there you go. >> You've got all these-- >> Problem solved. (laughs) >> Problem solved. You've got all these data sources. They're already doing their, they already have data's coming in how it's coming in. >> Dave: Simple and brilliant. >> Right, and we're not changing any of that. All we're saying is, if you want to query them as one, you can query them as one. I should say a few words about the machine learning that we're doing here at the conference. We've talked about the importance of an information architecture and how that lays a foundation for machine learning. But one of the things that we're showing and demonstrating at the conference today, or at the showcase today, is how we're actually putting machine learning into the database. Create databases that learn and improve over time, learn from experience. In 1952, Arthur Samuel was a researcher at IBM who first, had one of the most fundamental breakthroughs in machine learning when he created a machine learning algorithm that will play checkers. And he programmed this checker playing game of his so it would learn over time. And then he had a great idea. He programmed it so it would play itself, thousands and thousands and thousands of times over, so it would actually learn from its own mistakes. And, you know, the evolution since then. Deep Blue playing chess and so on. The Watson Jeopardy game. We've seen tremendous potential in machine learning. We're putting into the database so databases can be smarter, faster, more consistent, and really just out of the box (snaps) performing. >> I'm glad you brought that up. I was going to ask you, because the legend Steve Mills once said to me, I had asked him a question about in-memory databases. He said ever databases have been around, in-memory databases have been around. But ML-infused databases are new. >> Sam: That's right, something totally new. >> Dave: Yeah, great. >> Well, you mentioned Deep Blue. Looking forward to having Garry Kasparov on a little bit later on here. And I know he's speaking as well. But fascinating stuff that you've covered here, Sam. We appreciate the time here. >> Thank you, thanks for having me. >> And wish you continued success, as well. >> Thank you very much. >> Sam Lightstone, IBM fellow joining us here live on the Cube. We're back with more here from New York City right after this. (electronic music)

>> Narrator: From the Computer History Museum in Mountain View, California, it's theCUBE. On the ground with Accenture Labs 30th anniversary celebration. >> Okay welcome back everyone. We're here for a special on the ground presentation, our Accenture Labs 30th year celebration of being in business at the Computer History Museum in Mountain View, California, the heart of Silicon Valley. I'm John Furrier. Our next guest is Yvonne Wassenaar, who's the CEO of Airwave. Good to see you, Cube alumni, welcome back. >> Thank you so much, I'm happy to be here. >> So your integral executive at this event here. You've worked at VMware, you've worked at New Relic. You're now at Airware. What do you guys do? First explain what Airware is because this is fascinating. >> Yeah, yeah, yeah. Airware is the most fun and impactful company on the planet. I'm a bit biased, but fundamentally I explain it as commercial drone software analytics. And the reason I say that is commercial drone is important because it's not just hobbyists, it's businesses using drones to collect data, but ultimately the important part is what do you do with the data? And we provide cloud based software analytics machine learning AI to derive business insights from what they collect. >> And drones are very practical, other than my kids loving them, put the Go Pro on it, but you can go, instead of saying go drive out and check that meter or you know, go out and take those trash out of the power lines, there's all kind of applications that drones could do with not only technical, but also getting data, visual data. So what is that looking like these days because it sounds very magical and fantasy like? What are some of the applications? >> It's a great question, and I want to start with what are some of the changes that have enabled drones to go from personal use to commercial use? The first thing is the technology, and so if you think about the drones, it's kind of like the cell phones 10 years ago when the iPhone came out. It didn't do that much compared to today, but the advancement has been amazing. So we actually had an innovator, one of our customers, duct tape a cell phone to the bottom of a drone like four or five years ago to get the visual imagery that he needed to drive insights. Now you can just buy from DJI or senseFly, really powerful drones, so you're seeing a huge uptake in what drones can do, and then on the other side, you're seeing the ability with cloud based analytics to get insights in things such as, think about it, insurance, rooftop inspection. You don't have to climb a two story steep on a ladder. You can fly a drone up, less time, more safe, and you get the historical information. Mining and quarrying, we do a lot in that space. Stockpile measurement. It's really fascinating all the things you can do. It's almost what do you not do. >> So I've been fascinated with drones ever since two years ago when Amazon had that big hype announcement where packages will be delivered to your home, and everyone can relate to that because they know Amazon delivers packages, but who's going to deliver, how does that work? I mean is there like a name space for like airspace? That's a hard compute challenge, so how will you guys deal with the spacial imagery aspect of it because this is fascinating because a new set of companies are redefining what was an old, established, boring, static industry. I mean Hoover remaps New York City every five weeks, or some number. >> Well I was going to say, what's important is you have the geo spacial coordinates, and so what we do is to actually align the images we take to geo spatially where they are. We use GCPs to do that, and then we know exactly, to the pinpoint, how to stitch images together, how to relate images over time, so actually that piece is quite easy. The harder part is when you're doing like large quarries or commercial inspections, just the volume of data you're collecting and being thoughtful on how you can upload that, process that, that's the more interesting and challenging part. >> And certainly data ingestion's huge, so given that, I've got to ask you the internet of things questions. Internet of things, the intelligent edge. Drones are moving, so they're real time. They're going to the edge of the network, they are the network, and they're pushing the edge out. How are you looking at the IOT? What's your perspective of the current IOT landscape? Intelligent, dumb, not yet defined, hasn't been to school yet? This is a big topic. Microsoft's talking about it, we've been talking about it on a research side, an intelligent edge. >> Yeah, I think we are just on the cusp of what is possible, and to me, I think about the true power being of marrying that visual data that comes from the drone with the other internet of things data. So for example, if you think about, in the aggregate space, in quarries and mining, where we play a lot. You have a lot of big equipment that has a tremendous number of sensors around, fuel efficiency and what's going on with the machine. You can map that against the hull roads that they're driving and other elements, you know that you can see from the sky. You can start to redesign your roads, you can start to get huge fuel efficiencies and other benefits, so to me the magic is really in marrying the different data sources, which is now becoming more possible as like broader technologies in the cloud and analytics of all. >> So I've got to ask you some technical, kind of high level questions. You don't have to go deep under the hood, but because you worked at VMware, you know the federation which is EMC. You guys are helping the storage guys out big time because there's a lot of data coming in. So two questions. How do you move all that big data, big fat data, through little pipes called the airwaves into the storage? What's the strategy? Is there any kind of emerging trends you see with respect to architecture? >> Yeah, so we actually spent a lot of time thinking about how you pull the huge, vast amounts of data and get it into the cloud. I'm not going to give away all of our secrets there, but what I will fundamentally say is we are big users of the cloud, so we're taking advantage of somebody else building up big data centers and their ongoing reduction in cost. Storage only gets cheaper and cheaper, and so for us, what we're really focused on is the processing power and what you can do in the clouds you put your data into. >> So cloud helps you? >> Totally, yeah, yeah, yeah. >> What would life be like without the cloud? Would you be in business? >> It would be really hard, and it would be hard on two fronts. One because it takes a lot to build and scale up your own data centers as a company today, particularly as a startup, but I think even more importantly, the ability to do, you know, training of these AI algorithms on large datasets. You want to be able to look across datasets, and that's most easily done aggregating the cloud. >> So you guys are cloud native? >> Yes. >> So what's your advice to CIOs as they look at their hybrid or private cloud, or on premise IT that's not even private cloud? What, these guys are trying to transform fast. Accenture Labs and others are helping them. What does a CIO have to do to get to the benefits of being that agile? >> Yeah, I think it's a great question, and when I was at New Relic, I was the CIO, so I have a little bit of experience in it. >> John: Trick question. >> What I would say is it is hard and I feel the pain. You have a lot to do to run the day to day business, but ultimately I think being really strategic and carving out the time and the big initiatives, and fundamentally it comes down to, all your new stuff should be in the cloud. The stuff that's really critical that's on prem that you can convert, you should do it, and the rest you got to get rid of it. You can't be held back by legacy because it will only prevent you from innovating and somebody else will. >> And do you see CIOs ultimately going to an operating model that looks like cloud even though it might be on prem? >> It does, particularly some of the larger companies, and for certain applications where you have to have, for whatever reason, data within the company, but it will be more utility based, it will be more burst capacity. You'll see more sharing as the tools and monitoring gets better. >> So I got to get your take. So as AI comes down the pipe, you're in analytics, it's a big part of your business. >> Yeah, yeah, yeah. >> You understand analytics across your career. As jobs get automated away, we have a survey, and Market Size and Wikimon just did that says that by 2025, 150 billion dollars of non differentiated IT labor is going to go away and shift to other high value activities. So automation is going to replace those non differentiating jobs, labor. Okay, that means some other things are going to happen. So you can almost connect the dots and say software, analytics, some sort of new model. How does a company do analytics? Because what are those new value creation, you started a company on drone trend, real application, analytics is a differentiator. How does a company use analytics to help them figure out a differentiating strategy for their future? >> So I think it's a couple things. One is how to use analytics and automation to do what you currently do better, faster, cheaper. The more interesting thing is what you were talking about is if machines are doing that for you, if software's doing that for you, you have more time to think about well what's that next set of more advanced analytics I might do? Or how might I translate into better customer service? Or what's that new business model? So I think rather than jobs going away, it's really you know kind of like in the banks. The ATMs didn't get rid of the bank employees. It just gave them the ability to be personal advisors and take other. >> And they open up more branches. >> Exactly. >> And it's more people. It's actually helped create