>>from Miami >>Beach, Florida It's the Q covering a Cronus Global Cyber >>Summit 2019. Brought to you by a Cronus. >>Welcome to the Qi. We are in Miami, Florida, for the Cronus Global Cyber Summit. 2019 John for your host of the Cube. We're here for two days of coverage around cybersecurity and the impact to the enterprise in society in a great guest here to kick off the event. Linda Babcock, professor of economics at Carnegie Mellon University, author of the book, Ask for It, and she has a new book she's working on, and we'll get into that. Thanks for joining me. Thanks for coming on. >>Really happy to be here. >>Thanks. So Carnegie Mellon. Great. Great. Uh, University. They stole a bunch of people when I was in school, in the computer science department. Very well known for that as well. Economics, math, machine learning. I was good stuff there. What's going on in Carnegie Mellon? What's new in your world? >>Well, it's just actually just a great place to be because of the focus on interdisciplinary work. You know, problems in the world don't come as disciplines. They come with multiple perspectives needed and So it's just a place where people can flourish, attack ideas from all kinds of angles. And so it's a really great >>one of the things I hear a lot about, and we cover a lot about the the skills gap. Certainly this is Maur job openings than there are jobs and interesting. A lot of the jobs that are new haven't been skilled, important in the classic university setting. So a lot of these jobs, like cybersecurity, cloud computing, Blockchain, crypto economic token economics, all kind of have a maths economic steam to him. So you know your computer science, you got economics and policy. I seem to be the key areas around from these new skills and challenges. Way faces a society which your take on all this >>Well, actually, there's a lot going on in this area at Carnegie Mellon. Actually, the economics group at Carnegie Mellon ISS is been proposing a new major that really focuses on this interface between economics, machine learning and technology. And I think it's going to train our students just for the next generation of problems that the world of tech is gonna have. So it's very exciting. >>So let's talk about your book. Ask for it. Okay. Um, it's not a new book that's been around for a while, but you give a talk here. What's what's the talking talking track here at the event? >>Yeah, so I have a couple of themes of research, and it focuses on women's Berries to advancement in organizations. And so most of the work that I did with this book and my first book, Women Don't Ask, was looking about how men and women approached negotiation differently. And kind of the bottom line is that women are what less likely to negotiate than men over all kinds of things, like pay like opportunities for advancement like the next promotion. And it really harms them in the workplace because men are always out there asking for it and organizations reward that. And so the book is was really about shedding light on this disparity and what organizations could do about it and what women can do about it themselves, how they can learn to negotiate more effectively. >>What did you learn when you were writing the book around? Some of the use cases of best practices that women were doing in the field was it. Maura aggressive style has a more collaborative. You're seeing a lot more solidarity amongst women themselves, and men are getting involved. A lot of companies are kind of talking the game summer walking, the talk. What the big findings that you've learned >>well, I'd say that the approach is that women use are a lot different than the approaches that menus. And it's because our world lets men do a lot of different things. It lets them engage in a cooperative way, lets them be very competitive. But our world has a very narrow view about what's acceptable behavior for women. I often call it a tight rope because women are kind of balancing that they need to go out and assert themselves. But they have to do it in a way that our side, a society finds acceptable, and that that tight rope constrains women and doesn't allow them to be their authentic Selves on DSO. It makes it difficult for women to navigate that. What's your >>take on the the balancing of being aggressive and the pressure companies have to, you know, keep the women population certainly pipeline in tech. We see it all the time and the whole me to thing and the pressure goes on because norms were forming, right? So is there any new data that you can share around how, with norms and for forming and what men can do? Particularly, I get this question a lot, and I always ask myself, What am I doing? Can I do something different? Because I want to be inclusive and I want to do the right thing. But sometimes I don't know what to do. >>Yeah, of course. And it's really important that men get involved in this conversation as allies and, like you said, sometimes men but don't know what to do because they feel like maybe they don't have standing to be in the conversation when it's about women and weigh all need men, his allies. If women are gonna try to reach equality, ATT's some point. But the new data really suggests negotiation may be playing a role. The work that show Sandberg lean in, But the newest work that we have shows that actually the day to day things that happen at work that's holding women back. So let me tell you about that. So what we find is if you think about your calendar and what you do all day there a task that you can classify as being promotable, that is, they're really your core job. Responsibility there noticed, rewarded. But there's glass of other things that happen in your organization that are often below the surface that are important to dio valued but actually not rewarded. And what our research finds is that men spend much more time than women at the tasks that are these promotable task that rewarded women spend much more time than men on these tasks that we call non promotable that are not rewarded. And it's really holding women back. And how men can help is that the reason that women are doing these tasks is because everyone is asking them to do these tasks. And so what men can do is start asking men to do some of these things that are important but yet not rewarded because the portfolio's now are really out of balance and women are really shouldering the burden of these tasks disproportionately. >>So get on the wave of the promotional off the promotional oriented things that Maura and the man can come and pick up the slack on some of the things that were delegated to the women because they could order the kitchen food or whatever >>or help others with their work. Someone has to hire the summer intern. Someone has to organize events. Someone has to resolve underlying conflicts. Those are all really important things. Women get tasked with them, and that really doesn't allow them to focus on their core job responsibilities. And so men can step up to the blade, stop, do it, start doing their fair share of that work, and really then allow women to reach their full >>potential. I've been thinking a lot about this lately around how collaboration software, how collaborative teams. You started to see the big successful coming like Amazon to pizza team concept. Smaller teams, Team Orient. If you're doing it, you're in a teen. These things go. You've given you get so I think it's probably a better environment. Is that happening or no? It's >>unclear how teams kind of shake out for women in this setting, because there's actually some research that shows when a team produces an output and the supervisor trying to figure out, like who really made the output? Who was the valued player on the team. They often overvalue the contributions of men and undervalued the contributions of women. So actually, team projects can be problematic if women don't get their fair share of >>bias. Is everywhere >>biases everywhere. And you know it's not that people are trying discriminate against women. It's just that it's a subconscious, implicit bias and so affects our judgments in ways that we don't even realize. >>It's actually probably amplifies it. You know, the game are gaining a lot of things on digital indigenous communities. We see a lot where people are hiding behind their avatars. Yeah, that's also pretty bad environment. So we've been doing a lot of thinking and reporting around communities and data. I want to get your thoughts is I never really probed at this. But is there any economic incentives? And after you're an economics professor, you seeing things like crypto economics and tokens and all kinds of new things is a potential path towards creating an incentive system that's cutting edge what's progressive thinking around any kind of incentive systems for organizations or individuals. >>Well, when you think about incentives and maybe an economist, I think about those a lot, and I emerged that with my work on various to women's advancement, I think incentives is one area that you can actually play a big role. And that is that Organizational leaders should be incentive fied incentivized to see that they have equal advancement for their male and female employees in their workforce. Because if they don't it means they're losing out on this potential that women have, that they aren't able to fully be productive. And so that's, I think, the place. I think that incentives can really be important, >>a great leader and he said, and I'm quoting him. But I feel the same way says. Our incentive is business. Get a better outcome with them. We include women, give data, goes Yeah, we make software and have people that use our software with women I don't wanna have. So I'm like, Oh, that makes a lot of sense. Biases should be in there. Four Women for women by women for women >>and women spend more money as consumers than men. And so having women on teams allows them to see perspectives that men may not see, and so it can really add two new innovative thinking that hadn't been there before by including women. >>Well, I'm excited that this there's a little bit of movement in tech we're starting to see, certainly in venture capital, starting to see a lot more when you come into the board room work to do. But I think there's a nice sign that there's more jobs that are computer related that aren't just coding. That's male dominant pretty much now and still still is for a while. But there's a lot more skills, all kinds of range now in computer science. It's interesting. How is that affecting some of the new pipeline ing? >>Yeah, well, I think the good news is that there are is increasing levels of women's attainment in stem fields. And so there are more and more female workers entering the labor market today. Way just have to make sure that those workers are valued and feel included when they do doing tech companies. Otherwise they will leave because what happens unfortunately, sometimes in tech is it doesn't feel inclusive for women. And the quick rate for women in tech is over over twice the rate for men, and some of the reasons are is they're not feeling valued in their positions. They're not seeing their advancement. And so with this new wave of female workers, we have to make sure that those workplaces are ready to accept them and include them. >>That's great. Well, ask for it is a great book. I went through it and it's great handbook. I learned a lot. It really is a handbook around. Just standing up and taken what you can. You got some new, but you got a new book you're working on. What's that gonna look like? What if some of the themes in the new book >>Yeah. So the new book is on these promotable tasks, and the way I like to think about it is there's so much attention toe work, life balance, you know? How do you manage both of those with your career, your family? How does that work? But our work actually focuses on work, work, balance, and what remains is paying attention to the things that you do at work. Making sure that those things that you're doing are the things that are most valuable for your employer and are gonna be most valuable for your career. So it's a really different focus on the day to day ways that you spend your time at work and how that can propel women to the next level. >>That's awesome, Linda. Thanks for coming. I appreciate it. What do you think of the event here? Cronies? Global cyber security summit. >>Well, I got to say it's not my typical event, but I'm having a good time learning more about what's happening in the tech industry today. >>Cyber protection, Certainly a cutting edge issue. And certainly on the East Coast in Washington D certainly with national defense and all kinds of things happening, Ransomware is a big topic that kicked around here absolutely getting taken out like, Oh, my God. Yeah. Bitcoin in return for taking your systems out, >>all kinds of new stuff to add to my tool kit. >>Great to have you on. Thanks for your insight. Thanks for sharing. Appreciate it. I'm John for here at the Cube. We're here in Miami Beach for the Cronus Cyber Protection Conference. Thank you for watching

(upbeat electronic music) >> Hello everyone, welcome to this CUBE conversation here from the studios in the CUBE in Palo Alto, California. I'm John Furrier, your host. We're featuring a startup, Astronomer. Astronomer.io is the URL, check it out. And we're going to have a great conversation around one of the most important topics hitting the industry, and that is the future of machine learning and AI, and the data that powers it underneath it. There's a lot of things that need to get done, and we're excited to have some of the co-founders of Astronomer here. Viraj Parekh, who is co-founder of Astronomer, and Paola Peraza Calderon, another co-founder, both with Astronomer. Thanks for coming on. First of all, how many co-founders do you guys have? >> You know, I think the answer's around six or seven. I forget the exact, but there's really been a lot of people around the table who've worked very hard to get this company to the point that it's at. We have long ways to go, right? But there's been a lot of people involved that have been absolutely necessary for the path we've been on so far. >> Thanks for that, Viraj, appreciate that. The first question I want to get out on the table, and then we'll get into some of the details, is take a minute to explain what you guys are doing. How did you guys get here? Obviously, multiple co-founders, sounds like a great project. The timing couldn't have been better. ChatGPT has essentially done so much public relations for the AI industry to kind of highlight this shift that's happening. It's real, we've been chronicalizing, take a minute to explain what you guys do. >> Yeah, sure, we can get started. So, yeah, when Viraj and I joined Astronomer in 2017, we really wanted to build a business around data, and we were using an open source project called Apache Airflow that we were just using sort of as customers ourselves. And over time, we realized that there was actually a market for companies who use Apache Airflow, which is a data pipeline management tool, which we'll get into, and that running Airflow is actually quite challenging, and that there's a big opportunity for us to create a set of commercial products and an opportunity to grow that open source community and actually build a company around that. So the crux of what we do is help companies run data pipelines with Apache Airflow. And certainly we've grown in our ambitions beyond that, but that's sort of the crux of what we do for folks. >> You know, data orchestration, data management has always been a big item in the old classic data infrastructure. But with AI, you're seeing a lot more emphasis on scale, tuning, training. Data orchestration is the center of the value proposition, when you're looking at coordinating resources, it's one of the most important things. Can you guys explain what data orchestration entails? What does it mean? Take us through the definition of what data orchestration entails. >> Yeah, for sure. I can take this one, and Viraj, feel free to jump in. So if you google data orchestration, here's what you're going to get. You're going to get something that says, "Data orchestration is the automated process" "for organizing silo data from numerous" "data storage points, standardizing it," "and making it accessible and prepared for data analysis." And you say, "Okay, but what does that actually mean," right, and so let's give sort of an an example. So let's say you're a business and you have sort of the following basic asks of your data team, right? Okay, give me a dashboard in Sigma, for example, for the number of customers or monthly active users, and then make sure that that gets updated on an hourly basis. And then number two, a consistent list of active customers that I have in HubSpot so that I can send them a monthly product newsletter, right? Two very basic asks for all sorts of companies and organizations. And when that data team, which has data engineers, data scientists, ML engineers, data analysts get that request, they're looking at an ecosystem of data sources that can help them get there, right? And that includes application databases, for example, that actually have in product user behavior and third party APIs from tools that the company uses that also has different attributes and qualities of those customers or users. And that data team needs to use tools like Fivetran to ingest data, a data warehouse, like Snowflake or Databricks to actually store that data and do analysis on top of it, a tool like DBT to do transformations and make sure that data is standardized in the way that it needs to be, a tool like Hightouch for reverse ETL. I mean, we could go on and on. There's so many partners of ours in this industry that are doing really, really exciting and critical things for those data movements. And the whole point here is that data teams have this plethora of tooling that they use to both ingest the right data and come up with the right interfaces to transform and interact with that data. And data orchestration, in our view, is really the heartbeat of all of those processes, right? And tangibly the unit of data orchestration is a data pipeline, a set of tasks or jobs that each do something with data over time and eventually run that on a schedule to make sure that those things are happening continuously as time moves on and the company advances. And so, for us, we're building a business around Apache Airflow, which is a workflow management tool that allows you to author, run, and monitor data pipelines. And so when we talk about data orchestration, we talk about sort of two things. One is that crux of data pipelines that, like I said, connect that large ecosystem of data tooling in your company. But number two, it's not just that data pipeline that needs to run every day, right? And Viraj will probably touch on this as we talk more about Astronomer and our value prop on top of Airflow. But then it's all the things that you need to actually run data and production and make sure that it's trustworthy, right? So it's actually not just that you're running things on a schedule, but it's also things like CICD tooling, secure secrets management, user permissions, monitoring, data lineage, documentation, things that enable other personas in your data team to actually use those tools. So long-winded way of saying that it's the heartbeat, we think, of of the data ecosystem, and certainly goes beyond scheduling, but again, data pipelines are really at the center of it. >> One of the things that jumped out, Viraj, if you can get into this, I'd like to hear more about how you guys look at all those little tools that are out. You mentioned a variety of things. You look at the data infrastructure, it's not just one stack. You've got an analytic stack, you've got a realtime stack, you've got a data lake stack, you got an AI stack potentially. I mean you have these stacks now emerging in the data world that are fundamental, that were once served by either a full package, old school software, and then a bunch of point solution. You mentioned Fivetran there, I would say in the analytics stack. Then you got S3, they're on the data lake stack. So all these things are kind of munged together. >> Yeah. >> How do you guys fit into that world? You make it easier, or like, what's the deal? >> Great question, right? And you know, I think that one of the biggest things we've found in working with customers over the last however many years is that if a data team is using a bunch of tools to get what they need done, and the number of tools they're using is growing exponentially and they're kind of roping things together here and there, that's actually a sign of a productive team, not a bad thing, right? It's because that team is moving fast. They have needs that are very specific to them, and they're trying to make something that's exactly tailored to their business. So a lot of times what we find is that customers have some sort of base layer, right? That's kind of like, it might be they're running most of the things in AWS, right? And then on top of that, they'll be using some of the things AWS offers, things like SageMaker, Redshift, whatever, but they also might need things that their cloud can't provide. Something like Fivetran, or Hightouch, those are other tools. And where data orchestration really shines, and something that we've had the pleasure of helping our customers build, is how do you take all those requirements, all those different tools and whip them together into something that fulfills a business need? So that somebody can read a dashboard and trust the number that it says, or somebody can make sure that the right emails go out to their customers. And Airflow serves as this amazing kind of glue between that data stack, right? It's to make it so that for any use case, be it ELT pipelines, or machine learning, or whatever, you need different things to do them, and Airflow helps tie them together in a way that's really specific for a individual business' needs. >> Take a step back and share the journey of what you guys went through as a company startup. So you mentioned Apache, open source. I was just having an interview with a VC, we were talking about foundational models. You got a lot of proprietary and open source development going on. It's almost the iPhone/Android moment in this whole generative space and foundational side. This is kind of important, the open source piece of it. Can you share how you guys started? And I can imagine your customers probably have their hair on fire and are probably building stuff on their own. Are you guys helping them? Take us through, 'cause you guys are on the front end of a big, big wave, and that is to make sense of the chaos, rain it in. Take us through your journey and why this is important. >> Yeah, Paola, I can take a crack at this, then I'll kind of hand it over to you to fill in whatever I miss in details. But you know, like Paola is saying, the heart of our company is open source, because we started using Airflow as an end user and started to say like, "Hey wait a second," "more and more people need this." Airflow, for background, started at Airbnb, and they were actually using that as a foundation for their whole data stack. Kind of how they made it so that they could give you recommendations, and predictions, and all of the processes that needed orchestrated. Airbnb created Airflow, gave it away to the public, and then fast forward a couple years and we're building a company around it, and we're really excited about that. >> That's a beautiful thing. That's exactly why open source is so great. >> Yeah, yeah. And for us, it's really been about watching the community and our customers take these problems, find a solution to those problems, standardize those solutions, and then building on top of that, right? So we're reaching to a point where a lot of our earlier customers who started to just using Airflow to get the base of their BI stack down and their reporting in their ELP infrastructure, they've solved that problem and now they're moving on to things like doing machine learning with their data, because now that they've built that foundation, all the connective tissue for their data arriving on time and being orchestrated correctly is happening, they can build a layer on top of that. And it's just been really, really exciting kind of watching what customers do once they're empowered to pick all the tools that they need, tie them together in the way they need to, and really deliver real value to their business. >> Can you share some of the use cases of these customers? Because I think that's where you're starting to see the innovation. What are some of the companies that you're working with, what are they doing? >> Viraj, I'll let you take that one too. (group laughs) >> So you know, a lot of it is... It goes across the gamut, right? Because it doesn't matter what you are, what you're doing with data, it needs to be orchestrated. So there's a lot of customers using us for their ETL and ELT reporting, right? Just getting data from other disparate sources into one place and then building on top of that. Be it building dashboards, answering questions for the business, building other data products and so on and so forth. From there, these use cases evolve a lot. You do see folks doing things like fraud detection, because Airflow's orchestrating how transactions go, transactions get analyzed. They do things like analyzing marketing spend to see where your highest ROI is. And then you kind of can't not talk about all of the machine learning that goes on, right? Where customers are taking data about their own customers, kind of analyze and aggregating that at scale, and trying to automate decision making processes. So it goes from your most basic, what we call data plumbing, right? Just to make sure data's moving as needed, all the ways to your more exciting expansive use cases around automated decision making and machine learning. >> And I'd say, I mean, I'd say that's one of the things that I think gets me most excited about our future, is how critical Airflow is to all of those processes, and I think when you know a tool is valuable is when something goes wrong and one of those critical processes doesn't work. And we know that our system is so mission critical to answering basic questions about your business and the growth of your company for so many organizations that we work with. So it's, I think, one of the things that gets Viraj and I and the rest of our company up every single morning is knowing how important the work that we do for all of those use cases across industries, across company sizes, and it's really quite energizing. >> It was such a big focus this year at AWS re:Invent, the role of data. And I think one of the things that's exciting about the open AI and all the movement towards large language models is that you can integrate data into these models from outside. So you're starting to see the integration easier to deal with. Still a lot of plumbing issues. So a lot of things happening. So I have to ask you guys, what is the state of the data orchestration area? Is it ready for disruption? Has it already been disrupted? Would you categorize it as a new first inning kind of opportunity, or what's the state of the data orchestration area right now? Both technically and from a business model standpoint. How would you guys describe that state of the market? >> Yeah, I mean, I think in a lot of ways, in some ways I think we're category creating. Schedulers have been around for a long time. I released a data presentation sort of on the evolution of going from something like Kron, which I think was built in like the 1970s out of Carnegie Mellon. And that's a long time ago, that's 50 years ago. So sort of like the basic need to schedule and do something with your data on a schedule is not a new concept. But to our point earlier, I think everything that you need around your ecosystem, first of all, the number of data