jobs. >> Exactly. >> Kind of that fallacy kind of goes away. Okay, we've got a little bit of time left. Do a quick commercial on what you guys are doing. Give a plug for Airware. How many employees do you guys have, what stage you're at, what are you guys looking to do? You're hiring, what do your customers look like, who is your customer? Take a minute to talk about your company. >> Yeah, so like I said, Airware's an amazing company. We're about six years old. We're Series C. We've got great investors and backers with Andreessen, Kleiner, Perkins, John Chambers is on our board. We're about 100 people. We've got global operations, both in EMEA and in the US. The beautiful city of Paris as well as San Francisco, so hard to beat that, and fundamentally what we're focused on is global enterprise commercial drone software analytics. And I call it an enterprise because part of the reason I ended up at Airware is I spent 17 years at Accenture. I understand what it takes to sell into enterprise. I know what they're looking for in terms of security, in terms of scalability, deployment, ease of use, and so bringing that, not just fun innovative experiments and innovation departments, but scaled deployments, and we predominantly focus on insurance and agriculture, mining, and construction right now, but we're building a platform that can be leveraged across industries, and so the real value add is how we reassemble the components to quickly innovate for other industries as well. >> I know we got time to break here, but one final question. We're going to be at the Grace Hopper celebration this year for our fourth year as part of our women in tech celebration. With all the recent Silicon Valley scandals around women in tech, I got to ask you. You've been in the business for a long time. You know, you've seen a lot of stories. I'm not going to ask you to share any specifics. What does the future have to look like to get through this novel of the generational shift that's happening, a new generation's coming on board. What kinds of norms and practices would you like to see, and any comment or color you can share on what is the preferred outcome of the current situation? >> Yeah, so I deeply believe that for companies to be competitive, you have to be diverse in perspective skillset and your employee base, and this war for talent, if you're only going after a certain profile, you're going to lose. So I think the winning companies will diversify. I'm on the board at Harvey Mudd, who's done amazing work increasing the number of women in STEM. They had more than 50% of their computer science majors were female last year, so it's definitely doable. I think we all have a lot of unconscious bias, and fundamentally what's going to shift is having more role models, and quite frankly having more white male sponsors. I mean John Chambers is a huge sponsor of mine and that makes a big difference, and so I think we need. >> And including men in the conversation. >> Totally. >> Is a really important part of it. >> Yeah, yeah, yeah, yeah. I'm 100%. My best sponsors have been men, and that's what we need is that community to make a difference. >> Yvonne, thanks so much for sharing your insight and data here. Accenture Labs celebration. Your role at Accenture, you're working with them, you've worked with them. >> Yeah. >> What's the take here? >> I'm super excited to be here. I was at Accenture for 17 years starting in 1990, so I'm old, and I got to grow up with the labs, and so. >> Were they Arthur Anderson or were they Accenture Consulting at that point? >> It was Anderson Consulting. >> Anderson Consulting. >> I'm that old, it was Anderson Consulting. But I'd say the value of the labs is it's hard when you're a big enterprise company to reimagine the future, and so having places like Accenture Labs where you can see what the possible is and you have somebody experimenting with you is really powerful, so. >> And you've got a good team of people with you. The cloud, really good timing to have a cloud operation too. >> Yeah, yeah I'm excited to be here. >> Yvonne, thanks so much. Cube coverage here at the Computer History Museum. I'm John Furrier with theCUBE, on the ground for Accenture Labs, 30 years. The next 30 years ahead of us. A lot of exciting things, AI, new workforce, great action happening, drones. First of all, the drone racing leak, by the way, is really popular in my household. We're going to have drones in theCUBE >> Yvonne: Maybe we can connect you. >> With Cube coverage with drone cameras, coming soon. Thanks for watching, we'll be right back. (upbeat instrumental music)

>> Announcer: From The Computer History Museum in Mountain View, California, it's theCUBE On The Ground with Accenture Labs' 30th anniversary celebration. >> Welcome to a special CUBE On The Ground presentation of our coverage of Accenture Labs' 30th birthday party. They've been in business for 30 years. Accenture is doing some great things from here, 30 years ago, to the future. Future's all about AI, blockchain, you name it, virtual reality, augmented reality. I'm John Furrier with theCUBE. Our next guest is Joe Mikhail, who's the chief revenue officer of a company called Meta. Welcome to the conversation here at the Accenture Labs party. >> Thank you, John, and congratulations to Accenture. >> They have this theme, Magical, but really, it is a magical time. At my age, I've been in this business long enough, it's like I wish I was 20 again, because the technology is really amazing. Augmented reality, you guys do a lot of new stuff. Tell us what your company does, and you guys are doing some really cool stuff. >> Absolutely. We're really pioneering in augmented reality. For those who don't really understand augmented reality, it basically overlays digital data and virtual optics in the real world. With that comes, really, a change in paradigm of what's possible. Our forte is really in being a spatial interface company. We're not only changing the fidelity of the images you see in augmented reality, but how you interface with them, naturally based on neuroscience. >> Joe, first, take a step back, 'cause a lot of folks here in Silicon Valley, they all know what AR is, or augmented reality, something analyst relations work. But augmented reality is the big future. I always say AI stands for, not artificial intelligence, but augmented intelligence. That's what software's doing. What's your definition of augmented reality? >> Augmented reality is the ability to really change how man/machine interface around information, objects outside of 2D panels, and bringing the digital into our world. >> Let's talk about your company, Meta. You guys are doing some pretty cool stuff. Your CTO's not here, which, we'll get him on theCUBE soon. If you're watching, we'll get you on. But there's some cool stuff going on around visualization. I mean, we've covered big data since the day Hadoop was born 2009, 2010 timeframe. Visualization is key, but now, when you go to the next level, 3D, holograms, this is the future. The user interface is going to be augmented at work or at play. What are you guys doing? >> Absolutely, many things when it comes to data visualization. First of all, the third dimension, obviously adds a new way to see data, so, obviously, everything going from a 2D data analysis, you add a dimension, that gives you, obviously, added productivity. But in addition to that, you know, visualizing concepts. Mind-mapping, being able to correlate ideas, and not just data points. And, again, product design cycles and so on, productivity increases. Thirdly, ideation. Taking all that data, getting a 3D model with all its complexity into a simple form that we can collaborate around and design. >> You know, the next generation of users that are coming through the system, if you will, young kids, they're gamers. They love graphics. We're living in kind of a gaming culture, if you will, not to say gaming, literally, but per se, the interface is very rich in graphics, very rich in data. How is that going to impact CIOs? 'Cause they are looking at a old world of IT, put the servers on the racks, move the packets through the network. Now they have an opportunity with mobile, and now with global internet to put things out there like AR, like blockchain, smart contracts, AI. >> I think it's definitely an area that all CIOs should be looking at today, in many aspects. Number one, just like mobile, bring-your-own-device came into the office space. There will be, obviously, an impact from not just productivity solutions in the office, but as we get to consumer and AR, dealing with that and the implications of that. But, a more important, pressing issue for CIOs would be the fact that this is the future of compute. There is not a need anymore for 2D panels, or in the near future for 2D panels and keyboards and mouse interfaces, and how does that change IT support and, again, data sharing, collaboration, and all these-- >> And we see Siri, voice-activated, that's pretty classic. Throw the old movie Minority Report out there, where you're using your hands out there in the 3D space. This is an interface. >> Yes, it truly is. >> How real is that? I mean, come on, tell us! >> It's real, it's here, it's now. You can get a demo today for the audience. Soon, we can definitely invite you and get a demo. It is here. We're able to interact naturally today. We're on second-generation product. We have the widest field of view, which truly gives you immersion. You can walk around a hologram. You can stretch a hologram. You can surround yourselves with unlimited 3D images and panels and windows. >> So, what's the applications? What does this mean for the typical person out in the real world, whether they work in an enterprise, or a business, or a consumer? >> Absolutely. Early adopters right now are in business, enterprises. High-ROI type of applications and product design, so, rapidly iterating on concepts and ideas, getting all the way to sales and marketing, so once you have that design, then, how can you sell it and demonstrate it. All the way to maintenance, training, et cetera. That's the early adopters. Education is next, very close by. In the near future, and then, of course, we're thinking and trending towards consumers. What does shopping look like in the future? >> Check out Meta. It's a cool company. Now, Accenture Labs are having their party, and Accenture's been around for a while. I'm old enough to remember Arthur Andersen, the Big Six accounting firms, Accenture Consulting. These guys are not Johnny-come-latelies. They're doing some cool stuff. What's your role with Accenture Labs? You're on a panel here at this event, it's kind of a celebration. They're bringing the magic to life, talking about the magic of AI and cool things. What are you guys doing here, and what's Accenture Labs doing? >> Yeah, absolutely. We've been in collaboration with Accenture Labs for a little while, and it's been very, very exciting and productive. Number one, we're aligned on vision and strategy, so, currently, it's productivity. We're supporting productivity, we are going to develop a new platform, and so, for example, we've done a study together where we measured basic instructions around a LEGO, this was for the public, around building a LEGO piece used in our headset, using three-dimensional instructions versus 2D instructions, and Accenture brought that magic of quantifying productivity, and it was proved to be 20% faster with respect to instruction and training. >> So, Accenture has some chops, here, technically. >> Absolutely, absolutely. They do. (both laughing) And in the future, I mean, they're a big part of our ecosystem. This is what we're an enabler. We're a spatial interface-- >> What is the ecosystem for AI? That's a good question, 'cause people want to know, like, it's in a new, emerging area. Young kids are going to love this. New software development's coming in. What does the ecosystem look like in this new AR area, and what's the hiring profile? >> Yeah, that's a good question. Let me focus on ecosystem. I would say 50% of our current customers are developers, so the development community is adopting AR and they're building some really interesting and cool things. But the ecosystem comes from developers' content, so there's a lot of content developers, you know, high-fidelity 3D models. Enterprises are consuming all of this, and then channel partners, system integrators such as Accenture that are seeing the opportunity and bridging that gap for a lot of our corporate customers that are still forming their strategies. >> Joe Mikhail here, the chief revenue officer of Meta. I got to ask you, what percentage of your employees and customers are gamers? High amount, medium, low? Got to be a lot of gamers. >> There are some. Obviously, we integrate with Unity. A lot of our developers have come from that world, but our customers, we're a productivity company, and all of our customers are corporates at this time. Of course, we're interested to see what gamers can do on our platform. >> What's the low-hanging fruit for enterprise with respect to AR, because this is the question. No one debates the future. They see some augmentation coming on, obviously wearables, things of that nature, but software's going to power it all. What is the use case for enterprise? What's the low-hanging fruit? >> The lowest-hanging fruit is 3D CAD visualization in the product design cycle. That's just the lowest-hanging fruit right now. And then, training and education. >> You guys excited? >> We are very, very excited. The market's huge. >> All right, final question for you. For the folks that don't know the AR world, what is the future of AR going to be? What's the impact on society, what's the impact on daily lives of people with augmented reality? >> I think there are many, many impacts. One of our core values is technology serving humanity, so for us, it's very important to remove the barriers of devices coming between you and me, and being able to just look up content directly and interact with that. I think that's going to change how we think, how we collaborate, and then, of course, life sciences is huge, so there's a lot of companies starting to look at the future operating system, and the empathy that could come between a doctor and a patient looking at a case instead of just talking, you know? >> Joe, great, thanks for coming on. I'll give you a quick last word, here. What are you guys looking for as a company? You hiring, what's the strategy, what's the plan? Give a quick soundbite for what you guys are doing. >> Absolutely. We're growing. The market demand is huge, and we are hiring. We're looking for engineering, smart engineers that are interested in the space. We are growing on the sales and marketing side. We are absolutely interested in being part of our family, but I would say the biggest interest is in ecosystem partnerships. >> How long are you around for? >> Five years. >> Five years. Congratulations, Accenture Labs, 30 years celebration, where all the magic's happening, that's the theme. They got a magic show. We couldn't get video of that. They wouldn't let us record it. Joe from Meta, chief revenue officer, thanks for sharing your insight here on theCUBE. Appreciate it. >> Thanks, John. >> There'll be more coverage here at Accenture Labs' next 30 years. This is theCUBE coverage. We'll be right back. Thanks for watching. (upbeat music)

>> Announcer: From the Computer History Museum in Mountain View, California, it's the Cube. On the ground with Accenture Labs 30th Anniversary Celebration. >> Hello and welcome back to our special on the ground coverage of Accenture Labs 30 year celebration. Here's to the next 30 years is their slogan and I'm John Ferry with the Cube and I'm here with Marc Carrel-Billiard who's the Senior Manger that runs R&D Global for Accenture Labs. Welcome to the Cube conversation. Thanks for joining me. >> Marc: Thanks, John. >> So, I got to ask you, Accenture 30 years, they weren't called Accenture back then, it was called Arthur Anderson or Anderson Consulting and then it became Accenture, now you got Accenture Lab. But you have had labs all throughout. >> You're right. I mean, it's pretty amazing. And I think this is absolutely right. So we had this organization for 30 years, believe it or not. And that organization is doing applied research. So what we do is we leverage new technology innovations and everything to really solve business challenges or societal pacts and social changes and everything. >> State of the art back then, if I remember correctly my history was converting an S&A gateway to a technet to a TCP/IP network. >> Yeah we just improved a little bit. We went to quantum computing, to Blockchain, to different type of things like that. >> What a magical time it is right now >> It is magic. >> Share some color on today's culture, the convergence of all this awesomeness happening. Open source, booming. Cloud, unlimited compute. You have now more developers than ever, Enterprise is looking more and more like consumers. So a lot of action. What's the excitement? Share the cutting edge lab's activity. I think you said something absolutely right. I mean, I think there's a combinatorial effect of two different technology working very well together, and is a compression on time, all those technology waves that are maturing very fast. So one thing that we been doing is a great example for that, is quantum computing. You heard about quantum computing, you know? >> Of course. >> That's the new Paradigm of computing power. Leveraging like, quantum mechanics, you know? I mean it's really amazing stuff. And believe it or not, we've been working with D-Wave, they have a quantum computer in Vancouver, and a companies called 1QBit, it's a software company, and we've built, on top of that, an algorithm that has molecule comparison. And we worked with Biogen, a pharmaceutical company, to work on this. Now, the really staggering thing about it, is that we talked about it like six months ago, we build the pilot in two months time. Done. And then now, I mean, it's already made. >> Well, this is amazing. This is what highlights to me what's exciting. What you just described is a time frame that's really short. >> That's right! >> Back in the old days, it was these projects were months and months, and potentially years. >> Absolutely. >> What is the catalyst for that? Is it the technology leverage? Is it the people? Is it the process? All three? What's the take? >> I think it's all three. I would say that definitely the technology, as I said, get combined faster. You said very right, there's a lot of capability in term of high performance computing we can get through the Cloud, the storage as well. The data that we're going to be accessing, and then I think the beauty is that, putting all the people together for the quantum work. We had mathematicians, we have from Biogen, we have our own labs, and all people together, they make the magic happen. >> 30 years ago, just a little history 'cause I'm old enough to actually talk about 30 years ago, the Big Six Accounting Firms, accounting firms, ran all the big software projects. How ironic is that, that today Blockchain disrupts the even need for an accounting firm, because with Smart Contracts, Blockchain is turning out to be a very, very disruptive operation in technology, because you don't need an accounting firm to clear out contracts. Blockchain is very disruptive. What are you guys doing on Blockchain? >> You're absolutely right, John. And you know, the first thing. So, we have seven labs in Accenture Labs. And we have one lab didn't get it on Blockchain, and it's Sophia Antipolis inside of France, where I'm from, by the way. We're doing a lot of things with Blockchain. A lot of people are thinking about Blockchain as a system that's going to regulate, basically, transfer a transaction, financial transaction. We want to take Blockchain to the next level. And one thing we're doing, for example, We're using Blockchain for Angels. How we're track, basically, donation you're going to do. We going to use Blockchain for-- >> Well that's because people want to know their money's actually going to good. >> That's right! That's right! >> Not to scams that have been out there. >> You got it. >> We going to use Blockchain as a DRM system, Digital Rights Management system. We're going to use that in manufacturing industry, in many industry, and it goes on and on and on. >> What is the big buzz right now with Cryptocurrency? You're seeing a lot of these ICOs out there. Are those legit? In your mind, is it just a bubble? Is it just a normalization's going to come, what's your take on Initial Coin Offerings? >> I think, to be honest with you, I think this is a progress with thing. I mean, we discuss about Blockchain and everything. We see some trains going there. I think it's accelerating as well, because it's got a lot of take up and everything. We see, also, the world changing, and I think we need to look at the geo-political context of the world and what could happen. So I think those kind of new regulation, the way it's going to work. I mean, it's coming on time, people's going to leverage it, so I think it's not some fad stuff. This is something that's going to stay. >> It's just a Wild West. >> But it was, exactly. Right now, we need to work on the right standard, we need to figure out how it's going to work and everything. >> What is the exciting things that you see out there right now? I mean, Blockchain just kind of gets us excited 'cause you can imagine different new things happening. But the clients that I talk to, customers, your clients, or CIOs, they have to reimagine the future. >> That's right. >> With preexisting conditions called legacy infrastructure. >> Exactly >> Legacy software. How do they get the best of the magic and manage the preexisting conditions? >> So, there's a lot of innovation in term of software development. You take energy in everything that we have, basically, to connect to your legacy, and leverage it as much as you can. You know, there's a big progress in artificial intelligence today. I mean, I've live a lot of winters of artificial intelligence. I think finally, maybe there's going to be some spring. Why? Because of what we talk about. The iPad from one's computing the data available, and then also, some new type of algorithm like deep learning and everything. That data that is somewhere into this company called the Dark Data, people is going to be able to leverage it, and then make those artificial intelligence systems even more intelligence, smarter, and everything. So, legacy's here, but we're going to leverage it, and we're going to give a second life to those legacy environment. So those technology like artificial intelligence, new analytics and all those different things. >> So I got to ask you a kind of politically hot question, which is the digital transformation. >> Yes. >> So there's doubt we're in a digital transformation. No brainer. Yet, I go to conferences over and over again, and I see Gartner Magic Quadrant. I'm number one on the Magic Quadrant, and everybody's number one in the Magic Quadrant. So, the question is, what's the scoreboard of the new environment? Because, if you use the old scoreboard, and the world's horizontally scalable, you're going to have a blending of Magic Quadrants. So there's going to be a disruption, and that's causing confusion to the CIOs and CXOs because you got Chief Data Officer, Chief Security Officer, you got no perimeter for security, you have quantum computing, you have Cloud. So, people are trying to squint through all the nonsense and saying, how do you measure success? >> Yeah. >> Certainly customers is a good one. >> I think this is the typical question. I mean, this whole digital transformation, I understand that is important, and we need to understand. I mean, Accenture, and especially the lab, it's all about result. And you know what? The mission of the lab is new, it's applied, is now. New technology applied for real challenges, and I want to deliver it now, and I want to work for six months. So my word is that our research is outcome driven, and that's exactly what we're seeing. So, I told you about the quantum computing, and I have other example where we are really laser-focused on making an outcome. I think that's where-- >> So, to your point, people shouldn't buy promises. >> No. >> They should buy results. >> That's right. >> So, Peter Barris, who runs our research, said to me, and I asked him the question, he goes, ah, that's just a bunch of BS. The ultimate metric is how many customers you have. So, someone should be touting their