tools and developer tooling that has come out industry has 5X'd over the last 10 years. And so obviously as that ecosystem grows, and grows, and grows, and grows, the need for orchestration only increases. And I think, as Astronomer, I think we... And we work with so many different types of companies, companies that have been around for 50 years, and companies that got started not even 12 months ago. And so I think for us it's trying to, in a ways, category create and adjust sort of what we sell and the value that we can provide for companies all across that journey. There are folks who are just getting started with orchestration, and then there's folks who have such advanced use case, 'cause they're hitting sort of a ceiling and only want to go up from there. And so I think we, as a company, care about both ends of that spectrum, and certainly want to build and continue building products for companies of all sorts, regardless of where they are on the maturity curve of data orchestration. >> That's a really good point, Paola. And I think the other thing to really take into account is it's the companies themselves, but also individuals who have to do their jobs. If you rewind the clock like 5 or 10 years ago, data engineers would be the ones responsible for orchestrating data through their org. But when we look at our customers today, it's not just data engineers anymore. There's data analysts who sit a lot closer to the business, and the data scientists who want to automate things around their models. So this idea that orchestration is this new category is right on the money. And what we're finding is the need for it is spreading to all parts of the data team, naturally where Airflow's emerged as an open source standard and we're hoping to take things to the next level. >> That's awesome. We've been up saying that the data market's kind of like the SRE with servers, right? You're going to need one person to deal with a lot of data, and that's data engineering, and then you're got to have the practitioners, the democratization. Clearly that's coming in what you're seeing. So I have to ask, how do you guys fit in from a value proposition standpoint? What's the pitch that you have to customers, or is it more inbound coming into you guys? Are you guys doing a lot of outreach, customer engagements? I'm sure they're getting a lot of great requirements from customers. What's the current value proposition? How do you guys engage? >> Yeah, I mean, there's so many... Sorry, Viraj, you can jump in. So there's so many companies using Airflow, right? So the baseline is that the open source project that is Airflow that came out of Airbnb, over five years ago at this point, has grown exponentially in users and continues to grow. And so the folks that we sell to primarily are folks who are already committed to using Apache Airflow, need data orchestration in their organization, and just want to do it better, want to do it more efficiently, want to do it without managing that infrastructure. And so our baseline proposition is for those organizations. Now to Viraj's point, obviously I think our ambitions go beyond that, both in terms of the personas that we addressed and going beyond that data engineer, but really it's to start at the baseline, as we continue to grow our our company, it's really making sure that we're adding value to folks using Airflow and help them do so in a better way, in a larger way, in a more efficient way, and that's really the crux of who we sell to. And so to answer your question on, we get a lot of inbound because they're... >> You have a built in audience. (laughs) >> The world that use it. Those are the folks who we talk to and come to our website and chat with us and get value from our content. I mean, the power of the opensource community is really just so, so big, and I think that's also one of the things that makes this job fun. >> And you guys are in a great position. Viraj, you can comment a little, get your reaction. There's been a big successful business model to starting a company around these big projects for a lot of reasons. One is open source is continuing to be great, but there's also supply chain challenges in there. There's also we want to continue more innovation and more code and keeping it free and and flowing. And then there's the commercialization of productizing it, operationalizing it. This is a huge new dynamic, I mean, in the past 5 or so years, 10 years, it's been happening all on CNCF from other areas like Apache, Linux Foundation, they're all implementing this. This is a huge opportunity for entrepreneurs to do this. >> Yeah, yeah. Open source is always going to be core to what we do, because we wouldn't exist without the open source community around us. They are huge in numbers. Oftentimes they're nameless people who are working on making something better in a way that everybody benefits from it. But open source is really hard, especially if you're a company whose core competency is running a business, right? Maybe you're running an e-commerce business, or maybe you're running, I don't know, some sort of like, any sort of business, especially if you're a company running a business, you don't really want to spend your time figuring out how to run open source software. You just want to use it, you want to use the best of it, you want to use the community around it, you want to be able to google something and get answers for it, you want the benefits of open source. You don't have the time or the resources to invest in becoming an expert in open source, right? And I think that dynamic is really what's given companies like us an ability to kind of form businesses around that in the sense that we'll make it so people get the best of both worlds. You'll get this vast open ecosystem that you can build on top of, that you can benefit from, that you can learn from. But you won't have to spend your time doing undifferentiated heavy lifting. You can do things that are just specific to your business. >> It's always been great to see that business model evolve. We used a debate 10 years ago, can there be another Red Hat? And we said, not really the same, but there'll be a lot of little ones that'll grow up to be big soon. Great stuff. Final question, can you guys share the history of the company? The milestones of Astromer's journey in data orchestration? >> Yeah, we could. So yeah, I mean, I think, so Viraj and I have obviously been at Astronomer along with our other founding team and leadership folks for over five years now. And it's been such an incredible journey of learning, of hiring really amazing people, solving, again, mission critical problems for so many types of organizations. We've had some funding that has allowed us to invest in the team that we have and in the software that we have, and that's been really phenomenal. And so that investment, I think, keeps us confident, even despite these sort of macroeconomic conditions that we're finding ourselves in. And so honestly, the milestones for us are focusing on our product, focusing on our customers over the next year, focusing on that market for us that we know can get valuable out of what we do, and making developers' lives better, and growing the open source community and making sure that everything that we're doing makes it easier for folks to get started, to contribute to the project and to feel a part of the community that we're cultivating here. >> You guys raised a little bit of money. How much have you guys raised? >> Don't know what the total is, but it's in the ballpark over $200 million. It feels good to... >> A little bit of capital. Got a little bit of cap to work with there. Great success. I know as a Series C Financing, you guys have been down. So you're up and running, what's next? What are you guys looking to do? What's the big horizon look like for you from a vision standpoint, more hiring, more product, what is some of the key things you're looking at doing? >> Yeah, it's really a little of all of the above, right? Kind of one of the best and worst things about working at earlier stage startups is there's always so much to do and you often have to just kind of figure out a way to get everything done. But really investing our product over the next, at least over the course of our company lifetime. And there's a lot of ways we want to make it more accessible to users, easier to get started with, easier to use, kind of on all areas there. And really, we really want to do more for the community, right, like I was saying, we wouldn't be anything without the large open source community around us. And we want to figure out ways to give back more in more creative ways, in more code driven ways, in more kind of events and everything else that we can keep those folks galvanized and just keep them happy using Airflow. >> Paola, any final words as we close out? >> No, I mean, I'm super excited. I think we'll keep growing the team this year. We've got a couple of offices in the the US, which we're excited about, and a fully global team that will only continue to grow. So Viraj and I are both here in New York, and we're excited to be engaging with our coworkers in person finally, after years of not doing so. We've got a bustling office in San Francisco as well. So growing those teams and continuing to hire all over the world, and really focusing on our product and the open source community is where our heads are at this year. So, excited. >> Congratulations. 200 million in funding, plus. Good runway, put that money in the bank, squirrel it away. It's a good time to kind of get some good interest on it, but still grow. Congratulations on all the work you guys do. We appreciate you and the open source community does, and good luck with the venture, continue to be successful, and we'll see you at the Startup Showcase. >> Thank you. >> Yeah, thanks so much, John. Appreciate it. >> Okay, that's the CUBE Conversation featuring astronomer.io, that's the website. Astronomer is doing well. Multiple rounds of funding, over 200 million in funding. Open source continues to lead the way in innovation. Great business model, good solution for the next gen cloud scale data operations, data stacks that are emerging. I'm John Furrier, your host, thanks for watching. (soft upbeat music)

(soft music) >> Hello everyone, welcome to this Cube conversation here from the studios of theCube in Palo Alto, California. John Furrier, your host. We're featuring a startup, Astronomer, astronomer.io is the url. Check it out. And we're going to have a great conversation around one of the most important topics hitting the industry, and that is the future of machine learning and AI and the data that powers it underneath it. There's a lot of things that need to get done, and we're excited to have some of the co-founders of Astronomer here. Viraj Parekh, who is co-founder and Paola Peraza Calderon, another co-founder, both with Astronomer. Thanks for coming on. First of all, how many co-founders do you guys have? >> You know, I think the answer's around six or seven. I forget the exact, but there's really been a lot of people around the table, who've worked very hard to get this company to the point that it's at. And we have long ways to go, right? But there's been a lot of people involved that are, have been absolutely necessary for the path we've been on so far. >> Thanks for that, Viraj, appreciate that. The first question I want to get out on the table, and then we'll get into some of the details, is take a minute to explain what you guys are doing. How did you guys get here? Obviously, multiple co-founders sounds like a great project. The timing couldn't have been better. ChatGPT has essentially done so much public relations for the AI industry. Kind of highlight this shift that's happening. It's real. We've been chronologicalizing, take a minute to explain what you guys do. >> Yeah, sure. We can get started. So yeah, when Astronomer, when Viraj and I joined Astronomer in 2017, we really wanted to build a business around data and we were using an open source project called Apache Airflow, that we were just using sort of as customers ourselves. And over time, we realized that there was actually a market for companies who use Apache Airflow, which is a data pipeline management tool, which we'll get into. And that running Airflow is actually quite challenging and that there's a lot of, a big opportunity for us to create a set of commercial products and opportunity to grow that open source community and actually build a company around that. So the crux of what we do is help companies run data pipelines with Apache Airflow. And certainly we've grown in our ambitions beyond that, but that's sort of the crux of what we do for folks. >> You know, data orchestration, data management has always been a big item, you know, in the old classic data infrastructure. But with AI you're seeing a lot more emphasis on scale, tuning, training. You know, data orchestration is the center of the value proposition when you're looking at coordinating resources, it's one of the most important things. Could you guys explain what data orchestration entails? What does it mean? Take us through the definition of what data orchestration entails. >> Yeah, for sure. I can take this one and Viraj feel free to jump in. So if you google data orchestration, you know, here's what you're going to get. You're going to get something that says, data orchestration is the automated process for organizing silo data from numerous data storage points to organizing it and making it accessible and prepared for data analysis. And you say, okay, but what does that actually mean, right? And so let's give sort of an example. So let's say you're a business and you have sort of the following basic asks of your data team, right? Hey, give me a dashboard in Sigma, for example, for the number of customers or monthly active users and then make sure that that gets updated on an hourly basis. And then number two, a consistent list of active customers that I have in HubSpot so that I can send them a monthly product newsletter, right? Two very basic asks for all sorts of companies and organizations. And when that data team, which has data engineers, data scientists, ML engineers, data analysts get that request, they're looking at an ecosystem of data sources that can help them get there, right? And that includes application databases, for example, that actually have end product user behavior and third party APIs from tools that the company uses that also has different attributes and qualities of those customers or users. And that data team needs to use tools like Fivetran, to ingest data, a data warehouse like Snowflake or Databricks to actually store that data and do analysis on top of it, a tool like DBT to do transformations and make sure that that data is standardized in the way that it needs to be, a tool like Hightouch for reverse ETL. I mean, we could go on and on. There's so many partners of ours in this industry that are doing really, really exciting and critical things for those data movements. And the whole point here is that, you know, data teams have this plethora of tooling that they use to both ingest the right data and come up with the right interfaces to transform and interact with that data. And data orchestration in our view is really the heartbeat of all of those processes, right? And tangibly the unit of data orchestration, you know, is a data pipeline, a set of tasks or jobs that each do something with data over time and eventually run that on a schedule to make sure that those things are happening continuously as time moves on. And, you know, the company advances. And so, you know, for us, we're building a business around Apache Airflow, which is a workflow management tool that allows you to author, run and monitor data pipelines. And so when we talk about data orchestration, we talk about sort of two things. One is that crux of data pipelines that, like I said, connect that large ecosystem of data tooling in your company. But number two, it's not just that data pipeline that needs to run every day, right? And Viraj will probably touch on this as we talk more about Astronomer and our value prop on top of Airflow. But then it's all the things that you need to actually run data and production and make sure that it's trustworthy, right? So it's actually not just that you're running things on a schedule, but it's also things like CI/CD tooling, right? Secure secrets management, user permissions, monitoring, data lineage, documentation, things that enable other personas in your data team to actually use those tools. So long-winded way of saying that, it's the heartbeat that we think of the data ecosystem and certainly goes beyond scheduling, but again, data pipelines are really at the center of it. >> You know, one of the things that jumped out Viraj, if you can get into this, I'd like to hear more about how you guys look at all those little tools that are out there. You mentioned a variety of things. You know, if you look at the data infrastructure, it's not just one stack. You've got an analytic stack, you've got a realtime stack, you've got a data lake stack, you got an AI stack potentially. I mean you have these stacks now emerging in the data world that are >> Yeah. - >> fundamental, but we're once served by either a full package, old school software, and then a bunch of point solution. You mentioned Fivetran there, I would say in the analytics stack. Then you got, you know, S3, they're on the data lake stack. So all these things are kind of munged together. >> Yeah. >> How do you guys fit into that world? You make it easier or like, what's the deal? >> Great question, right? And you know, I think that one of the biggest things we've found in working with customers over, you know, the last however many years, is that like if a data team is using a bunch of tools to get what they need done and the number of tools they're using is growing exponentially and they're kind of roping things together here and there, that's actually a sign of a productive team, not a bad thing, right? It's because that team is moving fast. They have needs that are very specific to them and they're trying to make something that's exactly tailored to their business. So a lot of times what we find is that customers have like some sort of base layer, right? That's kind of like, you know, it might be they're running most of the things in AWS, right? And then on top of that, they'll be using some of the things AWS offers, you know, things like SageMaker, Redshift, whatever. But they also might need things that their Cloud can't provide, you know, something like Fivetran or Hightouch or anything of those other tools and where data orchestration really shines, right? And something that we've had the pleasure of helping our customers build, is how do you take all those requirements, all those different tools and whip them together into something that fulfills a business need, right? Something that makes it so that somebody can read a dashboard and trust the number that it says or somebody can make sure that the right emails go out to their customers. And Airflow serves as this amazing kind of glue between that data stack, right? It's to make it so that for any use case, be it ELT pipelines or machine learning or whatever, you need different things to do them and Airflow helps tie them together in a way that's really specific for a individual business's needs. >> Take a step back and share the journey of what your guys went through as a company startup. So you mentioned Apache open source, you know, we were just, I was just having an interview with the VC, we were talking about foundational models. You got a lot of proprietary and open source development going on. It's almost the iPhone, Android moment in this whole generative space and foundational side. This is kind of important, the open source piece of it. Can you share how you guys started? And I can imagine your customers probably have their hair on fire and are probably building stuff on their own. How do you guys, are you guys helping them? Take us through, 'cuz you guys are on the front end of a big, big wave and that is to make sense of the chaos, reigning it in. Take us through your journey and why this is important. >> Yeah Paola, I can take a crack at this and then I'll kind of hand it over to you to fill in whatever I miss in details. But you know, like Paola is saying, the heart of our company is open source because we started using Airflow as an end user and started to say like, "Hey wait a second". Like more and more people need this. Airflow, for background, started at Airbnb and they were actually using that as the foundation for their whole data stack. Kind of how they made it so that they could give you recommendations and predictions and all of the processes that need to be or needed to be orchestrated. Airbnb created Airflow, gave it away to the public and then, you know, fast forward a couple years and you know, we're building a company around it and we're really excited about that. >> That's a beautiful thing. That's exactly why open source is so great. >> Yeah, yeah. And for us it's really been about like watching the community and our customers take these problems, find solution to those problems, build standardized solutions, and then building on top of that, right? So we're reaching to a point where a lot of our earlier customers who started to just using Airflow to get the base of their BI stack down and their reporting and their ELP infrastructure, you know, they've solved that problem and now they're moving onto things like doing machine learning with their data, right? Because now that they've built that foundation, all the connective tissue for their data arriving on time and being orchestrated correctly is happening, they can build the layer on top of that. And it's just been really, really exciting kind of watching what customers do once they're empowered to pick all the tools that they need, tie them together in the way they need to, and really deliver real value to their business. >> Can you share some of the use cases of these customers? Because I think that's where you're starting to see the innovation. What are some of the companies that you're working with, what are they doing? >> Raj, I'll let you take that one too. (all laughing) >> Yeah. (all laughing) So you know, a lot of it is, it goes across the gamut, right? Because all doesn't matter what you are, what you're doing with data, it needs to be orchestrated. So there's a lot of customers using us for their ETL and ELT reporting, right? Just getting data from all the disparate sources into one place and then building on top of that, be it building dashboards, answering questions for the business, building other data products and so on and so forth. From there, these use cases evolve a lot. You do see folks doing things like fraud detection because Airflow's orchestrating how transactions go. Transactions get analyzed, they do things like analyzing marketing spend to see where your highest ROI is. And then, you know, you kind of can't not talk about all of the machine learning that goes on, right? Where customers are taking data about their own customers kind of analyze and aggregating that at scale and trying to automate decision making processes. So it goes from your most basic, what we call like data plumbing, right? Just to make sure data's moving as needed. All the ways to your more exciting and sexy use cases around like automated decision making and machine learning. >> And I'd say, I mean, I'd say that's one of the things that I think gets me most excited about our future is how critical Airflow is to all of those processes, you know? And I think when, you know, you know a tool is valuable is when something goes wrong and one of those critical processes doesn't work. And we know that our system is so mission critical to answering basic, you know, questions about your business and the growth of your company for so many organizations that we work with. So it's, I think one of the things that gets Viraj and I, and the rest of our company up every single morning, is knowing how important the work that we do for all of those use cases across industries, across company sizes. And it's really quite energizing. >> It was such a big focus this year at AWS re:Invent, the role of data. And I think one of the things that's exciting about the open AI and all the movement towards large language models, is that you can integrate data into these models, right? From outside, right? So you're starting to see the integration easier to deal with, still a lot of plumbing issues. So a lot of things happening. So I have to ask you guys, what is the state of the data orchestration area? Is it ready for disruption? Is it already been disrupted? Would you categorize it as a new first inning kind of opportunity or what's the state of the data orchestration area right now? Both, you know, technically and from a business model standpoint, how would you guys describe that state of the market? >> Yeah, I mean I think, I think in a lot of ways we're, in some ways I think we're categoric rating, you know, schedulers have been around for a long time. I recently did a presentation sort of on the evolution of going from, you know, something like KRON, which I think was built in like the 1970s out of Carnegie Mellon. And you know, that's a long time ago. That's 50 years ago. So it's sort of like the basic need to schedule and do something with your data on a schedule is not a new concept. But to our point earlier, I think everything that you need around your ecosystem, first of all, the number of data tools and developer tooling that has come out the industry has, you know, has some 5X over the last 10 years. And so obviously as that ecosystem grows and grows and grows and grows, the need for orchestration only increases. And I think, you know, as Astronomer, I think we, and there's, we work with so many different types of companies, companies that have been around for 50 years and companies that got started, you know, not even 12 months ago. And so I think for us, it's trying to always category create and adjust sort of what we sell and the value that we can provide for companies all across that journey. There are folks who are just getting started with orchestration and then there's folks who have such advanced use case 'cuz they're hitting sort of a ceiling and only want to go up from there. And so I think we as a company, care about both ends of that spectrum and certainly have want to build and continue building products for companies of all sorts, regardless of where they are on the maturity curve of data orchestration. >> That's a really good point Paola. And I think the other thing to really take into account is it's the companies themselves, but also individuals who have to do their jobs. You know, if you rewind the clock like five or 10 years ago, data engineers would be the ones responsible for orchestrating data through their org. But when we look at our customers today, it's not just data engineers anymore. There's data analysts who sit a lot closer to the business and the data scientists who want to automate things around their models. So this idea that orchestration is this new category is spot on, is right on the money. And what we're finding is it's spreading, the need for it, is spreading to all parts of the data team naturally where Airflows have emerged as an open source standard and we're hoping to take things to the next level. >> That's awesome. You know, we've been up saying that the data market's kind of like the SRE with servers, right? You're going to need one person to deal with a lot of data and that's data engineering and then you're going to have the practitioners, the democratization. Clearly that's coming in what you're seeing. So I got to ask, how do you guys fit in from a value proposition standpoint? What's the pitch that you have to customers or is it more inbound coming into you guys? Are you guys