customers. >> Sorry? >> They should be touting their customers, not some survey. >> No, absolutely. And I'm really for that. >> I want to tell you something, that I'm a very pragmatic person. I'm coming from the field, where I was serving 400 clients doing, every day, project delivery, you know? >> John: God bless you. >> And I've always been doing innovation at the same time, but my view was that innovation needs to be scalable, it needs to be tangible, it needs to be outcome driven. So again, this is really the matter of the lab, and if you look at how the lab works with the rest of the organization of Accenture, this is exactly what we're doing. We connect with our studio, where we can do prototyping front of the eyes of our client. We connect with Open Innovation, where we connect with the best start ups in the world. I think, you remember when I told you combinatorial effect. There's a combinatorial effect with technology that is a combinatorial effect with people. If you put the people from start up, the best guys from the lab, the best guys from the studios and everything, that's where the magic happens. >> So this is a new configuration? >> We collect the innovation architecture. >> So this is a scalable model for being agile, and the results are what? Faster performance? >> Faster performance, innovative performance, and tangible outcome. >> Okay Marc, you're an excitable guy, I like talkin' with you, what are you most excited about right now in this world that you're living in? So, I told you about the technology, and there's one thing that the lab is doing, and we'll be launching that this year, and we'll continue expanding. It's what we call Tech For Good. Tech For Good is how we're going to apply technology to change society. What we're going to do for fighting hunger in India. How we're going to give situational awareness to blind people using augmented reality immersion learning. That keeps me awake at night, because this is technology for best usage, it allows for our people to sleep well at night. My kids are proud of me, and I think we can-- >> Change the world! >> That's right! We can attract great people. >> Alright, final question. Here at the celebration, at the Computer History Museum in Silicon Valley, what's the big scene here? Share with the folks who are watching, who aren't here, what's happening. >> I think, first of all, the venue is amazing. Computer Historic Museum is probably one of my favorite museum here in Silicon Valley. I mean, you need to understand that, 15 years old I started to work on a IBM 360 of my uncle, so the machine over there, I know it. I worked on it. And when I see the completed progress where we are today, when we see the Cray, when we see the quantum and everything, I feel so lucky that we're celebrating 30 years. Now I'd to go for the next 30 years of the lab. That's what I want to do. >> Let's get that on our next interview. Marc, thanks for sharing, here's to the next 30 years. This is the Cube coverage of Accenture Lab's 30 year celebration. The Computer History Museum, I'm John Ferry. Thanks for watching.

>> Narrator: From the Computer History Museum in Mountain View, California, it's The Cube, on the ground with Accenture Labs' 30th anniversary celebration. >> Hello, everyone, welcome to the special coverage of The Cube, on the ground here at the Computer History Museum in Mountain View, California, the heart of Silicon Valley. It's The Cube's coverage of Accenture Labs' 30th year celebration. I'm here with Paul Dougherty, the chief technology and innovation officer at Accenture Labs. Welcome to The Cube conversation. Thanks for joining me. >> It's great to be here. >> So first I want to toast you guys to 30 years from turning to an accounting firm, Arthur Anderson, to Accenture Labs Consulting. Guys are really changed. Congratulations to all your success. Thanks for having us. >> Yeah, thanks, it's been an incredible journey. If you think back in the 30 years, it's the 30th anniversary of Accenture Labs, and the transformation of our company to now be an innovation-led company, leading in IT services and IT innovation, and with the amazing innovations that are happening in technology, it's a great time to be doing what we're doing. >> So the theme here at the party is magic. There's a magic show going on. We can't get coverage. It's a little private event, probably some G-rated, probably ... >> Lots of magic. >> A lot of magic. But there's magic right now. We were commenting earlier, before you came on, about, you know at my age, I love this innovation cycle, but if I was 20 years old, I'd really be excited. There's so much going on. It's really magical. You've got the convergence of infrastructure, cloud, software. You guys have been on all sides of innovation, from the mini-computer boom, all the way now through now, where AI and software and now data science is coming together. What's the exciting thing for you right now? Because it's beyond software eating the world, it's beyond data eating software. This is real applications. >> Yeah, this is ... We're at an era where technology is the driving force behind every business. There was a survey recently of CEOs, and they asked CEOs how do they view their business, and 81% of CEOs, 81%, said their company's a technology company. And that was a cross-industry survey. And that's why it's an exciting time, because the option we have as Accenture is to work with any company, and every company, and help them transform, change their business, and lead them through the transformation to deliver technology-enabled digital products and services. And that's why it's an exciting time. >> What I find exciting about these global system integrators, as they're now called, is that you guys have always been a consultative organization to customers, helping them through their journey of that generational shift. Now it's interesting, with cloud computing, you guys are not only just advising, you're delivering services. A mindset transformation as well as talent, technology, process, and people. How are you doing it? What's the secret formula? >> Yeah, absolutely. I mean, what we found, the reason we've driven our business model in that direction, is our clients need help throughout the cycle. So we help with Accenture strategy, with advising our clients. We help with Accenture consulting, on helping our clients transform. Accenture digital, bring the digital capabilities in. Accenture technology, building the solutions in. Accenture operations, providing business process, infrastructure, and cloud operations. So, we've found that our clients, they need help with it all. They want to understand where to take their business, they want to understand how to get there, and they want somebody to help them manage their business as they do. And that's why we've taken the business in that direction. >> Not to give you guys a lot of props, but I do want to give you guys kudos, Accenture, Accenture Labs, is that all of folks might not know, or some, you guys probably do know, you've accumulated a lot of data scientists over the years. You've got thousands of data scientists, a lot of talent coming in. Accenture Labs is a booming operation, it's not just a throwaway lip-service kind of operation for customers, to say "Hey, we got some smart people." You guys have actually have a real organization. What are some of the cool things that you guys are doing? Can you give some examples? >> Yeah, let's just step back and talk about Labs a bit, and then I'll give some examples. We've been at Labs now for 30 years, hence the celebration we're talking about, and it's thousands of patents, it's billions of dollars of impact on the revenue of our business. And really, you're driving innovation that sets us ahead in the marketplace. And it's a fabric of a global organizations. We have labs here in Silicon Valley. We have labs in Washington, DC, that focus on security and other things. We have labs in Dublin, Ireland, in Tel Aviv, in Bangalore, India, in Beijing, in Sophia Antipolis in France. And it's that global infrastructure that allows us to tap into the innovation, I think in the key hot spots where it's happening. The kinds of innovation that we've driven are, think back to the early days of the cloud, we were doing R&D in patents and research in the cloud before the term "cloud" existed. And once the cloud phenomena took off, we had assets and architectures that we turned into the Accenture cloud platform, which has made us a leader in the multi-billion dollar ... Built a multi-billion dollar business in the cloud market. So that's an example of research and idea in early patents going to scale business for Accenture. That's the research to results that we talk about and what makes a difference in our business. >> So, talk about AI. AI's a hot trend, it's a great buzzword. I love AI because it gets young people excited about software. IOT is a little bit more boring than AI. But AI is augmented intelligence, also a little bit of artificial intelligence. Look no further than a test load, look no further than some of these cool things. How's AI impacting your world? >> AI's massive. I would say AI is the biggest single innovation and the most disruptive innovation of the information age to date. And probably, the biggest impact on how we work and live since the industrial revolution a couple hundred years ago. That started a couple hundred years ago. So AI is a big impact, and we're just at the start of it. That's kind of a paradox, though, because AI has been around for 60 years. The term was coined 60 years ago in 1956 at Dartmouth. And it just did it kind of slowly, but now we're at the inflection point where we have the computing hardware and the data and the processing power to make it really happen. So for the next five to 10 and 20 years, it's all about applying intelligence to augment the way we as people work and live and really create new opportunities to improve the productivity and creativity of humans. That's why we're excited. >> It's a perfect innovation storm. You've got great compute capability, almost unlimited capacity, software, new developer, open source is booming, and now you have STEM. >> Well, before you get to STEM, let me just make one comment on that. I think the other exciting thing about AI is we've been working with dumb technology up until this point. Think about the way we interact with our thumbs on a mobile phone. Think about the way you use traditional software in an enterprise on your PC or your screen. We're slaves to dumb technology, and the power and potential of AI is to make technology smarter, more human-like, and really enhance our ability as humans to use it. And that's why it's an exciting era. >> That's a great perspective from someone who has been in the process business. The classic example is, does the process work for you? Do you work for the process? >> Dougherty: Yeah. That's what technology ... >> And technology, we don't work for technology. They should work for us. >> And that's what's changing. That's the inflection point. >> So now, 30 years now, a lot's changed, certainly in Silicon Valley lately. Women and the role of women in the industry is certainly important. We're going to be at Grace Hopper for the fourth year this year as part of our women in tech celebration, in California this year covering women in tech. STEM is huge, but also, the gender gap is still there. You guys have a pledge to be 50% by 2025, Accenture as an organization. Labs, in particular, getting STEM in the technical roles is also a challenge. What are you guys doing to address that, and what's your personal philosophy? What's your comment about STEM and women in tech? >> Well, look, the technology industry in general has a gender diversity problem, and we believe at Accenture, we can really set the standard for how to really get to gender equality in the workforce. And that's the commitment we've got with our 50/50 gender diversity pledge by 2025. We're well along the path to getting there, right about 36% or so. Now, with the actions we're taking, the formula we've got, I'm confident that we'll get to the 50/50 pledge that we set out there. And it's an imperative for the technology industry, not just for Accenture, because we won't innovate to the potential of the industry, and we won't create the right opportunity if we don't have the right gender balance in the workforce. That's what will lead to the right innovations. In this new era where the humanity of how we apply technology, as you were saying earlier, flipping the lens on a people-centric view, we need all the perspectives and an equal representation of the population going into the way we develop solutions. That's why it's a priority for us. And we think we can really set a standard for how to apply to the technology industry. >> It's certainly a topic near and dear to my heart and our company's heart. I want to ask one more question on that as a follow-up. Computer science was always kind of narrow, I'm not saying super narrow, but now it's broadened, with analytics, the tech science side is opening up, for all the reasons you were just talking about, the AI stuff. It's a broad landscape now for many diverse roles. Can you share your thoughts on where the entry points could be for women, where it's not a man-led culture or new opportunities or new areas, new opportunities to engage, learn? Certainly digital will help that, in terms of acquiring knowledge. But in terms of getting into the business, what is the surface area of opportunities? >> The surface, it's the whole surface area. I think the wrong approach is to think that there are certain roles that are better for women or better for any group to do. There's equal opportunity in all the roles. One stat that's striking to me is the fact that, when I graduated from college in 1986, 35% of the graduates were women. 