doing a lot of outreach, customer engagements? I'm sure they're getting a lot of great requirements from customers. What's the current value proposition? How do you guys engage? >> Yeah, I mean we've, there's so many, there's so many. Sorry Raj, you can jump in. - >> It's okay. So there's so many companies using Airflow, right? So our, the baseline is that the open source project that is Airflow that was, that came out of Airbnb, you know, over five years ago at this point, has grown exponentially in users and continues to grow. And so the folks that we sell to primarily are folks who are already committed to using Apache Airflow, need data orchestration in the organization and just want to do it better, want to do it more efficiently, want to do it without managing that infrastructure. And so our baseline proposition is for those organizations. Now to Raj's point, obviously I think our ambitions go beyond that, both in terms of the personas that we addressed and going beyond that data engineer, but really it's for, to start at the baseline. You know, as we continue to grow our company, it's really making sure that we're adding value to folks using Airflow and help them do so in a better way, in a larger way and a more efficient way. And that's really the crux of who we sell to. And so to answer your question on, we actually, we get a lot of inbound because they're are so many - >> A built-in audience. >> In the world that use it, that those are the folks who we talk to and come to our website and chat with us and get value from our content. I mean the power of the open source community is really just so, so big. And I think that's also one of the things that makes this job fun, so. >> And you guys are in a great position, Viraj, you can comment, to get your reaction. There's been a big successful business model to starting a company around these big projects for a lot of reasons. One is open source is continuing to be great, but there's also supply chain challenges in there. There's also, you know, we want to continue more innovation and more code and keeping it free and and flowing. And then there's the commercialization of product-izing it, operationalizing it. This is a huge new dynamic. I mean, in the past, you know, five or so years, 10 years, it's been happening all on CNCF from other areas like Apache, Linux Foundation, they're all implementing this. This is a huge opportunity for entrepreneurs to do this. >> Yeah, yeah. Open source is always going to be core to what we do because, you know, we wouldn't exist without the open source community around us. They are huge in numbers. Oftentimes they're nameless people who are working on making something better in a way that everybody benefits from it. But open source is really hard, especially if you're a company whose core competency is running a business, right? Maybe you're running e-commerce business or maybe you're running, I don't know, some sort of like any sort of business, especially if you're a company running a business, you don't really want to spend your time figuring out how to run open source software. You just want to use it, you want to use the best of it, you want to use the community around it. You want to take, you want to be able to google something and get answers for it. You want the benefits of open source. You don't want to have, you don't have the time or the resources to invest in becoming an expert in open source, right? And I think that dynamic is really what's given companies like us an ability to kind of form businesses around that, in the sense that we'll make it so people get the best of both worlds. You'll get this vast open ecosystem that you can build on top of, you can benefit from, that you can learn from, but you won't have to spend your time doing undifferentiated heavy lifting. You can do things that are just specific to your business. >> It's always been great to see that business model evolved. We used to debate 10 years ago, can there be another red hat? And we said, not really the same, but there'll be a lot of little ones that'll grow up to be big soon. Great stuff. Final question, can you guys share the history of the company, the milestones of the Astronomer's journey in data orchestration? >> Yeah, we could. So yeah, I mean, I think, so Raj and I have obviously been at astronomer along with our other founding team and leadership folks, for over five years now. And it's been such an incredible journey of learning, of hiring really amazing people. Solving again, mission critical problems for so many types of organizations. You know, we've had some funding that has allowed us to invest in the team that we have and in the software that we have. And that's been really phenomenal. And so that investment, I think, keeps us confident even despite these sort of macroeconomic conditions that we're finding ourselves in. And so honestly, the milestones for us are focusing on our product, focusing on our customers over the next year, focusing on that market for us, that we know can get value out of what we do. And making developers' lives better and growing the open source community, you know, and making sure that everything that we're doing makes it easier for folks to get started to contribute to the project and to feel a part of the community that we're cultivating here. >> You guys raised a little bit of money. How much have you guys raised? >> I forget what the total is, but it's in the ballpark of 200, over $200 million. So it feels good - >> A little bit of capital. Got a little bit of cash to work with there. Great success. I know it's a Series C financing, you guys been down, so you're up and running. What's next? What are you guys looking to do? What's the big horizon look like for you? And from a vision standpoint, more hiring, more product, what is some of the key things you're looking at doing? >> Yeah, it's really a little of all of the above, right? Like, kind of one of the best and worst things about working at earlier stage startups is there's always so much to do and you often have to just kind of figure out a way to get everything done, but really invest in our product over the next, at least the next, over the course of our company lifetime. And there's a lot of ways we wanting to just make it more accessible to users, easier to get started with, easier to use all kind of on all areas there. And really, we really want to do more for the community, right? Like I was saying, we wouldn't be anything without the large open source community around us. And we want to figure out ways to give back more in more creative ways, in more code driven ways and more kind of events and everything else that we can do to keep those folks galvanized and just keeping them happy using Airflow. >> Paola, any final words as we close out? >> No, I mean, I'm super excited. You know, I think we'll keep growing the team this year. We've got a couple of offices in the US which we're excited about, and a fully global team that will only continue to grow. So Viraj and I are both here in New York and we're excited to be engaging with our coworkers in person. Finally, after years of not doing so, we've got a bustling office in San Francisco as well. So growing those teams and continuing to hire all over the world and really focusing on our product and the open source community is where our heads are at this year, so. >> Congratulations. - >> Excited. 200 million in funding plus good runway. Put that money in the bank, squirrel it away. You know, it's good to kind of get some good interest on it, but still grow. Congratulations on all the work you guys do. We appreciate you and the open sourced community does and good luck with the venture. Continue to be successful and we'll see you at the Startup Showcase. >> Thank you. - >> Yeah, thanks so much, John. Appreciate it. - >> It's theCube conversation, featuring astronomer.io, that's the website. Astronomer is doing well. Multiple rounds of funding, over 200 million in funding. Open source continues to lead the way in innovation. Great business model. Good solution for the next gen, Cloud, scale, data operations, data stacks that are emerging. I'm John Furrier, your host. Thanks for watching. (soft music)

>> (narrator) From around the globe it's "theCUBE." With digital coverage of OutSystems-NextStep 2020. Brought to you by OutSystems. >> I'm Stu Miniman. And welcome back to thecubes coverage of OutSystems-NextStep. Of course, one of the items that we've been talking a lot in the industry is about how artificial intelligence machine learning are helping people as we go beyond what really human scale can do. And we need to be able to do things more machine scale to help us really dig into this topic. Happy to welcome to the program. First time, guest Antonio Alegria. He is the head of artificial intelligence at OutSystems. Antonio. Thanks so much for joining us. >> Thank you Stu. I'm really happy to be here and really talk a little bit about what we're doing at OutSystems to help our customers and how we're leveraging AI to get to those goals. >> ( Stu) Wonderful. So I saw ahead of the event, a short video that you did and talked about extreme agility with no limits. So, you know, before we dig into the product itself, maybe if you could just, you know, how should we be thinking about AI? You know, there's a broad spectrum, is that, you know, machine learning that there's various components in there, you listened to the big analyst firms, you know, the journey it's big steps and something that is pretty broad. So when we're talking about AI, you know, what does that mean to you? What does that mean to your customers? >> Yeah, so AI OutSystems really speaks to the vision and the core strategy we have for our product. Which is, you know, if you saw the keynote, you know, we talk about, you know, really enabling every company, even those that, you know, that had existed for decades, perhaps have a lot of legacy to become, you know, leading elite cloud software development companies. And really can develop digital solutions at scale really easily. But one thing we see, and then this is a big statistic. One of the things that limits CIO's the most, nowadays is really the lack of talent. Lack of, you know, engineering and software engineering, you know, ability and people that can do that. And there's a statistic that was reported by wall street journal. I saw it recently, perhaps last year. That said that according to federal jobs data in the U S, by the end of 2020 there would be about a million unfilled IET and software development jobs available, right. So there's this big problem. All of these companies really need to scale, really need to invest in digital systems. And so our belief at OutSystems, we've already been abstracting, and we've been focusing on automating as much as possible, the software development tools and applications that we use. We've already seen amazing stories of people coming from different backgrounds, really starting to develop really leading edge applications. And we want to take this to the next level. And we believe that artificial intelligence with machine learning, but also with other AI technologies that we're also taking advantage of can really help us get to a next stage of productivity. So from 10 X productivity to 100 X productivity. And we believe AI plays a role in three ways. We believe AI by learning from all of this data that we now collect in terms of, you know, the projects that are being developed. We are essentially trying to embed a tech lead, so to speak inside a product. And a tech lead that can help developers by guiding them, guiding the most junior ones, by automating some of the boring, repetitive tasks where by validating their work, making sure that they are using the best practices, making sure that it helps them as they scale to refactor their code to automatically design their architectures, things like that. >> (Stu) Wonderful . Antonio. Goncalo, stated it quite clearly in the interview that I had with him, it's really about enabling that next, you know, 10 million developers. We know that there is that skill gap, as you said, and you know, everybody right now, how can I do more? How can I react faster? So that's where, you know, the machine learning artificial intelligence should be able to help. So bring us inside. I know the platform itself has had, you know, guidance and the whole movement. You know, what we used to call low code. Was about simplifying things and allowing people to, you know, build faster. So bring us inside the product, you know, what are the enhancements? What are the new pieces? Some of the key items. >> Yeah. So one interesting thing, and I think one thing that I think OutSystems is really proud of being able to achieve is, if you look at how OutSystems has been using a AI within the platform. We started with introducing AI assistance within the, our software development environment, service studio, right? And so this capability we've been iterating it a lot. We've been evolving it, and now it's really able to accelerate significantly and guide novices, but also help pros dealing through the software development process and coding. By essentially.... and trying to infer and understanding their context and trying to infer their intent, and then automating the steps afterwards. And we do this by suggesting you the most likely, let's say function or code piece that you will need. But then at the next step, which we're introducing this year even better, which is we're trying to autofill most of the, let's say the variables and all of that, and the data flow that you need to collect. And so you get a very delightful, frictionless experience as you are coding. So you're closer to the business value even more than before. Now, this was just the first step. What you're seeing now and what we're announcing, and we're showing at this next step that we showed at the keynote, is that we are trying to fuse.... starting to fuse AI across the OutSystems products and across the software development life cycle. So we took this core technology that we use to guide developers and assist and automate their work. And we use the same capability to help developers, tech leads and architects to analyze the code. Learning from the bad patterns that exist learning from and receiving runtime information about crashes and performance. And inside the product that we call Architecture Dashboard. We're really able to give recommendations to these architects and tech leads, where should they evolve and improve their code. And we're using AI, refusing AI in this product in two very specific ways. Now that we are releasing today. Which is one, is to automatically collect and design and define the architecture. So we call this automated architecture discovery. So if you have a very large factory, you can imagine, you know, have lots of different modules, lots of different applications. And if you need to go and manually have to label everything. So this is a front end, this is a backend. That would take a lot of time. So we use machine learning. Learning from what architects have already done in the past and classifying their architecture. And we can map out your architecture completely, automatically, which is really powerful. Then we also use our AI engine to analyze your factory. And we can detect the best opportunities for refactoring. So refactoring is one of the top problems and the top smells and technical debt problems that large factories have, right? So we can completely identify and pinpoint what are these opportunities for refactoring and we guide you through it. We tell you, okay, all of these hundreds of functions and logic patterns that we see in your code, you could de-refactor this into a single function, and you can save a lots and lots of code. Because you know, the best code, the fastest code, the easiest to maintain is the code you don't write, you don't have. So we're trying to really eliminate crack from these factories, with these capabilities. >> (Stu) Well. >> It's fascinating. You're absolutely right. I'm, curious, you know, I think back to some of the earliest interactions I had with things, they'd give you guidance, you know, spellcheckers, grammar check. How much does the AI that you work on, does it learn what's specific for my organization and my preferences? Is there any community learning over time? Because there are industry best practices out there that are super valuable, but, you know, we saw in the SAS wave. When I can customize things myself, we learn over time. So how does that play into kind of today in the roadmap for AI that you're building? >> That's a great question. So our AI, let's say technology that we used, it actually uses two different, big kinds of AI. So we use machine learning definitely to learn from the community, what are the best practices and what are the most common patterns that people use. So we use that to guide developers, but also to validate and analyze their code. But then we also use automated reasoning. So this is more logic based, reasoning based AI. And we pair these two technologies to really create a system that is able to learn from data, but also be able to reason it at a higher order, about what are good practices and kind of reach conclusions from there and learn new things from there. Now we started by applying these technologies to more of the community data and kind of standard best practices, but our vision is to more and more start learning specifically, and allowing tech leads and architects even, in the future to tailor these engines of AI, perhaps to suggest these are the best practices for my factory. These patterns perhaps are good best practices in general. But in my factory, I do not want to use them because I have some specificities for compliance or something like that. And our vision is that, architects and tech leads can just provide just a few examples of what they like and what they don't like. And the engine just automatically learns and gets tailored to their own environment. >> (Stu) Oh, important that you're able to, you know, have the customers move things forward to the direction that makes sense on their end. I'm also curious, you talk about, you know, what partnerships OutSystems has out there, you know, being able to tie into things like what the public cloud is doing, lots of industry collaboration. So, you know, how does health systems fit into the kind of the broader AI ecosystem? >> Yeah. So one thing I did not mention, and to your point is, so we have kind of two complimentary visions and strategies for AI. So one of them is we really want to improve our own product, improve the automation in the product in the abstraction by using AI, together with great user experience and the best programming language for software automation. Right? So that's one, that's what we generally call AI assistant development and infusing AI across the software development life cycle. The other one is we also believe that, you know, true elite cloud software companies that create frictionless experiences, one of the things that they use to really be super competitive and create these frictionless experiences is that they can themselves use AI and machine learning. To automate processes, create really, really delightful experiences. So we're also investing and we've shown. And we're launching and announcing at NextStep. We've just shown this at the keynote. One tool that we call the machine learning builder, ML builder. So this essentially speaks to the fact that, you know, a lot of companies do not have access to data science talent. They really struggle to adopt machine learning. Like just one out of 10 companies are able to go and put AI in production. So we're essentially abstracting also that. We're also increasing the productivity for customers to implement AI and machine learning. We use partners behind the scenes and cloud providers for the core technology, with automated machine learning and all of that. But we abstract all of the experience. So developers can essentially just pick the data they have already inside the AI systems platform. And they want to just select. I want to train this machine learning model to predict this field, just click, click, click. And it runs dozens of experiments, selects the best algorithms transforms the data for you without you needing to have a lot of data science experience. And then you can just drag and drop into platform. Integrate in your application. And you're good to go. >> (Stu) Well. Sounds, you know, phenomenal. You mentioned data scientists. We talked about that the skill gap. Do you have any statistics, you know, is this helping people, you know, hire faster, lower the bar to entry for people to get on board, you know, increase productivity, what kind of hero numbers do your customers typically, you know, how do they measure success? >> Yeah. So we know that in, for machine learning adoption at companies, we know that, sorry, this is one of the top challenges that they have, right? So companies do not, it's not only that they do not have the expertise to implement machine learning in their products and their applications. They don't even have a good understanding of what are the use cases in order the technology opportunities for them to apply. Right? So this has been listed by lots of different surveys that this is the top problem. These are the two of the top problems that companies have to adopt AI's. Access to skill, data science skill, and understanding of the use case. And that's exactly what we're trying to kind of package up in a very easy to use product, where you can see the use cases you have available. You just select your data. You just click train, you do not need to know the nitty gritty details. And for us, a measure of success is that we've seen customers that are starting to experiment with ML builder. Is that in just a day or a few days, they can iterate over several machine learning models and put them in production. We have customers that have, you know, no machine learning models in production ever. And they just now have two. And they're starting to automate processes. They're starting to innovate with business. And that for us is we've seen as kind of the measure of success for businesses. Initially, what they want to do is they want to do POC's, and they want to experiment and they want to get to production, start getting to field for it and iterate. >> (Stu) From a product standpoint, is the AI just infused in, or there are additional licensing, you know, how do customers, you know, take advantage of it? What's the impact on that from the relationships without systems? >> Yeah. So for AI and machine learning that is fused into our product. And for automation, validation and guidance, there's, you know, no extra charges, just part of the product is what we believe is kind of a core building block in a core service for everything we do in our product. For machine learning services and components that customers can use to.... in their own applications. We allow you to integrate with cloud providers and the billing is done separately. And that's something that we're working towards and building great technical partnerships and exploring other avenues for deeper integration, so that developers and customers do not really have to worry about those things as well. >> (Stu) Yeah. Well, it's such a great way to really democratize the use of this technology platform that they're used to. They start doing it. What's general feedback from your customers? Do they just like, "Oh, it's there." "I started playing with it." "It's super easy, it makes it better." Are there any concerns or pushback? Have we gotten beyond that? What do you hear? Any good customer examples you can share as to general adoption? >> Yeah. So as I said, as we reduce the friction for adopting these technologies. We've seen one thing that's very interesting. So we have a few customers that are, for example, more in the logistics side of industry and vertical. And so they have a more conservative management, like they take time to adopt. They're more of a laggard in adopting these kinds of technologies. The business is more skeptical. Doesn't want to spend a lot of time playing around, right. And once they saw what they could do with a platform, they quickly did a proof of concept. They showed to the business and the business had lots of ideas. So they just started interacting a lot more with IT. Which is something we see with OutSystems platform, not just for AI machine learning, but generally in the digital transformation. Is when the IT can start really being very agile and iterating and innovating. And they start collaborating a lot with the business. And so what we see is, customers asking us for even more. So customers want more use cases to be supported like this. Customers also the ones that are more mature, that already have their centers of excellence, and they have their data scientists for example. They want to understand how they can also bring in perhaps, their use of very specialized tools. How can they integrate that into the platform so that, you know, for certain use cases, developers can very quickly train their own models, but so specialized data science teams can also bring in and developers can integrate their models easily and put them into production. Which is one of the big barriers. We see in a lot of companies, people working on year long projects, they develop the models, but they struggle to get them to production. And so we really want to focus on the whole end to end journey. Either you're building everything within the AI platform, or you're bringing it from a specialized pro tool. We want to make that whole journey frictionless and smooth. >> ( STU) Antonio, final question I have for you. Of course, this space we're seeing maturing, you know, rapid new technologies out there. Give us a little look forward. What should we be expecting to see from OutSystems or things even a little broader as you look at your partner ecosystem over kind of the next six, 12, 18 months? >> Yeah. So what... We're going to continue to see a trend, I think from the cloud service providers of democratization of the AI services. So this is during, just starting to advanced and accelerate. As these providers started packaging. It's like, what our system is also doing, starting to packaging some specific well-defined use cases. And then making the journey for training these models and deploying super simple. That's one thing that's continued to ramp up. And we're going to move from a AI services, more focused on cognitive pre-trained models, right. That, which is kind of the status quo. To custom AI models based on your data. That's kind of the trend we're going to start seeing in that, OutSystems also pushing forward. Generally from the AI and machine learning application and technology side of thing. I think one thing that we are leading on is that you know, machine learning and deep learning is definitely one of the big drivers for the innovation that we're seeing in AI. But you're start seeing more and more what is called hybrid AI. Which is taking machine learning and database artificial intelligence with more logic based automated reasoning techniques in pairing these two to really create systems that are able to operate at a really higher level. A higher cognitive level, which is what OutSystems is investing internally in terms of research and development. And with partnerships with institutions like Carnegie Mellon University, and such as that. >> (Stu) Wonderful. Antonio, who doesn't want, you know, a tech expert sitting next to them, helping get rid of some of the repetitive, boring things or challenges. Thank you so much for sharing the updates. >> (Antonio) Thank you Stu Congratulations on your progress And definitely look forward to hearing more in the future. >> (Antonio) Thank you Stu. Have a good day. >> (Stu) All right. >> Stay tuned for more from OutSystems NextStep. I'm Stu Miniman. And thank you for watching the "theCUBE."

>>from around the globe. It's the cue with digital coverage of out systems. Next Step 2020 Brought to you by out systems. I'm stupid, man. And welcome back to the cubes Coverage of out systems Next step course. One of the items that we've been talking a lot in the industry is about how artificial intelligence, machine learning or helping people is. We go beyond what really human scale can do and we need to be ableto do things more machine scale. Help us really dig into this topic. Happy to welcome to the program First time guest Antonio Alegria. He is the head of artificial intelligence at out systems. Tonio, thanks so much for joining us. >>Thank you. So I'm really happy to be here and and really talk a little bit about what? We're doing it out systems to help our customers and our leverage eai to get to those goals. >>Wonderful. So I I saw ahead of the event a short video that you did and talked about extreme agility with no limits. So, you know, before we drink, dig into the product itself. Maybe if you could just how should we be thinking about a I you know, there's broad spectrum. Is that machine learning that there's various components in there? Listen to the big analyst firms. You know, the journey. It's big steps and something that that is pretty broad. So when we're talking about A I, you know, what does that mean to you? What does that mean to your customers? >>Eso So AI out systems really speaks to division and the core strategy we have for our product, which is, you know, if you saw the keynote, no, we talk about no, really enabling every company, even those that you know, that existed for decades, perhaps have a lot of legacy to become. You know, leading elite cloud software development companies and really can develop digital solutions at scale really easily. But one thing we see and then this is a big statistic. One of the things that limits limits CEOs the most nowadays is really the lack of town lack of engineering, a softer engineering, you know, ability and people that that that could do that. And there's a statistic that was reported by The Wall Street Journal. I saw it recently, perhaps last year, that said that according to federal jobs dating the U. S. By the end of 2. 2020 there would be about a million unfilled I E. T s after development jobs available. Right? So there's this big problem All of these companies really need to scale, really need to invest in digital systems and so horribly fed out systems. We've already been abstracting and we've been focusing automating as much as possible the softer development tools and applications that use. We've already seen amazing stories of people coming from different backgrounds really starting to develop, really leading edge applications. And we want to take this to the next level. And we believe that artificial intelligence with machine learning but also with other AI technologies that were also taking advantage of can really help us get to a next stage of productivity. So from 10 x productivity to 100 x productivity and we believe AI plays a rolling three ways. We believe II by learning from all of this data that we not collect in terms of, you know, projects are being developed. We're essentially trying to embed a tech lead, so to speak, inside a product and attack Lee that can help developers by guiding them got in the most junior ones by automating some of the boring, repetitive tasks were by validating their work. Making sure that they're using the best practice is making sure that it helps them as they scale to re factor on their code to automatically designed architectures. Things like that >>Wonderful. Antonio Gonzalo stated it quite clearly in the interview that I had with him. It's really about enabling that next you know, 10 million developers. We know that there is that skill gap, as you said, and you know everybody right now how can I do more? How can I react faster? Eso that's where you know, the machine learning artificial intelligence should be able to help. So bring us inside. I know the platform itself has had, you know, guidance and and the whole movement. You know, what we used to call low code was about simplifying things and allowing people to, you know, build faster. So bring us inside the product. You know what? The enhancements? One of the new pieces. Some of the key key items, >>Yes, So 11 interesting thing. And I think one thing that I think out system is really proud of being able to achieve is if you look at how out system has been using a AI within the platform. We started with introducing AI assistance within the Our Software Development Environment Service studio. Right? And so this capability, we've been generating it a lot. We've been evolving it, and now it's really able to accelerate significantly and guide novices, but also help pros dealing through software development process and coding by essentially trying to infer understanding their context and trying to infer their intent and then automating the steps afterwards. And we do this by suggesting you the most likely let's say function or or code p sexual one you need. But then, at the next step, which we're introducing this year, even better, which is we're trying to auto fill most of them. Let's see the variables and all of that in the data flow that you need to collect. And so you get a very delightful frictionless experience as you are coating, so you're closer to the business value even more than before. Now this is the This was just the first step, what you're seeing now and what we're announcing, and we're showing up at this next step that we show that the keynote is that we're trying to fuse starting to fuse AI across the out systems products and across this after development life cycle. So he took this core technology that we used to guide developers and assistant automate their work. Um, and we use the same capability to help developers. Tech leads an architect's to analyze the code, learning from the bad patterns that exist, learning from and receiving runtime information about crashes and performance and inside the product recall architecture, dashboard were really able to give recommendations to these architects and tech leads. Where should they evolve and improve their code? And we're using AI refusing AI in this product into very specific ways. Now that we're releasing today, which is one is to automatically collect and design and defined the architecture. So we call this automated architecture discovery. So if you have a very large factory, you can imagine, you know have lots of different modules, lots of different applications, and if you need to go and manually have to label everything so this is ah, front, and this is the back end. That would take a lot of time. So we use machine learning, learning from what architects have already done in the past, classifying their architecture. And we can map out your architecture completely automatically, which is really powerful. Then we also use our AI engine to analyze your factory and weaken detect the best opportunities for re factoring. Sorry. Factoring is one of the top problems in the top smells and technical depth problems that large factories have. Right, So we can completely identify and pinpoint. What are these opportunities for re factory and we guide you through it, which held you okay, all of these hundreds of functions and logic patterns that we see in your code Could you re factor this into a single function and you can save a lots and lots of code because, as you know, the best code the fastest coast easiest to maintain is the Cody. Don't ride. You don't have. So we're trying to really eliminate Kurt from these factories with these kids ability. >>Well, it's fascinating. You're absolutely right. I'm curious. You know, I think back to some of the earliest interactions I had with things that give you guys spell checkers. Grammar check. How much does the AI that you work on. Does it learn what specific for my organization in my preferences? Is there any community learning over time? Because there are industry breast pack that best practices out there that are super valuable. But, you know, we saw in the SAS wave when I can customize things myself were learned over time. So how does that play into kind of today in the road map for a I that you're building >>that? That's a good question. So our AI let's say technology that we use it actually uses to two different big kinds of AI. So we use machine learning definitely to learn from the community. What are the best practices and what are the most common pattern that people use? So we use that to guide developers, but also to validate and analyze their code. But then we also use automated reasoning. So this is more logic based reasoning based AI and repair these two technologies to really create a system that is able to learn from data but also be able to reason at a higher order about what are good practices and kind of reach conclusions from there and learn new things from there now. We started by applying these technologies to more of the community data and kind of standard best practices. But our vision is to more and more start learning specifically and allowing tech leads an architect even in the future. To Taylor. These engines of AI, perhaps to suggest these are the best practices for my factory. These patterns perhaps, are good best practices in general. But in my factory, I do not want to use them because I have some specificities for compliance or something like that. And our vision is that architects and techniques can just provide just a few examples of what they like and what they don't like in the engine just automatically learns and gets tailor to their own environment. >>So important that you're able to, uh, you know, have the customers move things forward in the direction that makes sense on their end. I'm also curious. You talk about, um, you know what what partnerships out systems has out there, you know, being able to tie into things like what the public cloud is doing. Lots of industry collaboration. So how does health system fit into the kind of the broader ai ecosystem. >>Yes. So one thing I did not mention and to your point is eso were have kind of to, um Teoh Complementary visions and strategies for a I. So one of them is we really want to improve our own product, improve the automation in the product in the abstraction by using AI together with great user experience and the best programming language for software on automation. Right, So that's one. That's what we generally call AI assisted development. And if using AI across this software development life cycle, the other one is We also believe that you know, true elite cloud software companies that create frictionless experiences. One of the things that they used to really be super competitive and create this frictionless experiences is that they can themselves use AI and machine learning to to automate processes created really, really delightful experiences. So we're also investing and we've shown and we're launching, announcing that next step we just showed this at at the keynote one tool that we call the machine learning builder ml builder. So this essentially speaks to the fact that you know, a lot of companies do not have access to data science talent. They really struggle to adopt machine learning. Like just one out of 10 companies are able to go and put a I in production. So we're essentially abstracting also that were also increasing the productivity for you for customers to implement an AI and machine learning we use. We use partners behind the scenes and cloud providers for the core technology with automated machine learning and all of that. But we abstract all of the experience so developers can essentially just pick of the data they have already in the inside the all systems platform, and they want to just select. I want to trade this machine learning model to predict this field, just quickly click and it runs dozens of experiments, selects the best algorithms, transforms that the data for you without you needing to have a lot of data science experience. And then you can just drag and drop in the platform integrating your application. And you're good to go. >>Well, it sounds comes Ah, you know, phenomenal. You mentioned data scientists. We talked about that. The skill gap. Do you have any statistics? You know? Is this helping people you know? Higher, Faster. Lower the bar the entry for people to get on board, you know, increased productivity. What kind of hero numbers do your customers typically, you know, how do they measure success? >>Yes, So we know that in for machine learning adoption at cos we know that. Sorry, This is one of the top challenges that they have, right? So companies do not. It's not only that they do not have the expertise to implement machine learning at in their products in their applications. They don't even have a good understanding of what are the use cases in or out of the technology opportunities for them to apply. Right? So this has been listed by lots of different surveys that this is the top problem. These other 22 of the top problems that companies have to adopt a ice has access to skilled. They decided skill, understanding of the use case. And that's exactly what we're trying to kind of package up in a very easy to use product where you can see the use cases you have available, we just select your data, you just click train. You do not need to know that many greedy details and for us, a measure of success is that we've seen customers that are starting to experiment with ML Builder is that in just a day or a few days that can iterating over several machine learning models and put them in production. We have customers that have, you know, no machine learning models and production ever, and they just now have to, and they're starting to automate processes. They're starting to innovate with business. And that, for us, is we've seen it's kind of the measure of success for businesses initially, what they want to do is they want to do. POC is and they want to experiment and they want to get to production stopped. Getting to field for it and generate from >>a product standpoint, is the A. I just infused in or there's there additional licensing, how to customers, you know to take advantage of it. What's the impact on that from the relationship without systems? >>Yes. So for for for a I in machine learning that is fused into our product and for automation, validation and guidance, there's no extra charge is just part of the product. It's what we believe is kind of a core building block in a course service for everything we do in our product for machine learning services and components that customers can use to in their own applications. We allow you to integrate with cloud providers, and the building is is done separately on. That's something that that we're working towards and building great technical partnerships and exploring other avenues for deeper integration so that developers and customers do not really have to worry about those things. Well, >>it's it's It's such a great way to really democratize the use of this technology platform that they're used to. They start doing it. What's general feedback from your customers? Did they just like, Oh, it's there. I start playing with it. It's super easy. It makes it better there any concerns or push back. Have we gotten beyond that? What? What? What do you hear any any good customer examples you can share us toe general adoption? >>Yes. So, as I said, as we re reduce the friction for adopting these technologies, we've seen one thing that's very interesting. So we have a few customers that are present more in the logistics site of industry and vertical, and so they they have a more conservative management, like take time to adopt and more of a laggard in adopting these kinds of technologies, the businesses more skeptical. But I want to spend a lot of time playing around right and whence they saw. Once they saw what they could do with a platform, they quickly did a proof of concept. They show to the business and the business had lots of ideas. So they just started interacting a lot more with I t, which is something we see without systems platform not just for a I machine learning, but generally in the jib. Digital transformation is when the I teak and can start really being very agile in iterating and innovating, and they start collaborating a lot with the business. And so what we see is customers asking us for even more so customers want more use cases to be supported like this. Customers also the ones that are more mature than already, have their centers of excellence and they have their data scientists, for example. They want to understand how they can also bring in perhaps their use of very specialized tool talking in it. Integrate that into the platform so that you know, for certain use cases. Developer scan very quickly trained their own models. But so specialized data science teams can also bring in. And developers can integrate their models easily and put them into production, which is one of the big barriers we see in a lot of companies people working on yearlong projects. They develop the models that they struggle to get them to production. And so we really want to focus on the whole into in journey. Either you're building everything within the octopus platform or you're bringing it from a specialized pro tool. We want to make that whole journey frictionless in school. >>And Tony a final question I have for you. Of course, this space we're seeing maturing, you know, rapid Ah, new technologies out there gives a little look forward. What should we be expecting to see from out systems or things even a little broader? If you look at your your partner ecosystem over kind of the next 6, 12 18 months, >>Yes. So, um, what you're going to continues to see a trend, I think, from from the closer providers of democratization of the AI services. So this is during that just starting to advanced and accelerate as these providers started packaging. It's like what out systems also doing, starting to packaging Cem some specific, well defined use cases and then making the journey for training these models and deploying Super super simple. That's one thing that's continued to ramp up, and we're going to move from A I services more focused on cognitive, pre trained models, right, that which is kind of the status quo to custom ai models based on your data. That's kind of the train we're going to start seeing in that out systems also pushing forward generally from the AI and machine learning application and technology side of thing. I think one thing that we're leading leading on is that you know, machine learning and deep learning is definitely one of the big drivers for the innovation that we're seeing in a I. But you're start seeing more and more what is called hybrid I, which is taking machine learning and data based artificial intelligence with more logic based automated reasoning techniques, impairing these two to really create systems that are able to operate at a really higher level, higher cognitive level of which is what out systems investing internally in terms of research and development and with partnerships with institutions like Carnegie Mellon University and >>rely Antonio, who doesn't want, you know, a tech experts sitting next to them helping get rid of some of the repetitive, boring things or challenges. Thank you so much for sharing the update. Congratulations. Definitely Look forward to hearing war in the future. >>Thank you. Do have a good day >>Stay tuned for more from out systems. Next step is to minimum and thank you for watching.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hey welcome back here writer jeff rick here with the cube we're in our Palo Alto studios you know kind of continuing our leadership coverage reaching out to the community for people that we've got in our community to get their take on you know how they're dealing with the Kovach crisis how they're helping to contribute back to the community to to bring their resources to bear and you know just some general good tips and tricks of getting through these kind of challenging times and we're really excited to have one of my favorite guests he's being used to come on all the time we haven't had them on for three years which I can't believe it sabi Mehta the CEO of true SATA founder to say to obby I checked the record I can't believe it's been three years since we last that down great to see you Jeff there's well first of all it's always a pleasure and I think the only person to blame for that is you Jeff well I will make sure that it doesn't happen again so in just a check-in how's things going with the family the company thank you for asking you know family is great we have I've got two young kids who have become video conferencing experts and they don't teach me the tricks for it which I'm sure is happening a lot of families around the world and the team is great we vent remote at this point almost almost two months ago down and can't complain I think their intellectual property business like you are so it's been a little easier for us to go remote compared to a lot of other businesses in the world and in America but no complaints it'll be very fortunate we are glad that we have a business and a company that can withstand the the economic uncertainty and the family's great I hope the same for the queue family I haven't seen Dave and John and it's good to see you again and I hope all of you guys are helped happy and healthy great I think in we're good so thank you for asking so let's jump into it you know one of the things that I've always loved about you is you know really your sense of culture and this kind of constant reinforcing of culture in your social media posts and the company blog post at true SATA you know celebrating your interns and and you really have a good pulse for that and you know I just I think we may even talked about it before about you know kind of the CEOs and leadership and and social media those that do and that and those that don't and you know I think it's it's probably from any kind of a risk reward trade-off you know I could say something group it versus what am I getting at it but really it's super important and in these times with the distributed workforce that the the importance and value of communicating and culture and touching your people frequently across a lot of different mediums and topic areas is is more important than ever before share with us kind of your strategy why did you figure this out early how have you you know kind of adjusted you know your method of keeping your team up and communicating absolutely like I guess I owe you guys a little bit of gratitude for it which is we launched our company and you know I'm showing a member on the cube it was a social media launch you know if you say that say it like that I think there are two or three things that are very important Jeff and you hit on all of them one is the emphasis on information sharing it becomes more important than times like these and we as as a society value the ability to share a positive conversation of positive perspective and a positive outlook more but since day zero at the seder we've had this philosophy that there are no secrets it is important to be open and transparent both inside and outside the company and that our legacy is going to be defined by what we do for the community and not just what we do for our shareholders and by its very nature the fact that you know I grew up in a different continent now live and call America now a different continent my home I guess I was it's very important for me to stay connected to my roots it is a good memory or reminder that the world is very interconnected unfortunately the pandemic is the is the best or worst example of it in a really weird way but I think it's also a very important point Jeff that I believe we learned early and I hope coming out from this is something that we don't lose the point you made about kindness social media and social networking has a massively in my opinion massively positive binding force for the world at the same time there were certain business models it tried to capitalize on the negative aspects of it you know whether they are the the commercialized versions of slam books or not so nice business models that capitalize on the ability for people to complain I hope that people society and us humans coming out of it learn from people like yourself or you know the small voice that I have on social media or the messages we share and we are kinda in what we do online because the ability to have networks that are viral and can propagate or self propagate is a very positive unifying force and I hope out of this pandemic we all realize the positive nature's of it more than the negative nature's of it because unfortunately as you know that our business models built on the negative forces of social media and I really really hope they're coming out of this are positive voices drown out the negative voices that's great point and and it's a great I want to highlight a quote from one of your blog's again I think you're just a phenomenal communicator and in relationship to what's going on with kovat and and I quote we are fighting fear pain and anxiety as much as we are fighting the virus this is our humble attempt to we'll get into what you guys did to help the thousands of first responders clerks rockstars but I just really want to stick with that kindness theme you know I used to or I still joke right that the greatest smile in technology today is our G from signal FX the guys are gonna throw up a picture of him he's a great guy he looks like everybody's favorite I love that guy but therefore signal effects and actually it's funny signal FX also launched on the cube at big data a big data show I used to say the greatest smile intact is avi Mehta I mean how can I go wrong and and what I when I reached out to you I I do I consciously thought what what more important time do we have than to see people like you with a big smile with the great positive attitude focusing on on the positives and and I just think it's so important and it segues nicely into what we used to talk about it the strata shows and the big data shows all the time everyone wanted to talk about Hadoop and big data you always stress is never about the technology it's about the application of the technology and you focus your company on that very where that laser focus from day one now it's so great to see is we think you know the bad news about kovat a lot of bad news but one of the good news is is you know there's never been as much technology compute horsepower big data analytics smart people like yourself to bring a whole different set of tools to the battle than just building Liberty ships or building playing planes or tanks so you guys have a very aggressive thing that you're doing tell us a little bit about is the kovat active transmission the coat if you will tell us about what that is how did it come to be and what are you hoping to accomplish of course so first of all you're too kind you know thank you so much I think you also were the first people to give me a hard time about my new or Twitter picture I put on and he said what are you doing RV you know you have a good smile come on give me the smile die so thank you you're very kind Jeff I think as I as we as you know and I know I think you've a lot to be thankful for in life and there's no reason why we should not smile no matter what the circumstance we have so much to be thankful for and also I am remiss happy Earth Day you know I'm rocking my green for Earth Day as well as Ramadan Kareem today is the first day of Ramadan and you know I I wish everybody in the world Ramadan Kareem and on that friend right on that trend of how does do we as a community come together when faced with crisis so Court was a very simple thing you know it's I'm thank you for recognizing the hard work of the team that led it it was an idea I came up with it you know in the shower I'm like there are two kinds of people or to your you can we have we as humans have a choice when history is being made which I do believe I do believe history is being made right whether you look at it economically and a economic shock and that we have not felt as humanity since the depression so you look at it socially and again something we haven't seen sin the Spanish blue history is being made in in these times and I think we as humans have a choice we can either be witnesses to it or play our part in helping shape it and coat was our humble tiny attempt to when we look back when history was being made we chose to not just sit on the sidelines but be a part of trying to be part of the solution so all riddled with code was take a small idea I had team gets the entire credit read they ran with it and the idea was there was a lot of data being open sourced around co-ed a lot of work being done around reporting what is happening but nothing was being done around reporting or thinking through using the data to predict what could happen with it and that was code with code we try to make the first code wonder oh that came out almost two weeks ago now when you first contacted us was predicting the spread and the idea around breaking the spread wasn't just saying here is the number of cases a number of deaths and know what to be very off we wanted to provide like you know how firefighters do can we predict