35% in 1986. Today that number is about 18%. We've gone backwards in the percentage of women graduates from computer science programs. That's a problem that we need to address. We need to get more women into technology careers. It's about sponsorship, it's about mentorship, it's about having the right role models, and it's about painting the right picture of the opportunity in technology. One of the organizations I'm involved with is Girls Who Code, where I'm on the board of directors because of our Accenture involvement because I believe that we need that kind of early involvement with girls to get them on the right paths and make them aware of the right opportunities that we can get them into the pipeline earlier. >> Congratulations. Thanks for doing that; it's great stuff. Personal question. 30 years, you've been in Accenture for a long time, 30 years of labs now, celebrating. What's the coolest thing you've done? >> You know, the coolest thing, the coolest thing is building the fabric of innovation of the company, so what we've done with the labs, creating Accenture Ventures, which is our tool for investing in companies, formalizing our Accenture research capabilities, that we now have an innovation fabric that goes from research to our ventures into our labs and the rest of Accenture's business. So we can take innovations like quantum computing and scale it and ramp it right into our business like we're doing today. So that's what's exciting to me, is to have created a funnel that we can use to take the early-stage innovations and pump them into real impact on our business. >> Awesome, and quick, what's happening here tonight? We're here at the 30th, labs here in Silicon Valley, Computer History Museum, historic event, magic. What's the show about today? >> Yeah, it's all about the past, the present, and the future. The past is how we got here with tremendous leaders of Accenture Labs, who built the organization to where it is today. The present is what I was just talking about, all the opportunity we have. And the future is more exciting that it's ever been. The next 30 years ... My only regret is that I'm not 20 years old right now. So the next 30 years are going to be even more exciting than the 30 years that I've lived through. And we're in a great place. Computer History Museum isn't just about the past. It's about the future. I'm on the board of trustees here at the Computer History Museum, and I love the mission of the museum in the way it brings the stories of innovation to light and sets us on the course for the future as well. >> Well, since you have so much influence, we're going to have to get our genes edited for sequencing so we can actually live longer because that's coming around the corner, too. >> I think that's the right idea. >> Cheers. Congratulations. >> Paul: Cheers. >> We'll be back with more coverage here live in The Cube. Accenture Labs' 30-year anniversary. I'm John Furrier with Paul Daugherty, chief technology and information officer, great work, innovation officer, great work. Congratulations. More coverage after this short break. Thanks for watching.

>> Narrator: From the Computer History Museum in Mountain View, California, it's The CUBE. On the ground with Accenture Labs 30th Anniversary Celebration. (techy music) >> Hello everyone, welcome to the special CUBE coverage of Accenture Labs 30th years of celebration here at the Computer History Museum in Mountain View, California, the heart of Silicon Valley. I'm John Furrier with The CUBE. Our next guest is John Walsh who is the Northern California Office Managing Director as well as the General Manager of the P&L of Telecom, High Tech, and Media Entertainment. Three big P&Ls, plus running the whole territory. You got a big celebration here, thanks for joining me. >> Thanks for coming, John. It's great to have you. >> So first of all, Northern California, you got The Warriors in the backyard. I'm sure Accenture's got a box, schmoozing customers, you guys working with them at all? >> Well, ya know, it's funny you bring that up, John. We are working, we're pretty close with The Warriors as it turns out. As you know, The Warriors are building out their new stadium, right down at the Dogpatch in San Francisco, and so we've been working with them to really design the fan experience. Before, during, and after the game, what that experience is going to look like. Being here in Northern California, you can imagine that's going to be a very, very tech forward experience. Hopefully it's going to kind of define the state of the industry. We're proud to be a partner of The Warriors, and part of that design. >> What better topic to kind of, as a backdrop to the Labs, Accenture Labs, 30 years here, looking forward to the next 30 years. I mean, The Warriors are the poster child, kind of like The Patriots are in football, with respect to a culture, but they're innovative, tech geeks too. They understand how to use technology for an outcome, not trying to get an outcome out of their technology. They really understand that, and that's really kind of the ethos, of the Labs. >> I think that's exactly right, and obviously, ya know, we can talk about The Warriors as much as you want (John Furrier laughs) I'm a huge fan, but ya know, the way they've thought about actually changing the game through technology, and embedding it in part of the way they actually build that experience out, is one of the reasons why we partner well with them. Obviously, we'll leverage our Labs' capabilities and a lot of our Lab practitioners in order to actually co-innovate with The Warriors. I think all of us here in the Bay Area, are going to be able to appreciate that in the coming years. >> Well, when the NDAs are expired, or maybe even sooner, we'll have to come up to your office and get a deeper dive on The Warriors situation. >> Let's do a double click on that. >> It's worth a bigger feature. But here at the Labs and Computer History Museum, better place to kind of talk about where the industry's come from, where Accenture Labs has come from, and where it's going. So I got to ask you, Arthur Anderson back at a big six accounting firm 30 plus years ago, to Anderson Consulting to Accenture, really kind of was the ways of innovation that everyone talks about. Now, the next 30 years, we're looking down the throat of AI, blockchain, internet of things, using data at scale, cloud computing, quantum computing, really changing how companies are executing their business architecture, not just IT. >> For sure. >> I mean, it's a complete transformation, disruption. >> For sure. >> Well, I mean, Accenture, you went through the history. I actually joined Arthur Anderson, ya know, 30 some years ago. I think we've always prided ourselves on being on that leading edge, and sort of our objective was to actually incorporate those new technologies, apply them to our enterprise client base. Be able to do that, ya know kind of be there, and then be gone before our competitors get there. I think you'll see some of that tonight as we're sort of walking around the showcase here. You've heard this a hundred times, John. There's never been a better time to be in the tech world. To be able to actually look at the breadth of technology opportunity that's here. How to apply that to our global enterprise base to create advantage differentiation and change. Change is what drives our business model. >> Yeah, we were just talking with Mark, one of the Senior Directors of the Labs. Ya know, talking about accounting firms and those kinds of, way back in the day, they would instrument business. Now, as you guys are now in more, 30 years, plus years later, the instrumentation's all in the data. So literally, for the first time in the history of the world of business, you might not need accounting with blockchain, and everything's instrumented. So there's no more questions that can't be answered, some level! So this is going to be like a complete new generation. Next 30 years, pretty significant. Everything's instrumented, and all kind of disruptions around how a company organizes themselves. What is Accenture's vision? How do you guys talk to customers? Not only is it mind blowing, it also is fear. >> Yeah >> If I don't adapt and move on, I can't get there. >> Yeah, well I mean, and again that is, that's the nature of competition. That's always been the nature of technology. Right now, I think it's a combination of, the digital natives have been the ones that have kind of been pushing the envelope and putting pressure on every industry, every business model, and I think that they've been out in front. We're seeing, ya know, sort of our whole global client base adapt and respond and start to incorporate all of these, and re-engineer their processes with benefit of digital at every one of those layers. You mentioned it, analytics, sort of end data, is at the core of, I think, what will define success in the future for every enterprise, in every industry. That's really where we're spending our time with our customers. It's like, how do you take advantage of the data and the insight and the knowledge that you have, to run your business more efficiently and better serve customers? By empowering your employees to serve customers, and to allow customers to better serve themselves, with all these tools? >> We're here at the Computer History Museum, in your backyard, your territory, so you're obviously going to crash the party, but I find that really compelling, and rightfully so, to be in Silicon Valley. But the world's changing, and they're going to come up with the next 30 years, it's going to match your show here. So I got to ask you, someone who leads the business, who have been through the organization, how do you hire the next generation talent? You got to build out, you got to innovate. What's the profile, is there an algorithm? Is there a formula that you have as you build out and continue to scale out your people? Got the innovation DNA and the culture-- >> We do. >> We see that. We got the Labs pumping on all cylinders, we see that. What's the people strategy? Diversity's key, you're seeing more women coming into the workforce. Certainly in Silicon Valley, our territory, has been great news lately for women. >> Right. >> What are you guys doing? >> So, let me start last first, with the diversity comment. I think we've been pretty public in terms of communicating sort of, what the profile of our employee base looks like. All the statistics, top to bottom, from diversity, ethnic diversity and gender diversity. Our CEO has recently made a commitment to be at 50/50 gender diversity by 2025. I don't think there's any other company-- >> That's amazing-- >> of our size and scale, that's made that level of commitment >> That's a moon shot. That's a moon shot level, Mars