where it may go to next at a county by county level so we could create a little bit of a firewall to help it from stop you know have the spread of it to be slower in no ways are we claiming that if you did port you can stop it but if he could create firewalls around it and distribute tests not just in areas and cities and counties where it is you know spiking but look at the areas and counties where it's about to go to so we use a inner inner in-house Network algorithm we call that Orion and we were able to start predicting where the virus is gonna go to we also then quickly realize that this could be an interesting where an extra you know arrow and the quiver in our fight we should also think about where are there green shoots around where can recovery be be helped so before you know the the president email announced this it was surrender serendipitous before the the president came and said I want to start finding the green shoes to open the country we then did quote $2 which we announced a week ago with the green shoots around a true sailor recovery index and the recovery index is looking at its car like a meta algorithm we're looking at the rates of change of the rates of change so if you're seeing the change of the rates of change you know the meta part we're declining we're saying there are early shoots that we if as we plan to reopen our economy in our country these are the counties to look at first that was the second attempt of code and the third attempt we have done is we calling it the odd are we there yet index it got announced yesterday and now - you're the first public announcement of it and the are we there yet index is using the government's definition of the phase 1 phase 2 phase 3 and we are making a prediction on where which are the counties that are ready to be open up and there's good news everywhere in the country but we we are predicting there are 73 different counties that ask for the government's definition of ready to open are ready to open that's all you know we were able to launch the app in five days it is free for all first responders all hospital chains all not-for-profit organizations trying to help the country through this pandemic and poor profit operations who want to use the data to get tests out to get antibodies out and to get you know the clinical trials out so we have made a commitment that we will not charge for code through - for any of those organizations to have the country open are very very small attempt to add another dimension to the fight you know it's data its analytics I'm not a first responder this makes me sleep well at night that I'm at least we're trying to help you know right well just for the true heroes right the true heroes this is our our humble attempt to help them and recognize that their effort should not go to its hobby that that's great because you know there is data and there is analytics and there is you know algorithms and the things that we've developed to help people you know pick they're better next purchase at Amazon or where they gonna watch next on Netflix and it's such a great application no it's funny I just finished a book called ghost Bob and is a story of the cholera epidemic in London in like 1850 something or other about four but what's really interesting at that point in time is they didn't know about waterborne diseases they thought everything kind of went through the air and and it was really a couple of individuals in using data in a new and more importantly mapping different types of datasets on top of it and now this is it's as this map that were they basically figured out where the the pump was that was polluting everybody but it was a great story and you know kind of changing the narrative by using data in a new novel and creative way to get to an answer that they couldn't and you know they're there's so much data out there but then they're so short a date I'm just curious from a data science point of view you know um you know there there aren't enough tests for you know antibodies who's got it there aren't enough tests for just are you sick and then you know we're slowly getting the data on the desk which is changing all the time you know recently announced that the first Bay Area deaths were actually a month were they before they thought they were so as you look at what you're trying to accomplish what are some of the great datasets out there and how are you working around some of the the lack of data in things like you know test results are you kind of organizing pulling that together what would you like to see more of that's why I like talking to you so I missed you you are these good questions of me excellent point I think there are three things I would like to highlight number one it doesn't take your point that you made with the with the plethora of technical advances and this S curve shift that these first spoke at the cube almost eleven years ago to the date now or ten years ago just the idea of you know population level or modeling that cluster computing is finally democratized so everybody can run complicated tests and a unique segment or one and this is the beauty of what we should be doing in the pandemic I'm coming I'm coming I'm quite surprised actually and given the fact we've had this S curve shift where the world calls a combination of cloud computing so on-demand IO and technical resources for processing data and then the on-demand ability to store and run algorithms at massive scale we haven't really combined our forces to predict more you know that the point you made about the the the waterborne pandemic in the eighteen eighteen hundreds we have an ability as humanity right now to actually see history play out rather than write a book about it you know it has a past tense and it's important to do are as follows number one luckily for you and I the cost of computing an algorithm to predict is manageable so I am surprised why the large cloud players haven't come out and said you know what anybody who wants to distribute anything around predictions lay to the pandemic should get cloud resources for free I we are running quote on all three cloud platforms and I'm paying for all of it right that doesn't really make sense but I'm surprised that they haven't really you know joined the debate or contribute to it and said in a way to say let's make compute free for anybody who would like to add a new dimension to our fight against the pandemic number one but the good news is it's available number two there is luckily for us an open data movement you know that was started on the Obama administration and hasn't stopped because you can't stop open movements allows people companies like ours to go leverage know whether it's John Hancock Carnegie Mellon or the new data coming out of you know California universities a lot of those people are opening up the data not every single piece is at the level we would like to see you know it's not zip plus 4 is mostly county level it's available the third innovation is what we have done with code but not it's not an innovation for the world right which is the give get model so we have said we will curate everything is available lie and boo cost anybody is used but they're for purposes and computations you want to enrich it every organization who gives code data will get more out of it so we have enabled a data exchange keep our far-off purple form and the open up the rail exchange that my clients use but you know we've opened up our data exchange part of our software platform and we have open source for this particular case a give get model but the more you give to it the more you get out of there and our first installations this was the first week that we have users of the platform you know the state of Nevada is using it there are no our state in North Carolina is using it already and we're trying to see the first asks for the gift get model to be used but that's the three ways you're trying to address the that's great and and and and so important you know in this again when this whole thing started I couldn't help but think of the Ford plant making airplanes and and Keiser making Liberty ships in in World War two but you know now this is a different battle but we have different tools and to your point luckily we have a lot of the things in place right and we have mobile phones and you know we can do zoom and well you know we can we can talk as we're talking now so I want to shift gears a little bit and just talk about digital transformation right we've been talking about this for ad nauseam and then and then suddenly right there's this light switch moment for people got to go home and work and people got to communicate via via online tools and you know kind of this talk and this slow movement of getting people to work from home kind of a little bit and digital transformation a little bit and data-driven decision making a little bit but now it's a light switch moment and you guys are involved in some really critical industries like healthcare like financial services when you kind of look at this not from a you know kind of business opportunity peer but really more of an opportunity for people to get over the hump and stop you can't push back anymore you have to jump in what are you kind of seeing in the marketplace Howard you know some of your customers dealing with this good bad and ugly there are two towers to start my response to you with using two of my favorite sayings that you know come to mind as we started the pandemic one is you know someone very smart said and I don't know who's been attributed to but a crisis is a terrible thing to waste so I do believe this move to restoring the world back to a natural state where there's not much fossil fuels being burnt and humans are not careful about their footprint but even if it's forced is letting us enjoy the earth in its glory which is interesting and I hope you don't waste an opportunity number one number two Warren Buffett came out and said that it's only when the tide goes out you realize who's swimming naked and this is a culmination of both those phenomenal phrases you know which is one this is the moment I do believe this is something that is deep both in the ability for us to realize the virtuosity of humanity as a society as social species as well as a reality check on what a business model looks like visa vie a presentation that you can put some fancy words on even what has been an 11-year boom cycle and blitzscale your way to disaster you know I have said publicly that this the peak of the cycle was when mr. Hoffman mr. Reid Hoffman wrote the book bit scaling so we should give him a lot of credit for calling the peak in the cycle so what we are seeing is a kind of coming together of those two of those two big trends crises is going to force industry as you've heard me say many for many years now do not just modernize what we have seen happen chef in the last few years or decades is modernization not transformation and they are different is the big difference as you know transformation is taking a business model pulling it apart understanding the economics that drive it and then not even reassembling it recreating how you can either recapture that value or recreate that value completely differently or by the way blow up the value create even more value that hasn't happened yet digital transformation you know data and analytics AI cloud have been modernizing trends for the last ten years not transformative trends in fact I've also gone and said publicly that today the very definition of technology transformation is run a sequel engine in the cloud and you get a big check off as a technology organization saying I'm good I've transformed how I look at data analytics I'm doing what I was doing on Prem in the cloud there's still sequel in the cloud you know there's a big a very successful company it has made a businessman out of it you don't need to talk about the company today but I think this becomes that moment where those business models truly truly get a chance to transform number one number two I think there's going to be less on the industry side on the new company side I think the the error of anointing winners by saying grow at all cost economics don't matter is fundamentally over I believe that the peak of that was the book let's called blitzscaling you know the markets always follow the peaks you know little later but you and I in our lifetimes will see the return to fundamentals fundamentals as you know never go out of fashion Jeff whether it's good conversations whether it's human values or its economic models if you do not have a par to being a profitable contributing member of society whether that is running a good balance sheet individually and not driven by debt or running a good balance sheet as a company you know we call it financial jurisprudence financial jurisprudence never goes out of fashion and the fact that even men we became the mythical animal which is not the point that we became a unicorn we were a profitable company three years ago and two years ago and four years ago and today and will end this year as a profitable company I think it's a very very nice moment for the world to realize that within the realm of digital transformation even the new companies that can leverage and push that trend forward can build profitable business models from it and if you don't it doesn't matter if you have a billion users as my economic professor told me selling a watermelon that you buy for a dollar or fifty cents even if you sell that a billion times you cannot make it up in volume I think those are two things that will fundamentally change the trend from modernization the transformation it is coming and this will be the moment when we look back and when you write a book about it that people say you know what now Jeff called it and now and the cry and the pandemic is what drove the economic jurisprudence as much as the social jurisprudence obvious on so many things here we can we're gonna be we're gonna go Joe Rogan we're gonna be here for four hours so hopefully hopefully you're in a comfortable chair but uh-huh but I don't I don't sit anymore I love standing on a DD the stand-up desk but I do the start of my version of your watermelon story was you know I dad a couple of you know kind of high-growth spend a lot of money raised a lot of money startups back in the day and I just know finally we were working so hard I'm Michael why don't we just go up to the street and sell dollars for 90 cents with a card table and a comfy chair maybe some iced tea and we'll drive revenue like there's nobody's business and lose less money than we're losing now not have to work so hard I mean it's so interesting I think you said everyone's kind of Punt you know kind of this pump the brakes moment as well growth at the ethic at the cost of everything else right there used to be a great concept called triple-line accounting right which is not just shareholder value to this to the sacrifice of everything else but also your customers and your employees and-and-and your community and being a good steward and a good participant in what's going on and I think that a lot of that got lost another you know to your point about pumping the brakes and the in the environment I mean we've been kind of entertaining on the oil side watching an unprecedented supply shock followed literally within days by an unprecedented demand shock but but the fact now that when everyone's not driving to work at 9:00 in the morning we actually have a lot more infrastructure than we thought and and you know kind of goes back to the old mob capacity planning issue but why are all these technology workers driving to work every morning at nine o'clock it means one thing if you're a service provider or you got to go work at a restaurant or you're you're carrying a truck full of tools but for people that just go sit on a laptop all day makes absolutely no sense and and I'd love your point that people are now you know seeing things a little bit slowed down you know that you can hear birds chirp you're not just stuck in traffic and into your point on the digital transformation right I mean there's been revolution and evolution and revolution people get killed and you know the fact that digital is not the same as physical but it's different had Ben Nelson on talking about the changes in education he had a great quote I've been using it for weeks now right that a car is not a is not a mechanical horse right it's really an opportunity to rethink the you know rethink the objective and design a new solution so it is a really historical moment I think it is it's real interesting that we're all going through it together as well right it's not like there quake in 89 or I was in Mount st. Helens and that blew up in in 1980 where you had kind of a population that was involved in the event now it's a global thing where were you in March 20 20 and we've all gone through this indeed together so hopefully it is a little bit of a more of a unifying factor in kind of the final thought since we're referencing great books and authors and quotes right as you've all know Harare and sapiens talked about what is culture right cultures is basically it's it's a narrative that we all have bought into it I find it so ironic that in the year 2020 that we always joke is 20/20 hindsight we quickly found out that everything we thought was suddenly wasn't and the fact that the global narrative changed literally within days you know really a lot of spearhead is right here in Santa Clara County with with dr. Sarah Cody shutting down groups of more than 150 people which is about four days before they went to the full shutdown it is a really interesting time but as you said you know if you're fortunate enough as we are to you know have a few bucks in the bank and have a business that can be digital which you can if you're in the sports business or the travel business the hotel business and restaurant business a lot of a lot of a lot of not not good stuff happening there but for those of us that can it is an opportunity to do this nice you know kind of a reset and use the powers that we've developed for recommendation engines for really a much more power but good for good and you're doing a lot more stuff too right with banking and in in healthcare telemedicine is one of my favorite things right we've been talking about telemedicine and electronic medicine for now well guess what now you have to cuz the hospitals are over are overflowing Jeff to your point three stories and you know then at some point I know you have you I will let you go you can let me go I can talk to you for four hours I can talk to you for but days my friend you know the three stories that there have been very relevant to me through this crisis I know one is first I think I guess in a way all are personal but the first one you know that I always like to remind people on there were business models built around allowing people to complain online and then using that as almost like a a stick to find a way to commercialize it and I look at that all of our friends I'm sure you have friends have lots of friend the restaurant is big and how much they are struggling right they are honest working the hardest thing to do in life as I've been told and I've witnessed through my friends is to run a restaurant the hours the effort you put into it making sure that what you produce this is not just edible but it's good quality is enjoyed by people is sanitary is the hard thing to do and there was yet there were all of these people you know who would not find in their heart and their minds for two seconds to go post a review if something wasn't right and be brutal in those reviews and if they were the same people were to look back now and think about how they assort the same souls then anything to be supportive for our restaurant workers you know it's easy to go and slam them online but this is our chance to let a part of the industry that we all depend on food right critical to humanity's success what have we done to support them as easy as it was for us to complain about them what have we done to support them and I truly hope and I believe they're coming out of it those business models don't work anymore and before we are ready to go on and online on our phones and complain about well it took time for the bread to come to my table we think twice how hard are they working right number one that's my first story I really hope you do tell me about that my second story is to your have you chained to baby with Mark my kids I'm sure as your kids get up every morning get dressed and launch you know their online version of a classroom do you think when they enter the workforce or when they go to college you and me are going to try and convince them to get in a oil burning combustion engine but by the way can't have current crash and breakdown and impact your health impact the environment and show up to work and they'll say what do you talk about are you talking about I can be effective I can learn virtually why can't I contribute virtually so I think there'll be a generation of the next class of you know contribute to society who are now raised to live in an environment where the choice of making sure we preserve the planet and yet contribute towards the growth of it is no longer a binary choice both can be done so I completely agree with you we have fundamentally changed how our kids when they grew up will go to work and contribute right my third story is the thing you said about how many industries are suffering we have clients you know in the we have health care customers we have banking customers you know we have whoever paying the bills like we are are doing everything they can to do right by society and then we have customers in the industry of travel hospitality and one of my most humbling moments Jeff there's one of the no sea level executives sent us an email early in this in this crisis and said this is a moment where a strong David can help AV Goliath and just reading that email had me very emotional because they're not very many moments that we get as corporations as businesses where we can be there for our customers when they ask us to be their father and if we as companies and help our customers our clients who area today are flying people are feeding people are taking care of their health and they're well if V in this moment and be there for them we we don't forget those moments you know those as humans have long-term memories right that was one of the kindest gentlest reminders to me that what was more important to me my co-founder Richard you know my leadership team every single person at Reseda that have tried very hard to build automations because as an automation company to automate complex human process so we can make humans do higher order activities in the moment when our customers asked us to contribute and be there for them I said yes they said yes you said yes and I hope I hope people don't forget that that unicorns aren't important there are mythical animals there's nothing all about profits there's nothing mythical about fortress balance sheet and there's nothing mythical about a strong business model that is built for sustainable growth not good at all cost and those are my three stories that you know bring me a lot of lot of calm in this tremendous moment of strife and and in the piece that wraps up all those is ultimately it's about relationships right people don't do business I mean companies don't do business with companies people do business with people and it's those relationships and and in strong relationships through the bad times which really set us up for when things start to come back I me as always it's I'm not gonna let it be three years to the next time I hear me pounding on your door great to catch up you know love to love to watch really your your culture building and your community engagement good luck I mean great success on the company but really that's one thing I think you really do a phenomenal job of just keeping this positive drumbeat you always have you always will and really appreciate you taking some time on a Friday to sit down with us well first of all thank you I wish I could tell you I just up to you but we celebrate formal Fridays that to Seder and that's what this is all so I want to end on a good on a positive bit of news I was gonna give you a demo of it but if you want to go to our website and look at what everything we're doing we have a survival kit around a data survival kit around kovat how am I using buzzwords you know a is let's not use that buzzword right now but in your in your lovely state but on my favorite places on the planet when we ran the algorithm on who is ready as per the government definition of opening up we have five counties that are ready to be open you know between Santa Clara to LA Sacramento Kern and San Francisco the metrics today the data today with our algorithm there are meta algorithm is saying that those five counties those five regions look like I've done a lot of positive activities if the country was to open under all the right circumstances those five look you know the first as we were men at on cream happy Earth Day a pleasure to see you so good to know your family is doing well and I hope we see we talk to each other soon thanks AVI great conversation with avi Mehta terrific guy thanks for watching everybody stay safe have a good weekend Jeff Rick checking out from the cube [Music]