shot, what do you want to call it. >> It's a moon shot, for sure, but the way we're looking at it, it's 50 percent of the IQ actually, ya know, is there, and we need to be able to be tapping into all of that. For those folks, they're in the marketplace, they're just not at Accenture, and we want to create an environment that actually brings all those folks in. Other than that, it's just, ya know, it's based-- >> More data scientists. >> More data scientists. >> More engineering. >> More engineers, more computer science, and more people that are good at problem solving, and naturally curious. We have a pretty rigorous recruiting process, and we also have a brand that I think, attracts talent. We build deep relationships with universities, which helps, kind of gives us early access. I was talking to a couple of our interns who are here tonight, like wow, this is awesome. That's always been the recipe for Accenture. >> What do you say to the young college grads that are graduating, undergraduate or Masters degree, man, I'm going to land a job at Accenture! It's a dream job at some level. What do you say to them? What do you look for? I'm looking for, fill in the blank. When you say, answer that question. >> For me, I'm looking for people that love problem solving, right. That are naturally curious. Working at Accenture's hard, right. So having that work ethic, that ability to be persistent. >> You got to be skilled, you got to be skilled. >> Well, you got to be skilled. You don't even get the interview if you don't have (John Furrier laughs) at least that much on your resume. But beyond that, ya know, it's how they interact. We're a client focused business as well, so having people that are actually able to to work as part of a team, and work with clients, is pretty critical. >> John, congratulations, and the event's starting. Thanks from all at the CUBE, we really appreciate it. John Walsh, who runs the California, Northern California Managing Director, as well as the P&L responsibility for Telecom, High Tech, and Media Entertainment. Here at the CUBE coverage of Accenture Labs 30 year celebration at the Computer History Museum. I'm John Furrier with the CUBE, thanks for watching. (techy music)

>> Announcer: Live, from Washington, DC, it's the cube. Covering dot next conference. Brought to you by Nutanix. >> Welcome back to DC everybody, this is the Nutanix dot next conference #NEXTConf, and this is the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise. My name is Dave Vellante, and I'm here with my co-host Stu Miniman. Dr. Arthur Langer is here, he's a professor at Columbia University, and a cube alum. Good to see you, thanks very much for coming on. >> Great to be back. >> Dave: Appreciate your time. So, interesting conversations going on at dot next. People talking about cloud and you hear a lot about virtualization and infrastructure. We're going to up level it a bit. You're giving a talk-- you're hosting a panel today, and you're also giving a talk on strategic IT. Using IT as a competitive weapon. It wasn't that long ago where people were saying does IT matter. We obviously know it matters. What's your research showing, what is your activity demonstrating about IT and how is it a strategic initiative? >> Well, if you were to first look at what goes on on board meetings today, I would say, and I think I mentioned this last time, the three prominent discussions at a board is how can I use technology for strategic advantage, how can I use predictive analytics, and how are you securing and protecting us? And when you look at that, all three of those ultimately fall in the lap of the information technology people. Now you might say digital or other parts of it, but the reality is all of this sits at the heart of information technology. And if you look at many of us in that world, we've learned very efficiently and very good how to support things. But now to move into this other area of driving business, of taking risks, of becoming better marketers. Wow, what an opportunity that is for information technology leadership. >> Dave: So, obviously you believe that IT is a strategic advantage. Is it sustainable though? You know, I was sort of tongue-in-cheek joking about the Nick Car book, but the real premise of his book was it's not a sustainable competitive advantage. Is that true in your view? >> I don't believe that at all. I live and die by that old economics curve called the S curve. In which you evaluate where your product life is going to be. I think if you go back and you look at the industrial revolution, we are very early. I think that the changes, the acceleration of changes brought on by technological innovations, will continue to haunt businesses and provide these opportunities well past our life. How's that? So, if anybody thinks that this is a passing fad, my feeling is they're delusional. We're just warming up. >> So it can be a sustainable competitive advantage, but you have to jump S curves and be willing to jump S curves at the right time. Is that a fair difference? >> Yeah, the way I would say it to you, the S curve is shrinking, so you have less time to enjoy your victories. You know, the prediction is that-- how long will people last on a dow 500 these days? Maybe two, three years, as opposed to 20, 30, 40 years. Can we change fast enough, and is there anything wrong with the S curve ending and starting a new one? Businesses reinventing themselves constantly. Change a norm. >> Professor Langer, one of the challenges we hear from customers is keeping up with that change is really tough. How do you know what technologies, do you have the right skill set? What advice are you giving? How do people try to keep up with the change, understand what they should be doing internally versus turning to partners to be able to handle. >> I think it's energy and culture and excitement. That's the first thing that I think a lot of people are missing. You need to sell this to your organizations. You need to establish why this is such a wonderful time. Alright, and then you need to get the people in, between the millenials and the baby boomers and the gen x's, and you got to get them to work together. Because we know, from research right now, that without question, the millenials will need to move into management positions faster than any of their predecessors. Because of retirements and all of the other things that are going on. But the most important thing, which is where I see IT needing to move in, is you can't just launch one thing. You have to launch lots of things. And this is the old marketing concept, right. You don't bat a thousand. And IT needs to come out of its shell in that area and say I have to launch five, six, eight, 10 initiatives. Some of them will make it. Some of them won't. Can you imagine private equity or venture people trying to launch every company and be successful? We all know that in a market of opportunity, there are risks. And to establish that as an exciting thing So, you know what, it comes back to leadership in many ways. >> Great point, because if you're not having those failures, your returns are going to be minuscule. If you're only investing in things that are sure things, then it's pretty much guaranteed to have low single-digit returns, if that. >> Look what happened at Ford. They did everything pretty well. They never took any of the money, right, but they changed CEO's because they didn't get involved in driverless cars enough. I mean these are the things that we're-- If you're trying to catch up, it's already over. So how do you predict what's coming. And who has that? It's the data. It's the way we handle the data. It's the way we secure the data. Who's going to do that? >> So, that brings me to the dark side of all this enthusiasm, which is security. You see things like IOT, you know the bad guys have AI as well. Thoughts on security, discussions that are going on in the board room. How CIOs should be thinking about communicating to the board regarding security. >> I've done a lot of work in this area. And whether that falls into the CISO, the Chief Information Security Officer, and where they report. But the bottom line is how are they briefing their boards. And once again, anybody that knows anything about security knows that you're not going to keep 'em out. It's going to be an ongoing process. It's going to be things like okay what do we do when we have these type >> response >> How do we respond to that? How do we predict things? How do we stay ahead of that? And that is the more of the norm. And what we see, and I can give you sort of an analogy, You know when the President comes to speak in a city, what do they, you know, they close down streets, don't they? They create the unpredictability. And I think one of the marvelous challenges for IT is to create architectures, and I've been writing about this, which change so that those that are trying to attack us and they're looking for the street to take inside of the network. We got to kind of have a more dynamic architecture. To create unpredictability. So these are all of the things that come into strategy, language, how to educate our boards. How to prepare the next generation of those board members. And where will the technology people sit in those processes. >> Yeah, we've had the chance to interview some older companies. Companies 75, 150 years old, that are trying to become software companies. And they're worried about the AirBnB's of the world disrupting what they're doing. How do you see the older companies keeping pace and trying to keep up with some of young software companies? >> Sure, how do you move 280 thousand people at a major bank, for example. How do you do that? And I think there's several things that people are trying. One is investing in startups with options to obtain them and purchase them. The other is to create, for lack of a better word, labs. Parts of the company that are not as controlled, or part of the predominant culture. Which as we know historically will hold back the company. Because they will just typically try to protect the domain that has worked for them so well. So those are the two main things. Creating entities within the companies that have an ability to try new things. Or investing entrepreneurially, or even intrapreneurally with new things with options to bring them in. And then the third one, and this last one is very difficult, sort of what Apple did. One of the things that has always haunted many large companies is their install base. The fact that they're trying to support the older technologies because they don't want to lose their install base. Well remember what Steve Jobs did. He came in with a new architecture and he says either you're with me or you're not. And to some extent, which is a very hard decision, you have to start looking at that. And challenge your install base to say this is the new way, we'll help you get there, but at some point we can't support those older systems. >> One of my favorite lines in the cube, Don Tapps, God created the world in six days, but he didn't have an install base. Right, because that handcuffs companies and innovation, in a lot of cases. I mean, you saw that, you've worked at big companies. So I want to ask you, Dr. Langer, we had this, for the last 10 years, this consumerization of IT. The Amazon effect. You know, the whole mobile thing. Is technology, is IT specifically, getting less complex or more complex? >> I think it's getting far more complex. I think what has happened is business people sometimes see the ease of use. The fact that we have an interface with them, which makes life a lot easier. We see more software that can be pushed together. But be careful. We have found out with cybersecurity problems how extraordinarily complicated this world is. With that power comes complexities. Block chain, other things that are coming. It's a powerful world, but it's a complicated one. And it's not one where you want amateurs running the back end of your businesses. >> Okay, so let's talk about the role of those guys running. We've talked a lot about data. You've seen the emergence of the chief data officer, particularly in regulated industries, but increasingly in non-regulated businesses. Who should be running the technology show? Is it a business person? Is it a technologist? Is it some kind of unicorn blend of