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello, and welcome to theCUBE Studios here in Palo Alto, California. We have a special Around theCUBE segment, Unpacking AI. This is a Get Smart Series. We have three great guests. Rajen Sheth, VP of AI and Product Management at Google. He knows well the AI development for Google Cloud. Dr. Kate Darling, research specialist at MIT media lab. And Professor Barry O'Sullivan, Director SFI Centre for Training AI, University of College Cork in Ireland. Thanks for coming on, everyone. Let's get right to it. Ethics in AI as AI becomes mainstream, moves out to the labs and computer science world to mainstream impact. The conversations are about ethics. And this is a huge conversation, but first thing people want to know is, what is AI? What is the definition of AI? How should people look at AI? What is the definition? We'll start there, Rajen. >> So I think the way I would define AI is any way that you can make a computer intelligent, to be able to do tasks that typically people used to do. And what's interesting is that AI is something, of course, that's been around for a very long time in many different forms. Everything from expert systems in the '90s, all the way through to neural networks now. And things like machine learning, for example. People often get confused between AI and machine learning. I would think of it almost the way you would think of physics and calculus. Machine learning is the current best way to use AI in the industry. >> Kate, your definition of AI, do you have one? >> Well, I find it interesting that there's no really good universal definition. And also, I would agree with Rajen that right now, we're using kind of a narrow definition when we talk about AI, but the proper definition is probably much more broad than that. So probably something like a computer system that can make decisions independent of human input. >> Professor Barry, your take on the definition of AI, is there one? What's a good definition? >> Well, you know, so I think AI has been around for 70 years, and we still haven't agreed the definition for it, as Kate said. I think that's one of those very interesting things. I suppose it's really about making machines act and behave rationally in the world, ideally autonomously, so without human intervention. But I suppose these days, AI is really focused on achieving human level performance in very narrowly defined tasks, you know, so game playing, recommender systems, planning. So we do those in isolation. We don't tend to put them together to create the fabled artificial general intelligence. I think that's something that we don't tend to focus on at all, actually if that made sense. >> Okay the question is that AI is kind of elusive, it's changing, it's evolving. It's been around for awhile, as you guys pointed out, but now that it's on everyone's mind, we see it in the news every day from Facebook being a technology program with billions of people. AI was supposed to solve the problem there. We see new workloads being developed with cloud computing where AI is a critical software component of all this. But that's a geeky world. But the real world, as an ethical conversation, is not a lot of computer scientists have taken ethics classes. So who decides what's ethical with AI? Professor Barry, let's start with you. Where do we start with ethics? >> Yeah, sure, so one of the things I do is I'm the Vice-Chair of the European Commission's High-Level Expert Group on Artificial Intelligence, and this year we published the Ethics Guidelines for Trustworthy AI in Europe, which is all about, you know, setting an ethical standard for what AI is. You're right, computer scientists don't take ethical standards, but I suppose what we are faced with here is a technology that's so pervasive in our lives that we really do need to think carefully about the impact of that technology on, you know, human agency, and human well-being, on societal well-being. So I think it's right and proper that we're talking about ethics at this moment in time. But, of course, we do need to realize that ethics is not a panacea, right? So it's certainly something we need to talk about, but it's not going to solve, it's not going to rid us of all of the detrimental applications or usages of AI that we might see today. >> Kate, your take on ethics. Start all over, throw out everything, build on it, what do we do? >> Well, what we do is we get more interdisciplinary, right? I mean, because you asked, "Who decides?". Until now it has been the people building the technology who have had to make some calls on ethics. And it's not, you know, it's not necessarily the way of thinking that they are trained in, and so it makes a lot of sense to have projects like the one that Barry is involved in, where you bring together people from different areas of expert... >> I think we lost Kate there. Rajen, why don't you jump in, talk about-- >> (muffled speaking) you decide issues of responsibility for harm. We have to look at algorithmic bias. We have to look at supplementing versus replacing human labor, we have to look at privacy and data security. We have look at the things that I'm interested in like the ways that people anthropomorphize the technology and use it in a way that's perhaps different than intended. So, depending on what issue we're looking at, we need to draw from a variety of disciplines. And fortunately we're seeing more support for this within companies and within universities as well. >> Rajen, your take on this. >> So, I think one thing that's interesting is to step back and understand why this moment is so compelling and why it's so important for us to understand this right now. And the reason for that is that this is the moment where AI is starting to have an impact on the everyday person. Anytime I present, I put up a slide of the Mosaic browser from 1994 and my point is that, that's where AI is today. It's at the very beginning stages of how we can impact people, even though it's been around for 70 years. And what's interesting about ethics, is we have an opportunity to do that right from the beginning right now. I think that there's a lot that you can bring in from the way that we think about ethics overall. For example, in our company, can you all hear me? >> Yep. >> Mm-hmm. >> Okay, we've hired an ethicist within our company, from a university, to actually bring in the general principles of ethics and bring that into AI. But I also do think that things are different so for example, bias is an ethical problem. However, bias can be encoded and actually given more legitimacy when it could be encoded in an algorithm. So, those are things that we really need to watch out for where I think it is a little bit different and a little bit more interesting. >> This is a great point-- >> Let me just-- >> Oh, go ahead. >> Yeah, just one interesting thing to bear in mind, and I think Kate said this, and I just want to echo it, is that AI is becoming extremely multidisciplinary. And I think it's no longer a technical issue. Obviously there are massive technical challenges, but it's now become as much an opportunity for people in the social sciences, the humanities, ethics people. Legal people, I think need to understand AI. And in fact, I gave a talk recently at a legal symposium, and the idea of this on a parallel track of people who have legal expertise and AI expertise, I think that's a really fantastic opportunity that we need to bear in mind. So, unfortunately us nerds, we don't own AI anymore. You know, it's something we need to interact with the real world on a significant basis. >> You know, I want to ask a question, because you know, the algorithms, everyone talks about the algorithms and the bias and all that stuff. It's totally relevant, great points on interdisciplinary, but there's a human component here. As AI starts to infiltrate the culture and hit everyday life, the reaction to AI sometimes can be, "Whoa, my job's going to get automated away." So, I got to ask you guys, as we deal with AI, is that a reflection on how we deal with it to our own humanity? Because how we deal with AI from an ethics standpoint ultimately is a reflection on our own humanity. Your thoughts on this. Rajen, we'll start with you. >> I mean it is, oh, sorry, Rajen? >> So, I think it is. And I think that there are three big issues that I see that I think are reflective of ethics in general, but then also really are particular to AI. So, there's bias. And bias is an overall ethical issue that I think this is particular here. There's what you mentioned, future of work, you know, what does the workforce look like 10 years from now. And that changes quite a bit over time. If you look at the workforce now versus 30 years ago, it's quite a bit different. And AI will change that radically over the next 10 years. The other thing is what is good use of AI, and what's bad use of AI? And I think one thing we've discovered is that there's probably 10% of things that are clearly bad, and 10% of things that are clearly good, and 80% of things that are in that gray area in between where it's up to kind of your personal view. And that's the really really tough part about all this. >> Kate, you were going to weigh in. >> Well, I think that, I'm actually going to push back a little, not on Rajen, but on the question. Because I think that one of the fallacies that we are constantly engaging in is we are comparing artificial intelligence to human intelligence, and robots to people, and we're failing to acknowledge sufficiently that AI has a very different skillset than a person. So, I think it makes more sense to look at different analogies. For example, how have we used and integrated animals in the past to help us with work? And that lets us see that the answer to questions like, "Will AI disrupt the labor market?" "Will it change infrastructures and efficiencies?" The answer to that is yes. But will it be a one-to-one replacement of people? No. That said, I do think that AI is a really interesting mirror that we're holding up to ourselves to answer certain questions like, "What is our definition of fairness?" for example. We want algorithms to be fair. We want to program ethics into machines. And what it's really showing us is that we don't have good definitions of what these things are even though we thought we did. >> All right, Professor Barry, your thoughts? >> Yeah, I think there's many points one could make here. I suppose the first thing is that we should be seeing AI, not as a replacement technology, but as an assistive technology. It's here to help us in all sorts of ways to make us more productive, and to make us more accurate in how we carry out certain tasks. I think, absolutely the labor force will be transformed in the future, but there isn't going to be massive job loss. You know, the technology has always changed how we work and play and interact with each other. You know, look at the smart phone. The smart phone is 12 years old. We never imagined in 2007 that our world would be the way it is today. So technology transforms very subtly over long periods of time, and that's how it should be. I think we shouldn't fear AI. I think the thing we should fear most, in fact, is not Artificial Intelligence, but is actual stupidity. So I think we need to, I would encourage people not to think, it's very easy to talk negatively and think negatively about AI because it is such a impactful and promising technology, but I think we need to keep it real a little bit, right? So there's a lot of hype around AI that we need to sort of see through and understand what's real and what's not. And that's really some of the challenges we have to face. And also, one of the big challenges we have, is how do we educate the ordinary person on the street to understand what AI is, what it's capable of, when it can be trusted, and when it cannot be trusted. And ethics gets of some of the way there, but it doesn't have to get us all of the way there. We need good old-fashioned good engineering to make people trust in the system. >> That was a great point. Ethics is kind of a reflection of that mirror, I love that. Kate, I want to get to that animal comment about domesticating technology, but I want to stay in this culture question for a minute. If you look at some of the major tech companies like Microsoft and others, the employees are revolting around their use of AI in certain use cases. It's a knee-jerk reaction around, "Oh my God, "We're using AI, we're harming the world." So, we live in a culture now where it's becoming more mission driven. There's a cultural impact, and to your point about not fearing AI, are people having a certain knee-jerk reaction to AI because you're seeing cultures inside tech companies and society taking an opinion on AI. "Oh my God, it's definitely bad, our company's doing it. "We should not service those contracts. "Or, maybe I shouldn't buy that product "because it's listening to me." So, there's a general fear. Does this impact the ethical conversation? How do you guys see this? Because this is an interplay that we see that's a personal, it's a human reaction. >> Yeah, so if I may start, I suppose, absolutely there are, you know, the ethics debates is a critical one, and people are certainly fearful. There is this polarization in debate about good AI and bad AI, but you know, AI is good technology. It's one of these dual-use technologies. It can be applied to bad situation in ways that we would prefer it wasn't. And it can also, it's a force for tremendous good. So, we need to think about the regulation of AI, what we want it to do from a legal point of view, who is responsible, where does liability lie? We also think about what our ethical framework is, and of course, there is no international agreement on what is, there is no universal code of ethics, you know? So this is something that's very very heavily contextualized. But I think we certainly, I think we generally agree that we want to promote human well-being. We want to compute, we want to have a prosperous society. We want to protect the well-being of society. We don't want technology to impact society in any negative way. It's actually very funny. If you look back about 25-30 years ago, there was a technology where people were concerned that privacy was going to be a thing of the past. That computer systems were going to be tremendously biased because data was going to be incomplete and not representative. And there was a huge concern that good old-fashioned databases were going to be the technology that will destroy the fabric of society. That didn't happen. And I don't think we're going to have AI do that either. >> Kate? >> Yeah, we've seen a lot of technology panic, that may or may not be warranted, in the past. I think that AI and robotics suffers from a specific problem that people are influenced by science fiction and pop culture when they're thinking about the technology. And I feel like that can cause people to be worried about some things that maybe perhaps aren't the thing we should be worrying about currently. Like robots and jobs, or artificial super-intelligence taking over and killing us all, aren't maybe the main concerns we should have right now. But, algorithmic bias, for example, is a real thing, right? We see systems using data sets that disadvantage women, or people of color, and yet the use of AI is seen as neutral even though it's impinging existing biases. Or privacy and data security, right? You have technologies that are collecting massive amounts of data because the way learning works is you use lots of data. And so there's a lot of incentive to collect data. As a consumer, there's not a lot of incentive for me to want to curb that, because I want the device to listen to me and to be able to perform better. And so the question is, who is thinking about consumer protection in this space if all the incentives are toward collecting and using as much data as possible. And so I do think there is a certain amount of concern that is warranted, and where there are problems, I endorse people revolting, right? But I do think that we are sometimes a little bit skewed in our, you know, understanding where we currently are at with the technology, and what the actual problems are right now. >> Rajen, I want your thoughts on this. Education is key. As you guys were talking about, there's some things to pay attention to. How do you educate people about how to shape AI for good, and at the same time calm the fears of people at the same time, from revolting around misinformation or bad data around what could be? >> Well I think that the key thing here is to organize kind of how you evaluate this. And back to that one thing I was saying a little bit earlier, it's very tough to judge kind of what is good and what is bad. It's really up to personal perception. But then the more that you organize how to evaluate this, and then figure out ways to govern this, the easier it gets to evaluate what's in or out . So one thing that we did, was that we created a set of AI principles, and we kind of codified what we think AI should do, and then we codified areas that we would not go into as a company. The thing is, it's very high level. It's kind of like the constitution, and when you have something like the constitution, you have to get down to actual laws of what you would and wouldn't do. It's very hard to bucket and say, these are good use cases, these are bad use cases. But what we now have is a process around how do we actually take things that are coming in and figure out how do we evaluate them? A last thing that I'll mention, is that I think it's very important to have many many different viewpoints on it. Have viewpoints of people that are taking it from a business perspective, have people that are taking it from kind of a research and an ethics perspective, and all evaluate that together. And that's really what we've tried to create to be able to evaluate things as they come up. >> Well, I love that constitution angle. We'll have that as our last final question in a minute, that do we do a reset or not, but I want to get to that point that Kate mentioned. Kate, you're doing research around robotics. And I think robotics is, you've seen robotics surge in popularity from high schools have varsity teams now. You're seeing robotics with software advances and technology advances really become kind of a playful illustration of computer technology and software where AI is playing a role, and you're doing a lot of work there. But as intelligence comes into, say robotics, or software, or AI, there's a human reaction to all of this. So there's a psychology interaction to either AI and robotics. Can you guys share your thoughts on the humanization interaction between technology, as people stare at their phones today, that could be relationships in the future. And I think robotics might be a signal. You mentioned domesticating animals as an example back in the early days of when we were (laughing) as a society, that happened. Now we all have pets. Are we going to have robots as pets? Are we going to have AI pets? >> Yes, we are. (laughing) >> Is this kind of the human relationship? Okay, go ahead, share your thoughts. >> So, okay, the thing that I love about robots, and you know, in some applications to AI as well, is that people will treat these technologies like they're alive. Even though they know that they're just machine. And part of that is, again, the influence of science fiction and pop culture, that kind of primes us to do this. Part of it is the novelty of the technology moving into shared spaces, but then there's this actual biological element to it, where we have this inherent tendency to anthropomorphize, project human-like traits, behaviors, qualities, onto non-humans. And robots lend themselves really well to that because our brains are constantly scanning our environments and trying to separate things into objects and agents. And robots move like agents. We are evolutionarily hardwired to project intent onto the autonomous movement in our physical space. And this is why I love robots in particular as an AI use case, because people end up treating robots totally differently. Like people will name their Roomba vacuum cleaner and feel bad for it when it gets stuck, which they would never do with their normal vacuum cleaner, right? So, this anthropomorphization, I think, makes a huge difference when you're trying to integrate the technology, because it can have negative effects. It can lead to inefficiencies or even dangerous situations. For example, if you're using robots in the military as tools, and they're treating them like pets instead of devices. But then there are also some really fantastic use cases in health and education that rely specifically on this socialization of the robot. You can use a robot as a replacement for animal therapy where you can't use real animals. We're seeing great results in therapy with autistic children, engaging them in ways that we haven't seen previously. So there are a lot of really cool ways that we can make this work for us as well. >> Barry, your thoughts, have you ever thought that we'd be adopting AI as pets some day? >> Oh yeah, absolutely. Like Kate, I'm very excited about all of this too, and I think there's a few, I agree with everything Kate has said. Of course, you know, coming back to the remark you made at the beginning about people putting their faces in their smartphones all the time, you know, we can't crowdsource our sense of dignity, or that we can't have social media as the currency for how we value our lives or how we compare ourselves with others. So, you know, we do have to be careful here. Like, one of the really nice things about, one of the really nice examples of an AI system that was given some significant personality was, quite recently, Tuomas Sandholm and others at Carnegie Mellon produced this Liberatus poker playing bot, and this AI system was playing against these top-class Texas hold' em players. And all of these Texas hold 'em players were attributing a level of cunning and sophistication and mischief on this AI system that clearly it didn't have because it was essentially trying to just behave rationally. But we do like to project human characteristics onto AI systems. And I think what would be very very nice, and something we need to be very very careful of, is that when AI systems are around us, and particularly robots, you know, we do need to treat them with respect. And what I mean is, we do make sure that we treat those things that are serving society in as positive and nice a way as possible. You know, I do judge people on how they interact with, you know, sort of the least advantaged people in society. And you know, by golly, I will judge you on how you interact with a robot. >> Rajen-- >> We've actually done some research on that, where-- >> Oh, really-- >> We've shown that if you're low empathy, you're more willing to hit a robot, especially if it has a name. (panel laughing) >> I love all my equipment here, >> Oh, yeah? >> I got to tell ya, it's all beautiful. Rajen, computer science, and now AIs having this kind of humanization impact, this is an interesting shift. I mean, this is not what we studied in computer science. We were writin' code. We were going to automate things. Now there's notions of math, and not just math cognition, human relations, your thoughts on this? >> Yeah, you know what's interesting is that I think ultimately it boils down to the user experience. And I think there is this part of this which is around humanization, but then ultimately it boils down to what are you trying to do? And how well are you doing it with this technology? And I think that example around the Roomba is very interesting, where I think people kind of feel like this is more, almost like a person. But, also I think we should focus as well on what the technology is doing, and what impact it's having. My best example of this is Google Photos. And so, my whole family uses Google Photos, and they don't know that underlying it is some of the most powerful AI in the world. All they know is that they can find pictures of our kids and their grandkids on the beach anytime that they want. And so ultimately, I think it boils down to what is the AI doing for the people? And how is it? >> Yeah, expectations become the new user experience. I love that. Okay, guys, final question, and also humanization, we talked about the robotics, but also the ethics here. Ethics reminds me of the old security debate, and security in the old days. Do you increase the security, or do you throw it all away and start over? So with this idea of how do you figure out ethics in today's modern society with it being a mirror? Do we throw it all away and do a do-over, can we recast this? Can we start over? Do we augment? What's the approach that you guys see that we might need to go through right now to really, not hold back AI, but let it continue to grow and accelerate, educate people, bring value and user experience to the table? What is the path? We'll start with Barry, and then Kate, and then Rajen. >> Yeah, I can kick off. I think ethics gets us some of the way there, right? So, obviously we need to have a set of principles that we sign up to and agree upon. And there are literally hundreds of documents on AI ethics. I think in Europe, for example, there are 128 different documents around AI ethics, I mean policy documents. But, you know, we have to think about how are we actually going to make this happen in the real world? And I think, you know, if you take the aviation industry, that we trust in airplanes, because we understand that they're built to the highest standards, that they're tested rigorously, and that the organizations that are building these things are held account when things go wrong. And I think we need to do something similar in AI. We need good strong engineering, and you know, ethics is fantastic, and I'm a strong believer in ethical codes, but we do need to make it practical. And we do need to figure out a way of having the public trust in AI systems, and that comes back to education. So, I think we need the general public, and indeed ourselves, to be a little more cynical and questioning when we hear stories in the media about AI, because a lot of it is hyped. You know, and that's because researchers want to describe their research in an exciting way, but also, newspaper people and media people want to have a sticky subject. But I think we do need to have a society that can look at these technologies and really critique them and understand what's been said. And I think a healthy dose of cynicism is not going to do us any harm. >> So, modernization, do you change the ethical definition? Kate, what's your thoughts on all this? >> Well, I love that Barry brought up the aviation industry because I think that right now we're kind of an industry in its infancy, but if we look at how other industries have evolved to deal with some thorny ethical issues, like for example, medicine. You know, medicine had to develop a whole code of ethics, and develop a bunch of standards. If you look at aviation or other transportation industries, they've had to deal with a lot of things like public perception of what the technology can and can't do, and so you look at the growing pains that those industries have gone through, and then you add in some modern insight about interdisciplinary, about diversity, and tech development generally. Getting people together who have different experiences, different life experiences, when you're developing the technology, and I think we don't have to rebuild the wheel here. >> Yep. >> Rajen, your thoughts on the path forward, throw it all away, rebuild, what do we do? >> Yeah, so I think this is a really interesting one because of all the technologies I've worked in within my career, everything from the internet, to mobile, to virtualization, this is probably the most powerful potential for human good out there. And AI, the potential of what it can do is greater than almost anything else that's out there. However, I do think that people's perception of what it's going to do is a little bit skewed. So when people think of AI, they think of self-driving cars and robots and things like that. And that's not the reality of what AI is today. And so I think two things are important. One is to actually look at the reality of what AI is doing today and where it impacts people lives. Like, how does it personalize customer interactions? How does it make things more efficient? How do we spot things that we never were able to spot before? And start there, and then apply the ethics that we've already known for years and years and years, but adapt it to a way that actually makes sense for this. >> Okay, like that's it for the Around theCUBE. Looks like we've tallied up. Looks like Professor Barry 11, third place, Kate in second place with 13. Rajen with 16 points, you're the winner, so you get the last word on the segment here. Share your final thoughts on this panel. >> Well, I think it's really important that we're having this conversation right now. I think about back to 1994 when the internet first started. People did not have that conversation nearly as much at that point, and the internet has done some amazing things, and there have been some bad side effects. I think with this, if we have this conversation now, we have this opportunity to shape this technology in a very very positive way as we go forward. >> Thank you so much, and thanks everyone for taking the time to come in. All the way form Cork, Ireland, Professor Barry O'Sullivan. Dr. Kate Darling doing some amazing research at MIT on robotics and human psychology and like a new book coming out. Kate, thanks for coming out. And Rajen, thanks for winning and sharing your thoughts. Thanks everyone for coming. This is Around theCUBE here, Unpacking AI segment around ethics and human interaction and societal impact. I'm John Furrier with theCUBE. Thanks for watching. (upbeat music)