those? >> I just don't think, from what we've seen by trying marketing people, by trying business people, that they can really ultimately grasp the significance of the technical aspects of this. It's almost like asking someone who's not a doctor to run a hospital. I know theoretically you could possibly do that, but think about that. So you need that technology. I'm not caught up on the titles, but I am concerned, and I've written an article in the Wall Street Journal a couple years ago, that there are just too many c-level people floating around owning this thing. And I think, whether you call it the chief technologist, or the executive technical person, or the chief automation individual, that all those people have to be talking to each other, and have to lead up to someone who's not only understanding the strategy, but really understands the back end of keeping the lights on, and the security and everything else. The way I've always said it, the IT people have the hardest job in the world. They're fighting a two-front war. Because both of those don't necessarily mesh nicely together. Tell me another area of an organization that is a driver and a supporter at the same time. You look at HR, they're a supporter. You look at marketing, they're a driver. So the complexities of this are not just who you are, but what you're doing at any moment in time. So you could have a support person that's doing something, but at one moment, in that person's function, could be doing a driving, risk-taking responsibility. >> So what are some of the projects you're working on now? What's exciting you? >> Well, the whole idea of how to drive that strategy, how to take risks, the digital disruption era, is a tremendous opportunity. This is our day for the-- because most companies are not really clear what to do. Socially, I'm looking very closely at smart cities. This is another secret wave of things that are happening. How a city's going to function. Within five, seven years, they're predicting that 75% of the world's population will live in major cities. And you won't have to work in the city and live there. You could live somewhere else. So cities will compete. And it's all about the data, and automation. And how do organizations get closer with their governments? Because our governments can't afford to implement these things. Very interesting stuff. Not to mention the issues of the socially excluded. And underserved populations in those cities. And then finally, how does this mess with cyber risk? And how does that come together to the promotion of that role in organizations. Just a few things, and then way a little bit behind, there's of course block chain. How is that going to affect the world that we live in? >> Just curious, your thoughts on the future of jobs. You know, look about what automation's happening, kind of the hollowing out of the middle class. The opportunities and risks there. >> I think it has to do with the world of what I call supply chain. And it's amazing that we still see companies coming to me saying I can't fill positions. Particularly in the five-year range. And an inability to invest in younger talent to bring them in there. Our educational institutions obviously will be challenged. We're in a skills-based market. How do they adopt? How do we change that? We see programs like IBM launching new collar. Where they're actually considering non-degree'd people. How do universities start working together to get closer, in my opinion, to corporations. Where they have to work together. And then there is, let's be careful. There are new horizons. Space, new things to challenge that technology will bring us. 20 years ago I was at a bank which I won't mention, about the closing of branch banks. Because we thought that technology would take over online banking. Well, 20 years later, online banking's done everything we predicted, and we're opening more branches than ever before. Be careful. So, I'm a believer that, with new things come new opportunities. The question is how do governments and corporations and educational institutions get closer together. This is going to be critical as we move forward. Or else the have nots are going to grow, and that's a problem. >> Alright, we have to leave it there. Dr. Arthur Langer, sir, thanks very much for coming in. To the cube >> It's always a pleasure to be here >> It's a pleasure to have you. Alright, keep it right there everybody, we'll be back with our next guest. Dave Vollante, Stu Miniman, be right back.

but cube at servicenow knowledge 14 is sponsored by service now here are your hosts Dave vellante and Jeff trick here we go hi buddy we're back this is Dave vellante with Jeff freak this is the cube we go out to the events we extract the signal from the noise we have a crowd chatting on its crowd chat / no 14 so check that out put your tweets in crouch at awesome engagement app Frank's Lupin is here president CEO of service now Frank it's it's a pleasure to have you back on the cube great to see again great to be here thanks things how you feeling I'm feeling great no I got that keynote got the keynote out this morning you had the financial analyst in yesterday had the industry analyst and they're working you hard absolutely it's a circus yeah so your keynote this morning was great I was right up front they have a nice spot for the industry analyst so appreciate that take good notes but one of the themes that you struck was really hit home to me because you talked about transforming IT from essentially a cost center into a value producer and how service now is at the heart of that and and how the role of the CIO is changing so one of you could sort of summarize and talk a little bit about how you see the role of IT and generally in the CIO specifically changing and what role service now plays in that transformation yeah just just to give a little bit on macro context right that's sort of the worst of all scenarios that we see out there where I t is essentially viewed as as a commodity as a utility and as a result you know people don't see much impact I just want to get a cheaper cheaper cheaper and they want to cut more costs out of the infrastructure and staffing levels and so on and actually is just an organization that we're tolerating because I guess we have to have email and Internet access and all that sort of thing now you go looking into broader world around what technology has done to change business right what amazon has done based on their technology platform what we've seen an online banking you know what we're seeing an online education there's just just incredible examples of innovation using technologies now aighty hasn't done that for their own enterprises they happen in some instances are some some really great examples out there where I t did impacted business but by and large IT is not viewed as to go to people that know how to bring technology into business you know in a way that that really turns the tables on the competition do some mind-blowing things i always ask CIOs when i when i meet him and says what have you done in the last 12 months that really blew people's minds or in terms of applying technology to business problems right and they start sort of thinking like i'll actually it is surely nothing i can think of well that's probably you know a question you should be asking yourself all the time right if it's not when lightning in a bottle when it's not the sort of thing that sort of lights up the whole enterprise like we won't do it is we have to do this that excitement then you're shooting too low and you know in general I find the the cost obsession and IT is an indication that we're not looking for the opportunity and I think that's that that's it that's a damn shame or we're here to change that well you talked about panning for gold was that proposed here in California and it's also a propellant you your company is smoking hot and you know your your commonly associated with the likes of workday and Salesforce and sponsor must be very very proud of that but also there's gold and then RIT shops right there's goal than those organizations that's not being being mined and and you know I think you talk about your penetration is what twenty percent of your your target your global 2000 where we have footprint in about eighteen percent of the enterprises that we think are relevant and appropriate to us but within those eighteen percent you know we were probably a third saturated so so very early innings for service now even though we've achieved considerable scale and very high growth at that scale so when you go into one of your accounts can you discern actual that actual value production vision that you set forth can you see it can you touch it can you you know to this to a skeptic a prospectus yeah Frank that sounds good but can you actually sort of provide proof points yeah managing surface is just essential in terms of economizing and saving money and here's why no I'll give you some some very pedestrian examples that we've seen in real life and the human resources department and probably get the example because I t everybody sort understands how to how the game works right HR organizations historically have not had service models they have that email and phones and so on the problem just called somebody as a result that was a huge amount of work that preoccupied the HR organization that nobody knew what people were working on and the staffing grew and grew and grew to deal with the growing volume of ink wires and problems and changes and so on until they have systems service models and they have reporting and analytics that showed them what was consuming their time once you know that you can put initiatives in place to start dealing with the underlying causes that are driving that work I have seen HR organizations dwindle their staffing by 50% just by understanding what of this day we're working on right that's what service management is all about instead of just delivering service you're managing and once that quarter drops by the way IT organizations they get this in space right because you know large enterprises they got fifty hundred thousand one hundred fifty thousand instance flowing to their organization a month it's a huge consumer of resource right if you go to these other service domains and you see very similar things this layer of software really optimizes that resource well the way they attack it oftentimes is human resource doesn't that scare a lot of prospects away when they hear oh wow near cup service now and they're going to replace all these these people it's a it's a good question actually wrote a blog post about it recently as well there is no doubt that in the economy at large we're going to see massive substitution from people to systems why because the technology is here and the economic imperative is here it's very much a societal and social question but you know here's the thing see alternative you know are we going to try and stop it and not do it it's going to happen the markets are going to run their course what needs to happen is that we adjust you know for example you know in education we have a lot of teachers right what's going to happen to teachers when education is delivered through online streaming well teachers gobble you want to become crooklyn developers in other words evolve and change in their roles because education is going online slowly but let's go into why because the format the service experience is that much better it scales that much better in step much more economical than what we currently have well you said today in your key note that the system is broken you know I'm having to put four kids through school I appreciate a nudge there to the educational system why did it take so long I mean these are the IT guys ease of the technology guys in the organization they're there to deliver value why did it take so long for this kind of transformational yeah wave Steve Jobs has been the late Steve Jobs been quoted many times people don't know what they want until I show it to him and that's sort of what we're doing we're showing it to him that's what we did this morning we're showing people what they can aspire to that's what we're here for we're trying to stimulate inspire motivate give people a sense of mission right as opposed to keeping the lights on managing crises running around with your hair on fire that's not a very attractive you know a view to half of your organization and what you do all day right yeah so I have it struck again by your keynote the Affordable Care Act affectionately known as Obamacare they not the government not a customer of yours or what's the scoop oh no they could you have helped with had problem we could have for sure but then again many people cook that for the foot of people then software and technology they look at something like that yeah last night I set a dinner with Adam infrastructure for Kaiser Permanente and they had a certainly know the problems of open enrollment that a massive scale and certainly we didn't want to trivialize the problem it is really really hard to need to operate the service like that at the scale that that they need to but there is no doubt that you know we don't need any new core technology to build systems like that I mean the technology exists the skills exists sure that I want to walk better than so let's talk about your business a little bit this year third year now right since you've joined service now exactly three years this week yeah so let's sort of break that down but when you when you join service now that the discussion was around and you talked about this yesterday the the whole team and everybody was looking at help desk saying wow how can these these these values be justified and of course you blew that away and now people are beginning to understand that it's interesting to note that data domain you sold the company i think for what 2.5 billion the entire market is is now greater than the market that it replaced interesting that's right the market was three billion it's now I according obviously bigger than three billion and growing yeah you know so that's kind of interesting now that's a much more confined market you know you talked about the tons of the team they're being finite you always knew it was finite here it's different you guys have started to sort of fine tune your tam analysis and communicate that it's still hard because you just don't know the how people are going to use your software they're finding new ways but the team and I took a stab and I came up with 30 billion but it was a top-down it wasn't a bottom up and it was I had to get the blog post out so it's kind of a back of the napkin but still it's very very large clearly a multiple of the IT service management market so I wonder if you could talk about sort of the the evolution of your thinking in terms of the market opportunity with service now were you always sort of where we are today or that have to evolve over time now it has evolved I'd say dramatically obviously the expansion from what used to be called help desk management to IT service management basically you know exploded the market at least 5-fold and they were licensing five to ten times as many people on our system now for itsm purposes then we used to and in the mid 90s during to help desk area because back then all we did was licensed people ever physically on the help desk right people that would take phone calls and emails and so on now really everybody in the IT organization is an actor and a participant in the workflow of service management you may be a DBA maybe a network engineer you're going to get when an incident comes in or a problem is defined you're going to be part of that workflow right so that Dad expansion was not understood early on but beyond that services is everything is everywhere and services everything and every physical and even non physical assets have service models around them so once you start looking forward you see it absolutely everywhere you know I don't know what's a few billion among friends you know I know all that the numbers are but this is heavily transformational I think one of the things that people struggle with they're looking for a line of sight right in our company like workday is viewed very possibly why because they're seeing them take dollar for dollar market evaluation away from companies that they can identify recipe in Oracle and so on feels very credible to gamma that's 250 billion dollars or mark oh I can see those guys from work the Oracle Sapa okay take a chunk out of their eyes I know you go look at service now you need to have more imagination there's this great court from Arthur Schopenhauer that I showed you yesterday which said you know you know takedowns to hit a target that nobody else can hit but it takes genius to hit a target on nobody else can see right it's transformational right what worked it does is modern with what service now does this transformation is fundamentally different so when you came on to service now I presume your focus was putting in the infrastructure and the process is to make sure that you could scale just having watched you in your career you're you're big on growth and yeah you're pretty aggressive so so take us through sort of you know where you sort of started and what the emphasis was and and where it is now be clearly you're investing in sales and marketing you're investing in AP I didn't know this the substantial number of global 2000 companies in asia-pacific so that's another so how is that I mean break that down into maybe one or two or three sort of segments of your attention and effort there there's sort of you can sort of split up in two major stages or phases the first phase you know when when I took over the helm of the company was very much focused on operationalizing stabilizing scale being able to deliver what we're already doing in a consistent and predictable manner and that was not a minor task because because the company had grown so fast but hadn't been able to basically catch itself in terms of bill into business building the organization underneath its business so that preoccupied us tremendously the whole thing about cloud is is not like there's a lot of people you know running around out there to actually no clout that understand clot that can build clouds and how many people do you know that I've actually done this because there's you know three years ago I mean they were far and few we actually recruited people that have built the original cloud of ebay because those guys were pioneers they have solved a lot of the problems associated with cloud early on we saw a lot of people that understood data centers the cloud this is almost in verse two data centers the mentality that you need to to run them davos phase one before us and we sort of got through that you know about you know a year and a half ago for sure about a year ago and we started to shift gears you know really from the operational infrastructure concentration that we've had to really trying to drive strategically the business towards enterprise service management they're really expanding the addressable market way beyond where we had been before we were going to market until i see i l-look itsm replacement you have to do it you're sitting on 10 15 20 year old software it's crappy it's got to go fine we're going to do that right but we want to give you this much bigger perspective managing service in the enterprise and you know make that a mission that you can own as a CEO and drive throughout your organization over a period of years and a lot of our customers have road maps that are 24 36 months and it shows you all the things they're going to knock off over that period of time and all the different you know parksley enterprises to sell is its engineering its market yourself so on yes okay so Tam expansion and now obviously accessing that to him we hire in a lot of sales people and go to market I was struck walking around the exhibit hall last night because you just announced app creator I think last year yep knowledge I was struck by you know that the booth down there with the number of apps I mean it's just astounding where that's going wouldn't have predicted you know some of them that I that I saw so that's obviously part of the the tam expansion as well I wonder if we could talk about the importance of a single system of record in order to achieve that vision because it's not always easy right politically people want to keep data in their own little silos so how does that work you can't force it in because it sort of just happen organically how critical is that to your success I mean when you have applications or services that relate to each other like for example you know this morning we showed in a demo I think we're sure like seven or eight different applications in the course of one demonstration the reason that is a single system of record matters so much when you do that is all these apps need to be aware of each other right when your when your staff in the projects you need to look at the resource management well that resource management relates to the skill requirements as well the skills that are available right what you don't want is these apps living in their own universes with their own data moss your own database because now you have to start the hack integrations between them to make any sense out of that and that's the world we lived and that's been the bane of software existence for for so long the ServiceNow said I'm not going to do that okay every application that relates to any other application they're going to be operating on exactly the same data model and by the way you see that throughout our platform right when you bring up an asset in the CMDB like a server or a rather or Santa whatever it is you'll be able to see all the other data artifacts throughout the platform like instantly problem of changes in projects and tasks that relate to that particular asset there's nobody else that can do that right and we provide the 360-degree visibility that makes application development so compelling because you know all the users are already defined the system you don't even have to get started with that you only define users once right you reuse all that and all the other artifacts already exists so you get this data gravitas that the more data that is there to richer the application with almond environment becomes yeah we talked about this too at the analyst meeting about the relationship to your M&A strategy you've got to be selective it's got to fit in to that single system of record does that however limit your choices in rule absolute limit our choices but you know this is the commitment from an architectural standpoint that we make us that we're not going to repeat what legacy vendors have done is I mean you know 50 apps whole stand along to hack integrations between them as I said that's the world our customers want to leave behind because it was just horrible former from an efficiency standpoint after a while all you people do is managing the operability of the patchwork plethora of assets that they have they're not doing anything productive and in our world they don't they do none of that right they're not upgrading software because it's the clouds you know we do that and they're not hacking integrations between apps because there is no constant of integration on service not with all the apps are aware through a shared data model so is there still plenty of M&A opportunity for you out there though I mean your stocks up I know it's off a little bit lately which I think it's really healthy I'm happy about that nice little breather but still you know you've made great progress adding value you can obviously use your stock as acquisition card co there's still plenty of opportunities for you notice there's absolutely tons of opportunities again in a day you know software infrastructure is it's very similar and very common between application itself for us to bring an application into our user interface framework I mean they have to have a user interface framework of some sort right so whether we replace what they have with ours with a replace the data structure we replace the underlying cloud we can do all those things right the question is is there going to be hard is it going to be expensive is it going to be time-consuming or maybe not as much and that will influence how attractive we are to the asset all right Frank we're way over on time but I could go forever i mean really appreciate you coming on CX for having us here it's really fantastic event all right keep it right to everybody we're back with our next guest this is the cube we're live from moscone right back