>>from our studios in the heart of Silicon Valley, Palo Alto, California It is a cute conversation. >>Hey, welcome back already, Jeffrey. Here with the cue, we're in our pal Amato Studios for acute conversation or excited, have ah, many Time Cube alone. I has been at all types of companies. He's moving around. We like to keep him close because he's got a great feel for what's going on. And now he's starting a new adventure. Eso really happy to welcome Alan Cohen back to the studio. Only great to see you. >>Hey, Draft, how are you >>in your new adventure? Let's get it right. It's the D C v c your partner. So this is ah, on the venture side. I'm gonna dark. You've gone to the dark side of the money side That is not a new firm, dark side. You know what's special about this town of money adventure right now, but you guys kind of have a special thesis. So tell us about yeah, and I think you've spoken >>to Matt and Zack. You know my partners in the past, So D. C. V. C is been in the venture business for about a decade and, um, you know, the 1st 5 years, the fund was very much focused on building, ah, lot of the infrastructure that we kind of take for granted. No things have gone into V m wear and into Citrix, and it's AWS, and hence the data collect of the D. C out of D. C. V. C. Really, the focus of the firm in the last five years and going forward is an area we call deep tech, which think about more about the intersection of science and engineering so less about. How do you improve the IittIe infrastructure? But how do you take all this computational power and put it to work in in specific industries, whether it's addressing supply chains, new forms of manufacturing, new forms of agriculture. So we're starting to see all that all the stuff that we've built our last 20 years and really apply it against kind of industrial transformation. So and we're excited. We just raise the $725 million fund. So we I got a little bit of ammunition to work with, >>Congratulate says, It's fun. Five. That's your eighth fund. Yeah, and really, it's consistent with where we're seeing all the time about applied a I and applied machine. Exactly. Right in New York, a company that's gonna build a I itt s'more the where you applying a i within an application, Where you applying machine, learning within what you do. And then you can just see the applications grow exactly right. Or are you targeting specific companies that are attacking a particular industrial focus and just using a eyes, their secret sauce or using deep taxes or secret uh, all of the above? Right. So, like I >>did when I think about D c v c like it's like so don't think about, um, I ops or throughput Orban with think about, um uh, rockets, robots, microbes, building blocks of effectively of human life and and of materials and then playing computational power and a I against those areas. So a little bit, you know, different focus. So, you know, it's the intersection of compute really smart computer science, but I'll give you a great example of something. It would be a little bit different. So we are investors and very active in a company called Pivot Bio, which is not exactly a household name. Pivot bio is a company that is replacing chemical fertilizer with microbes. And what I mean by that is they create microbes they used. So they've used all this big data and a I and computational power to construct microbes that when you plant corn, you insert the microbe into the planting cycle and it continuously produces nitrogen, which means you don't have to apply fertilizer. Right? Which fertilizer? Today in the U. S. A. $212 billion industry and two things happen. One you don't have. All of the runoff doesn't leech into the ground. The nitrous does. Nitrogen doesn't go into the air, and the crop yield has been a being been between about 12 and 15% higher. Right? >>Is it getting put? You know, the food industry is such a great place, and there's so many opportunities, both in food production. This is like beyond a chemical fertilizer instead of me. But it's great, but it's funny because you think of GMO, right? So all food is genetically modified. It's just It took a long time in the past because you had to get trees together, and yet you replant the pretty apples and throw the old apple trees away. Because if you look at an apple today versus an apple 50 years, 100 years, right, very, very different. And yet when we apply a man made kind of acceleration of that process than people, you know, kind of pushed back Well, this is this is not this is not nature, So I'm just curious in, in, in in, Well, this is like a microbe, you know? You know, they actually it is nature, right? So nature. But there'll be some crazy persons that wait, This is not, you know, you're introducing some foreign element into Well, you could take >>potash and pour it on corn. Or you could create a use, a microbe that creates nitrogen. So which one is the chemical on which one is nature, >>right, That that's why they get out. It's a funny part of that conversation, but but it's a different area. So >>you guys look, you guys spent a lot of time on the road. You talked a lot of startups. You talked a lot of companies. You actually talked to venture capitalists and most of the time where you know, we're working on the $4 trillion I t sector, not an insignificant sector, right? So that's globally. It's that's about the size of the economy. You know, manufacturing, agriculture and health care is more like 20 to $40 billion of the economy. So what we've also done is open the aperture to areas that have not gone through the technical disruption that we've seen an I t. Right now in these industries. And that's what's that mean? That's why I joined the firm. That's why I'm really excited, because on one hand you're right. There is a lot of cab you mentioned we were talking before. There is a lot of capital in venture, but there's not a CZ much targeted at the's area. So you have a larger part of global economy and then a much more of specific focus on it. >>Yeah, I think it's It's such a you know, it's kind of the future's here kind of the concept because no one knows, you know, the rate of which tech is advancing across all industries currently. And so that's where you wake up one day and you're like, Oh, my goodness, you know, look at the impacts on transportation. Look at the impacts on construction of the impacts on health care. Look at the impacts on on agriculture. So the opportunity is fantastic and still following the basic ideas of democratizing data. Not using a sample of old data but using, you know, real time analytics on hold data sets. You know, all these kind of concepts that come over really, really well to a more commercial application in a nightie application. Yeah. So, Jeff, I'm kind of like >>looking over your shoulder. And I'm looking at Tom Friedman's book The world is flat. And you know, if we think about all of us have been kind of working on the Internet for the last 20 years, we've done some amazing things like we've democratized information, right? Google's fairly powerful part of our lives. We've been able to allow people to buy things from all over the world and ship it. So we've done a lot of amazing things in the economy, but it hasn't been free. So if I need a 2032 c r. 20 to 32 battery for my key fob for my phone, and I buy it from Amazon and it comes in a big box. Well, there's a little bit of a carbon footprint issue that goes with that. So one of our key focus is in D. C V. C, which I think is very unique, is we think two things can happen is that weaken deal with some of the excess is over the economy that we built and as well as you know, unlock really large profit pulls. At the end of the day, you know, it has the word Venture Patrol says the word capital, right? And so we have limited partners. They expect returns. We're doing this obviously, to build large franchises. So this is not like this kind of political social thing is that we have large parts of the economy. They were not sustainable. And I'll give you some examples. Actually, you know, Jeff Bezos put out a pledge last week to try to figure out how to turn Amazon carbon neutral. >>Pretty amazing thing >>right with you from the was the richest person Now that half this richest person in the world, right? But somebody who has completely transformed the consumer economy as well as computing a comedy >>and soon transportation, right? So people like us are saying, Hey, >>how can we help Jeff meet his pledge? Right? And like, you know, there are things that we work on, like, you know, next generation of nuclear plants. Like, you know, we need renewables. We need solar, but there's no way to replace electricity. The men electricity, we're gonna need to run our economy and move off of coal and natural gas, Right? So, you know, being able to deal with the climate impacts, the social impacts are going to be actually some of the largest economic opportunities. But you can look at it and say, Hey, this is a terrible problem. It's ripping people across. I got caught in a traffic jam in San Francisco yesterday upon the top of the hill because there was climate protest, right? And you know, so I'm not kind of judging the politics of that. We could have a long conversation about that. The question is, how do you deal with these real issues, right and obviously and heady deal with them profitably and ethically, and I think that something is very unique about you know, D. C. V. C's focus and the ability to raise probably the largest deep tech fund ever to go after. It means that you know, a lot of people who back us also see the economic opportunity. And at the end of day there, you know, a lot of our our limited partners, our pension funds, you know, in universities, like, you know, there was a professor who has a pension fund who's gotta retire, right? So a little bit of that money goes into D C V C. So we have a responsibility to provide a return to them as well as go after these very interesting opportunities. >>So is there any very specific kind of investment thesis or industry focus Or, you know, kind of a subset within, you know, heavy lifting technology and science and math. That's a real loaded question in front of that little. So we like problems >>that can be solved through massive computational capability. And so and that reflects our heritage and where we all came from, right, you and I, and folks in the industry. So, you know, we're not working at the intersection of lab science at at a university, but we would take something like that and invest in it. So we like you know we have a lot of lessons in agriculture and health care were, surprisingly, one of the largest investors in space. We have investments and rocket labs, which is the preferred launch vehicle for any small satellite under two and 1/2 kilograms. We are large investors and planet labs, which is a constellation of 200 small satellites over investors and compel a space. So, uh, well, you know, we like space, and, you know, it's not space for the sake of space. It's like it's about geospatial intelligence, right? So Planet Labs is effectively the search engine for the planet Earth, right? They've been effectively Google for the planet, right? Right. And all that information could be fed to deal with housing with transportation with climate change. Um, it could be used with economic activity with shipping. So, you know, we like those kinds of areas where that technology can really impact and in the street so and so we're not limited. But, you know, we also have a bio fund, so we have, you know, we're like, you know, we like agriculture and said It's a synthetic biology types of investments and, you know, we've still invest in things like cyber we invest in physical security were investors and evolve, which is the lead system for dealing with active shooters and venues. Israel's Fordham, which is a drone security company. So, um, but they're all built on a Iot and massive >>mess. Educational power. I'm just curious. Have you private investment it if I'm tree of a point of view because you got a point of view. Most everything on the way. Just hear all this little buzz about Quantum. Um, you know, a censure opened up their new innovation hub in the Salesforce tower of San Francisco, and they've got this little dedicated kind of quantum computer quanta computer space. And regardless of how close it is, you know there's some really interesting computational opportunities last challenges that we think will come with some period of time so we don't want them in encryption and leather. We have lost their quantum >>investments were in literally investors and Righetti computing. Okay, on control, cue down in Australia, so no, we like quantum. Now, Quantum is a emerging area like it's we're not quite at the X 86 level of quantum. We have a little bit of work to get there, but it offers some amazing, you know, capabilities. >>One thing >>that also I think differentiates us. And I was listening to What you're saying is we're not afraid. The gold long, I mean a lot of our investments. They're gonna be between seven and 15 years, and I think that's also it's very different if you follow the basic economics adventure. Most funds are expected to be about 10 years old, right? And in the 1st 3 or four years, you do the bulk of the preliminary investing, and then you have reserves traditional, you know, you know, the big winners emerged that you can continue to support the companies, some of ours, they're going to go longer because of what we do. And I think that's something very special. I'm not. Look, we'd like to return in life of the fun. Of course, I mean, that's our do share a responsibility. But I think things like Quantum some of these things in the environment. They're going to take a while, and our limited partners want to be in that long ride. Now we have a thesis that they will actually be bigger economic opportunities. They'll take longer. So by having a dedicated team dedicated focus in those areas, um, that gives us, I think, a unique advantage, one of one of things when we were launching the fund that we realized is way have more people that have published scientific papers and started companies than NBA's, um, in the firm. So we are a little bit, you know, we're a little G here. That >>that's good. I said a party one time when I was talking to this guy. You were not the best people at parties we don't, but it is funny. The guy was He was a VC in medical medical tech, and I didn't ask him like So. Are you like a doctor? Did you work in a hospital where you worked at A at a university that doesn't even know I was investment banker on Wall Street and Michael, that's that's how to make money move. But do you have? Do you have the real world experience of being in the trenches? Were Some of these applications are being used, but I'm also curious. Where do you guys like to come in? ABC? What's your well, sweets? Traditionally >>we are have been a seed in Siri's. A investor would like to be early. >>Okay, Leader, follow on. Uh, everybody likes the lead, right? Right, right, right. You know what? Your term feet, you >>know? Yeah, right. And you have to learn howto something lead. Sometimes you follow. So we you know, we do both. Okay, Uh, there are increasing as because of the size of the fund. We will have the opportunity to be a little bit more multi stage than we traditionally are known for doings. Like, for example, we were seed investors in little companies, like conflict an elastic that worked out. Okay, But we were not. Later stage right. Investors and company likes companies like that with the new fund will more likely to also be in the later stages as well for some of the big banks. But we love seed we love. Precede. We'd like three guys in in a dog, right? If they have a brilliant >>tough the 7 50 to work when you're investing in the three guys in a dog and listen well and that runs and runs and you know you >>we do things we call experiments. Just you know, uh, we >>also have >>a very unique asset. We don't talk about publicly. We have a lot of really brilliant people around the firm that we call equity partners. So there's about 60 leaning scientists and executives around the world who were also attached to the firm. They actually are, have a financial stake in the firm who work with us. That gives us the ability to be early Now. Clearly, if you put in a $250,000 seed investment you don't put is the same amount of time necessarily as if you just wrote a $12 million check. What? That's the traditional wisdom I found. We actually work. Address this hard on. >>Do you have any? Do you have any formal relationships within the academic institutions? How's that >>work? Well, well, I mean, we work like everybody else with Stanford in M I t. I mean, we have many universities who are limited partners in the fund. You know, I'll give you an example of So we helped put together a company in Canada called Element A I, which actually just raised $150 million they, the founder of that company is Ah, cofounder is a fellow named Joshua Benji. Oh, he was Jeff Hinton's phD student. Him in the Vatican. These guys invented neural networks ing an a I and this company was built at a Yasha his position at the University of Montreal. There, 125 PhDs and a I that work at this firm. And so we're obviously deeply involved. Now, the Montreal A icing, my child is one of the best day I scenes in the world and cool food didn't and oh, yeah, And well, because of you, Joshua, because everybody came out of his leg, right? So I think, Yes, I think so. You know, we've worked with Carnegie Mellon, so we do work with a lot of universities. I would, I would say his university's worked with multiple venture firm Ah, >>such an important pipeline for really smart, heavy duty, totally math and tech tech guys. All right, May, that's for sure. Yeah, you always one that you never want to be the smartest guy in the room, right, or you're in the wrong room is what they say you said is probably >>an equivalent adventure. They always say you should buy the smallest house in the best neighborhood. Exactly. I was able to squeeze its PCB sees. I'm like, the least smart technical guy in the smartest technical. There >>you go. That's the way to go. All right, Alan. Well, thanks for stopping by and we look forward. Thio, you bring in some of these exciting new investment companies inside the key, right? Thanks for the time. Alright. He's Alan. I'm Jeff. You're watching the Cube. We're Interpol about the studios. Thanks for watching. We'll see you next time.

>> live from Washington, D. C. It's the Cube covering a ws public sector summit by Amazon Web services. >> Welcome back, everyone to our nation's capital. We are the Cube. We are live at A W s Public Sector summit. I'm your host Rebecca Night, along with my co host, John Farrier. We're joined by Ken Eisner Director Worldwide Educational programs at a WS Thanks so much for coming on the show >> you for having me. >> So tell our viewers a little bit. About what? What you do as the director of educational programs. Sure, I head >> up a program called a Ws Educate a ws educate is Amazon's global initiative to provide students and teachers around the world with the resource is that they need really to propel students into this awesome field of cloud computing. We launched it back in May of 2,015 and we did it to fill this demand. If we look at it today, what kind of right in the midst of this fourth industrial revolution is changing the means of production obviously in the digital on cloud space, But it's also creating this new worker class all around. Yeah, the cloud Advanced services like machine learning I robotics, I ot and so on. And if you looked at the employer demand, um, Cloud computing has been the number one linked in skill for the past four years in a row. We look at cloud computing. We kind of divide into four families. Software development, cloud architecture, the data world, you know, like machine learning I data science, business intelligence and Alex and then the middle school opportunities like technical customer support, age and cybersecurity, which can range all the way from middle school of Ph. D. But yet the timeto hire these people has grown up dramatically. Glass door as study of companies over there platform between two thousand 92 1,050 18 and show that the timeto higher had increased by 80%. Yet just think about that we talk about I mean, this conference is all about innovation. If you don't have builders, if you don't have innovators, how the heck Kenya Kenya innovate? >> Can I gotta ask you, Andy, just to have known him for over eight years and reporting on him and covering it was on when when everyone didn't understand yet what it was. Now everyone kind of does our congratulations and success. But to see him on stage, talk passionately about education. Yeah, mean and knowing Andy means it's kind of boiled up because he's very reserved, very conservative guy, pragmatic. But for him to be overtly projecting, his opinion around education, which was really yeah, pretty critical means something's going on. This is a huge issue not just in politics, riel, state, local areas where education, where >> the root of income inequality it's it's a lot of. >> There's a lot of challenges. People just aren't ready for these new types of jobs that are coming out that >> pay well, by the way. And this is Elliott >> of him out there that are unfilled for the first time, there are more jobs unfilled than there are candidates for them. You're solving this problem. Tell us what's going on in Amazon. Why the fewer what's going on with all this? Why everyone's so jacked up >> a great point. I, Andy, I think, said that education is at a crisis point today and really talked about that racial inequality piece way. Timeto hire people in the software development space Cloud architecture um technical called cloud Support Age. It's incredibly long so that it's just creating excess costs into the system, but were so passionate, like if you look at going to the cloud, Amazon wants to disrupt areas where we do not see that progress happening. Education is an area that's in vast need for disruption. There are people were doing amazing stuff. We've heard from Cal Poly. We've heard from Yeah, Arizona State. Carnegie Mellon. There's Joseph Alan at North Northeastern. >> People are >> doing great stuff. We're looking at you some places that are doing dual enrollment programs between high school and community in college and higher ed. But we're not moving fast enough, but you guys >> are provided with educate your program. This is people can walk in the front door without any kind of going through gatekeepers or any kind of getting college. This is straight up from the front, or they could be dropouts that could be post college re Skilling. Whatever it is, they could walk in the front door and get skilled up through educators that correct, >> we send people the ws educate dot com. All you need is some element of being in school activity, or you won't be going back from Re Skilling perspective and you came free access into resource is whether your student teacher get free access into content. That's map two jobs, because again, would you people warm from the education way? All want enlightenment contributors to sai all important, But >> really they >> want careers and all the stats gallop ransom good stats about both what, yet students and what industry wants. They want them to be aligned to jobs. And we're seeing that there's a man >> my master was specifically If I'm unemployed and I want to work, what can I do? I walk into you, You can go >> right on and we can you sign up, we'll give you access to these online cloud. Career pathways will give you micro credentials so we can bad you credential you against you We belong something on Samarian Robo maker. So individual services and full pathways. >> So this a >> direct door for someone unemployed We're going to get some work and a high paying job, >> right? Right. Absolutely. >> We and we also >> give you free access into a ws because we know that hands on practice doing real world applications is just vital. So we >> will do that end. By the way, at the end of >> this, we have a job board Amazon customer In part of our job, we're all saying >> these air >> jobs are super high in demand. You can apply to get a job as an intern or as a full time. Are you through our job? >> This is what people don't know about Rebecca. The war is not out there, and this is the people. Some of the problems. This is a solution >> exactly, but I actually want to get drilled down a little bit. This initiative is not just for grown ups. It's it's for Kimmie. This is for you. Kid starts in kindergarten, So I'm really interested to hear what you're doing and how you're thinking about really starting with the little kids and particularly underrepresented minorities and women who are not. There were also under representative in the in the cloud industry how you're thinking expansively about getting more of those people into these jacks. And actually, it's still >> Day one within all y'all way started with Way started with 18 and older because we saw that as the Keith the key lever into that audience and start with computer science but we've expanded greatly. Our wee last year reinvent, We introduced pathways for students 14 over and cloud literacy materials such as a cloud inventor, Cloud Explorer and Cloud Builder. Back to really get at those young audiences. We've introduced dual enrollment stuff that happens between high school community college or high school in higher ed, and we're working on partnerships with scratch First Robotics Project lead the way that introduced, whether it's blocked based coding, robotics were finding robotics is such a huge door opener again, not just for technically and >> get into it absolutely, because it's hands on >> stuff is relevant. They weren't relevant stuff that they can touch that. They can feel that they can open their browser, make something happen, build a mobile application. But they also want tohave pathways into the future. They want to see something that they can. Eventually you'll wind up in and a ws the cloud just makes it real, because you, Khun do real worlds stuff from a browser by working with the first robot. Biotics are using scratch toe develop Ai ai extensions in recognition and Lex and Polly and so on. So we've entered into partnerships with him right toe. Open up those doors and create that long term engagement and pipe on into the high demand jobs of tomorrow. >> What do you do in terms of the colleges that you mentioned and you mention Northeastern and Cal Poly Arizona State? What? What are you seeing? Is the most exciting innovations there. >> Yes. So, first of all, we happen to be it. We're in over 24 100 institutions around the world. We actually, by the way, began in the U. S. And was 65% us. Now it's actually 35% US 65% outside. We're in 200 countries and territories around the world. But institutions such as the doing amazing stuff Polo chow at a Georgia Tech. Things that he's doing with visual ization on top of a ws is absolutely amazing. We launched a cloud Ambassador program to reward and recognize the top faculty from around the world. They're truly doing amazing stuff, but even more, we're seeing the output from students. There was a student, Alfredo Cologne. He was lived in Puerto Rico, devastated by Hurricane Maria. So lost his, you know, economic mobility came to Florida and started taking classes at local schools. He found a ws educate and just dove headlong into it. Did eight Pathways and then applied for a job in Dev Ops at Universal Studios and received a job. He is one of my favorite evangelists, but and it's not just that higher ed. We found community college students. We launched a duel enrolment with between Santa Monica College and Roosevelt High School in Los Angeles, focusing again a majority minority students, largely Hispanic, in that community. Um, and Michael Brown, you finish the cloud computing certificate, applied for an internship, a mission clouds so again a partner of ours and became a God. Hey, guys, internship And they start a whole program around. So not only were seeing your excitement out of the institutions, which we are, but we're also seeing Simon. Our students and businesses all want to get involved in this hiring brigade. >> Can I gotta ask. We're learning so much about Amazon would cover him for a long time. You know all the key buzzwords. Yeah, raise the bar all these terms working backwards. So >> tell us about what's your >> working backwards plan? Because you have a great mission and we applaud. I think it's a super critical. I think it's so under promoted. I think we'll do our best to kind of promote. It's really valuable to society and getting people their jobs. Yeah, but it's a great opportunity, you know, itself. But what's your goal? What's your What's your objective? How you gonna get there, What your priorities, What do you what do you what do you need >> to wear? A pure educational workforce? And today our job is to work backwards from employers and this cloud opportunity, >> the thing that we >> care about our customers still remains or student on DH. So we want to give excessive mobility to students into these fields in cloud computing, not just today and tomorrow. That requires a lot that requires machine lurking in the algorithm that you that changed the learning objectives you based on career, so content maps to thes careers, and we're gonna be working with educational institutions on that recruited does. Recruiting doesn't do an effective job at matching students into jobs. >> Are we >> looking at all of just the elite institutions as signals for that? That's a big >> students are your customer and customer, but older in support systems that that support you, right? Like Cal Poly and others to me. >> Luli. We've also got governments. So we were down in Louisiana just some last month, and Governor Bel Edwards said, We're going to state why with a WS educates cloud degree program across all of their community college system across the University of Louisiana State system and into K 12 because we believe in those long term pathways. Never before have governors have ministers of country were being with the Ministry of Education for Singapore in Indonesia, and we're working deep into India. Never had they been more aligned toe workforce development. It creates huge unrest. We've seen this in Spain and Greece we see in the U. S. But it's also this economic imperative, and Andy is right. Education is at a crisis. Education is not solving the needs of all their constituents, but also industries to blame. We haven't been deeply partnered with education. That partnership is such a huge part of >> this structural things of involved in the educational system. It's Lanier's Internets nonlinear got progressions air differently. This is an opportunity because I think if the it's just like competition, Hey, if the U. S Department of Education not get their act together. People aren't going to go to school. I mean, Peter Thiel, another political spectrums, was paying people not to go to college when I was a little different radical view Andy over here saying, Look at it. That's why you >> see the >> data points starting to boil up. I see some of my younger son's friends all saying questioning right what they could get on YouTube. What's accessible now, Thinking Lor, You can learn about anything digitally now. This is totally People are starting to realize that I might not need to be in college or I might not need to be learning this. I can go direct >> and we pay lip >> service to lifelong education if you end. If you terminally end education at X year, well, you know what's what's hap happening with the rest of your life? We need to be lifelong learners. And, yes, we need to have off ramps and the on ramps throughout our education. Thie. Other thing is, it's not just skill, it's the skills are important, and we need to have people were certified in various a ws skills and come but we also need to focus on those competencies. Education does a good job around critical decision making skills and stuff like, um, collaboration. But >> do they really >> do a good job at inventing? Simplified? >> Do they teach kids >> to fam? Are we walking kids to >> social emotional, you know? >> Absolutely. Are we teaching? Were kids have tio think big to move >> fast and have that bias for action? >> I think that I want to have fun doing it way. Alright, well, so fun having you on the show. A great conversation. >> Thank you. I appreciate it. >> I'm Rebecca Knight for John. For your you are watching the cube. Stay tuned.

>> from Palo Alto, California It's the Cube covering the EM Where women Transforming technology twenty nineteen. Brought to You by VM Wear >> Hi Lisa Martin with the Cube on the ground at the end. Where in Palo Alto, California, for the fourth annual Women Transforming Technology, even W. T. Squared on event that is near and dear to my heart. Excited to welcome back to the Cube pretty much. Rog Lachman, engineering leader, blocked in at the end where pretty much It's so great to have you back on the Cube. Thank you, Lisa. It's amazing to be here, and I can't believe it's been a year, a year. And so last year, when Protein was here, she launched her book. Nevertheless, she persistent love the title You just Did a session, which we'LL get to in a second, but I'd love to get your your experiences in the last year about the book launch. What's the feedback? Ben? What are some of the things that have made me feel great and surprised you at the same time? It's been fantastic. I wasn't expecting that when I started to write this book. It was more like I want to impact one woman's life. But what was interesting is I delivered around twenty twenty five talks last year. My calendar's booked for this year, but every time I go give a talk, my Lincoln goes crazy and I'm connecting with all these women and men. And it's just fantastic because they're basically resonating with everything I talk about in the book. I spoke at the Federal Reserve. Wow, I was like, This is a book on tech and they were like, No, this impacts all of us And I spoke to a group of lawyers and actually, law firms have fifty fifty when they get into law, right when they get into whenever I mean live, I'm not that familiar with it. But getting to partner is where they don't have equality or diversity, and it's resonated. So now I'm like, maybe I should just take the word check out What? You It's been impactful. And so last year was all about companies, so I did. You know, I spoke at uber I spoken Veum, where spoken nutanix it's looking a lot of these companies last year. This year is all about schools, fantastic schools of all different type, so I you know, I've done a talk at San Jose State. I went to CMU. They invited me over Carnegie Mellon. I supported the robotics team, which is all girls team. Nice. And it was fantastic because these girls high school kids were designing robots. They were driving these robots. They were coding and programming these robots and was an all girls team. And I asked them, I said, But you're excluding the men and the boys and they said no. When it's a combined boy girls team, the women end up the girls and organizing the men of the boys are actually writing the code. They're doing the drilling there, doing all that. And so the girls don't get to do any of that. And I was looking at just the competition and as watching these teams, the boy girl steams and those were all organizing. And I thought, this is exactly what happens in the workforce. You're right. Yeah. We come into the workforce, were busy organizing, coordinating and all that, and the men are driving the charge. And that's why these kids where this is at high school, Yeah, thirteen to seventeen, where this is becoming part of their cultural upbringing. Exactly. Pretty. In great. Yes, yes. And a very young age. So that was fascinating. I think that surprised me. You know, you were asking me what surprised you that surprised me. And what also surprised me was the confidence. Though these girls were doing all these things. I've never built a robot. I would love to. I haven't built a robot, and they were doing all these amazing things, and I thought, Oh, my God, >> they're like, >> confident women. But they were not. And it was because they felt that there was too much to lose. They don't want to take risks, they don't want to fail. And it was that impostor syndrome coming back so that conditioning happens way more impossible syndrome is something that I didn't even know what it wass until maybe the last five or six years suddenly even just seeing that a very terse description of anyone Oh, my goodness, it's not just me. And that's really a challenge that I think the more the more it's brought to light, the more people like yourself share stories. But also what your book is doing is it's not just like you were surprised to find out It's not just a tech. This is every industry, Yes, but his pulse syndrome is something that maybe people consider it a mental health issue and which is so taboo to talk about. But I just think it's so important to go. You're not alone. Yeah, vast majority men, women, whatever culture probably have that. Let's talk about that. Let's share stories. So that your point saying why I was surprised that these young girls had no confidence. Maybe we can help. Yes, like opening up. You know, I'm sharing it being authentic. Yeah. So I'm looking at my second book, which basically says what the *** happens in middle school? Because what happens is somewhere in middle school, girls drop out, so I don't know what it is. I think it's Instagram or Facebook or boys or sex. I don't know what it is, but something happens there. And so this year of my focus is girls and you know, young girls in schools and colleges. And I'm trying to get as much research as I can in that space to see what is going on there, because that totally surprised me. So are you kind of casting a wide net and terms like as you're. Nevertheless, she persisted. Feedback has shown you it's obviously this is a pervasive, yes issue cross industry. This is a global pandemic, yes, but it's your seeing how it's starting really early. Tell me a little bit about some of the things that we can look forward to in that book. So one thing that's important is bravery, Which reshma So Johnny, who's the CEO off girls code? She has this beautiful quote, she says. We raise our voice to be brave, and we'd raise our girls to be perfect, pretty telling. And so we want to be perfect. We won't have the perfect hair, the perfect bodies. We want a perfect partner. That never happens. But we want all that and because we want to be perfect, we don't want to take risks, and we're afraid to fail. So I want to focus on that. I want to talk to parents. I want to talk to the kids. I want to talk to teachers, even professors, and find out what exactly it is like. What is that conditioning that happens, like, why do we raise our girls to be perfect because that impacts us at every step of our lives. Not even careers. It's our lives. Exactly. It impacts us because we just can't take that risk. That's so fascinating. So you had a session here about persistent and inclusive leadership at W T squared forth and you will tell me a little bit about that session today. What were some of the things that came up that you just said? Yes, we're on the right track here. So I started off with a very depressing note, which is twenty eighty five. That's how long it's gonna take for us to see equality. But I talked about what we can do to get to twenty twenty five because I'm impatient. I don't want to wait twenty eighty five I'LL be dead by them. We know you're persistent book title. You know, my daughter will be in the seventies. I just don't want that for her. So, through my research, what I found is we need not only women to lean in. You know, we've have cheryl sound. We're talking about how women need to lean in, and it's all about the women. And the onus is on the woman the burdens on the woman. But we actually need society. Selena. We need organizations to lean in, and we need to hold them accountable. And that's where we're going to start seeing that changes doing that. So if you take the m r. I. You know, I've been with him for ten years, and I always ask myself, Why am I still here? One of the things we're trying to do is trying to take the Cirrus early this morning rail Farrell talked about like on the panel. He said, We are now Our bonuses are tied to, you know, domestic confusion, like we're way have to hire, you know, not just gender, right, Like underrepresented communities as well. We need to hire from there, and they're taking this seriously. So they're actually making this kind of mandatory in some sense, which, you know, it kind of sucks in some ways that it has to be about the story that weighing they're putting a stake in the ground and tying it to executive compensation. Yes, it's pretty bold. Yes. So organizations are leaning in, and we need more of that to happen. Yeah. So what are some of the things that you think could, based on the first *** thing you talked about the second one that you think could help some of the women that are intact that are leaving at an alarming rate for various reasons, whether it's family obligations or they just find this is not an environment that's good for me mentally. What are some of the things that you would advise of women in that particular situation? First thing is that it's to be equal partnership at home. A lot of women leave because they don't have that. They don't have that support on having that conversation or picking the right partner. And if you do pick the wrong partner, it's having that conversation. So if you have equal partnership at home, then it's both a careers that's important. So you find that a lot of women leave tech or leave any industry because they go have babies, and that happens. But it's just not even that, like once they get past that, they come backto work. It's not satisfying because they don't get exciting projects to work on that you don't get strategic projects, they don't have sponsors, which is so important toward the success, and they they're you know, people don't take a risk on them, and they don't take a risk. And so these are some of those things that I would really advice women. And, you know, my talk actually talked about that. Talked about how to get mail allies, how to get sponsors. Like what? You need to actually get people to sponsor you. Don't talk to me a little bit more about that. We talk about mentors a lot. But I did talk this morning with one of our guests about the difference between a sponsor and a mentor. I'd love you to give Sarah some of your advice on how women can find those sponsors. And actually, we activate that relationship. So mentors, uh, talk to you and sponsors talk about okay. And the way to get a sponsor is a is. You do great work. You do excellent work. Whatever you do, do it well. And the second thing is B is brag about it. Talk about it. Humble bragging, Yeah. Humble bragging talkabout it showcases demo it and do it with people who matter in organizations, people who can notice your work building that brand exactly. And you find that women are all the men toward and under sponsored. Interesting, Yes. How do you advise that they change that? There was a Harvard study on this. They found that men tend to find mentors are also sponsors. So what they do is, you know, I like you to stick pad girl singer, he says. Andy Grove was his mentor, but Andy Grove was also his sponsor in many ways, in for his career at Intel, he was a sponsor and a mental. What women tend to do is we find out like even me, like I have female spot him. Mentors were not in my organization, and they do not have the authority to advocate for me. They don't They're not sitting in an important meeting and saying, Oh, patina needs that project for team needs to get promoted. And so I'm not finding the right mentors who can also be my sponsors, or I'm not finding this one says right, and that's happens to us all the time. And so the way we have to switch this is, you know, mentors, a great let's have mentors. But let's laser focus on sponsors, and I've always said this all of last year. I'm like the key to your cell. Success is sponsorship, and I see that now. I am in an organization when my boss is my sponsor, which is amazing, because every time I go into a meeting with him, he says, This is about pretty much grew up. This is a pretty mers group. It's not me asking him. He's basically saying It's pretty nose grow, which is amazing to hear because I know he's my mentor in sponsor as well. And it's funny when I gave him a copy of my book and I signed it and I said, And he's been my sponsor to be more for like ten years I said, Thank you for being my sponsor and he looked at me. He said, Oh, I never realized it was your sponsor So that's another thing is men themselves don't know they're in this powerful position to have an impact, and they don't know that they are sponsors as well. And so we need. We need women to Fox and sponsors. I always say find sponsors. Mentorship is great, but focus of sponsors Look, I think it's an important message to get across and something I imagine we might be reading about in your next book to come. I know. Yeah, well, we'LL see. Artie, thank you so much for stopping by the Cube. It's great to talk to you and to hear some of the really interesting things that you've learned from nevertheless you persistent and excited to hear about book number two and that comes out. You got a combined studio. I'd love to thank you and thank you. I'm Lisa Martin. You're watching the queue from BM Where? At the fourth Annual Women Transforming Technology event. Thanks for watching.

(energetic music) >> Live from Orlando, Florida, it's theCUBE covering .conf 18, brought to you by Splunk. >> We're back in Orlando, Splunk .conf 2018, I'm Dave Vellante, Stu Miniman, and this is theCUBE, the leader in live tech coverage. Doug Merritt is here, the CEO of Splunk, long time CUBE guest, great to see you again. >> Thank you, Dave, great to be here. >> So, loved the keynote yesterday and today. You guys have a lot of fun, I was laughing my you-know-what off at the auditions. They basically said, Doug wasn't a shoo in for the keynote, so they had these outtake auditions. They were really hilarious, you guys are a lot of fun. You got the great T-shirts, how do you feel? >> It's been a, my favorite time of year is .conf, both because there's usually so much that we're funneling to our customers at this time, but being here is just infectious, it's, and one of the things that always amazes me is it's almost impossible to tell who are the customers and who are the employees. That just, I think Devonia this morning said it's a family affair, and it's not just a family affair, it's that there's a shared passion, a shared, almost culture and value set, and there's, it just is a very inspiring and naturally flowing type of event and I know I'm biased because I'm the CEO of Splunk, but I don't, I just don't know of events that feel like our, like .conf does. There's a lot of great shows out there, but this has got a very unique feel to it. >> Well, we do a lot of shows, as you know, and I've always said, .conf, I think ServiceNow, does a great job obviously, re-invent the tableau shows. That energy is there, and the other thing is, we do, when we go to these shows, a lot of times, you'll look at the keynotes and say, are there any products being announced? You guys, that wasn't a problem here. You guys announced this -- >> Not this year. >> Bevy of products, I mean, it's clear the R and D is translating into stuff that people can consume, and obviously that you can sell, so that's huge. >> I'm really excited about the product roadmap right now, and it's, that was, when I got the job, almost three years ago, one of the key areas I leaned forward and the board was excited about it was, what, where or how are we going to take this product beyond the amazing index and search technology that we have? And this show, it takes a while to progress the roadmap to the point that you can get the type of volume that we have here, but this show was the first time that I felt that we had laid enough of the tracks, so you could see a much, much broader landscape of capabilities, and now it's a challenge of packaging and making sure our customers are successful with it, with the product that we just have, the products we've announced. >> Cloud caught a lot of companies and a lot of end user companies, flatfooted. You guys have embraced the cloud, not only with the AWS partnership, which we're going to talk about, but also the business model. You're successfully transitioning from a company with perpetual license model, to a ratable model, which is never easy. Wall Street is killing companies who try to do that. Why have you been successful doing that? You know, give us an update. >> Yeah, so five years ago, less than 20% of our contracts were, had any type of subscription orientation to it, whether it's a multi-year term or a cloud. We'd just launched our cloud four years ago. And we moved from there to we had told the street there would be 65% term in subscription by the end of this year and updated guidance at the end of the second quarter, which is just a month and change ago, that we've already hit the 75% mark that we were set in for next year, so it's been a pretty rapid progression and I think there're two elements that have helped us with that. One: cloud continues to catch fire and so the people's orientation on "Do I do something in the cloud?" four years ago they were much more nervous, so less nervous today. But data is growing at such a huge rate and people are still wrapping their heads around, "How do I take advantage of this data, how do I even begin to collect this data and then how do I take advantage of it?" And the elasticity that comes in the cloud and that comes with term contracts, we can flex out and flex back in, I think it's just a much more natural contracting motion than you bought this big, perpetual thing and pay maintenance on it, especially when someone is growing as fast as data is growing. >> Well and it requires you to communicate differently to the financial analysts. >> It does. >> Obviously, billings, you know, was an important metric. You've come up with some new metrics to help people understand the real health of the business. And one of the other metrics that strikes me, and you see this with some of the successful companies, I actually think Aneel Bhusri was sort of the modern version of this, is the number of seven figure deals. You're startin' to hit that, and it's not, the way he's phrased it was pretty good. It's not something you're trying to engineer, it's the outcome -- >> Yes. >> of having great, loyal customers, it's not something you try to micromanage. >> Right, and that's, just recently we dropped six figure deals, which, when I joined, you got this wonderful dynamic forecasting system that sits on top of sales for us, and so as head of sales, where I started, you're really paying attention to deals. I'd go down to a hundred thousand dollar deals that would track throughout the quarter. And now it's hard to get it down to the six figures 'cause we've got a big enough envelope of seven figure deals. So the business has changed pretty dramatically from where it was, but it is an outgrowth of our number one customer priority, which is, or number one corporate priority, which is customer success. 'Cause that investment by companies, when you get to a million dollars plus, in most cases that's a million annually, you better believe in and trust that vendor, 'cause that's no longer an easy, small departmental sale. You're usually at the CIO, CFO type level. So it's something that we're very honored by, that people trust us enough to get that footprint of Splunk to be that size and to feel like they're getting a value from Splunk to justify that purchase. >> Alright we'll get off the income statement, Stu, and you can read about all that stuff, and we're going to get into, we've got a lot of ground to cover with you, Doug. Jump in here, Stu. >> Yeah, so Doug, I've really enjoyed talking to some of your customers that, you know, most of them started on premises with you and now many of them, they're using Splunk cloud, it's really kind of a hybrid model, and it's been really interesting to watch the maturation of your partnership with Amazon, and being the leader in the cloud space. Give us a little bit of color as to what you're hearing from the customers, you said three, four years ago, you know, they were obviously a little bit more cautious around it, and bring us inside a little bit that partnership. >> Sure, so the first piece that, as part of Splunk, that I think is a little bit different than other vendors is because we are both a lower level infrastructural technology, right, data is, the way I frame what we do is there's these raw materials, which are all these different renditions of data around, and companies increasingly have to figure out how to gather together these different raw materials, put them together different ways, for the output that is driving their business. And we are the manufacturing parts provider that makes it easy for them to go and pick up any of these different compounds and then actually do what they want to do, which is make things happen with data. And that middle layer is really important and we have never taken a super strong stance either, we started on prem, but as we moved to cloud, we never took a strong stance saying everything should be in the cloud or everything should be on prem because data has gravity, there is physics to data. And it doesn't always make sense to move data around and it doesn't always make sense to keep data stagnant, so having that flexibility, being able to deploy your collection capability, whether it's ours or third party, your storage capability, and then your process and your search, what are you going to do with the data, anywhere that makes sense for a customer, I think, is important. And that's part of that hybrid story, is as people increasingly trust and interview us and other cloud vendors to build core apps and then house a lot of their data, we absolutely need to be there. And I think that momentum of the cloud is certainly as secure and, in many cases, more secure than my on prem footprint, and the velocity of invention that some like ABDS is driving allows me to be much more agile and effectively drive application development and leading edge capability, I think just has people continuing to trust the cloud service providers a little bit more. >> Yeah well, we're here in the pavilion, and seeing your ecosystem grow, we've been at re:Invent for about five years, that ecosystem is just so >> It's been amazing. >> massive and full, give us a little bit about the relationship with Amazon and how you look at that, how Amazon looks at a company like yours. >> Yeah, it's been, so one, whenever you're playing with a highly inventive and hugely successful company like Amazon, my orientation and what I convey back to the company is our job is to be more inventive, more agile, and continue to find value with our maniacal focus every day being the data landscape. Data is a service and outcomes is a service, so our job is run faster than Amazon. And I think that this show and our announcements help illustrate that our invention cycle is in high tilt gear and for what we do, we are leaning in in a really aggressive way to add that value. With that backdrop, Andy and I formed this partnership four years ago. He felt there's enough value in Splunk and we were a good enough partner and the way we consume their services that he would commission and quota their sales reps whenever a Splunk sale was done in the ADBS landscape, which I think has been really helpful for us, but we obviously are a huge customer of ADBS's and they become an increasingly large customer of ours and finally gave us approval with their three year renewal a quarter ago to publicly reference them as a sizeable customer for us. >> Oh, okay, congratulations on that. And something I've really, it's really crystallized for me: so many administrators out there, you look at their jobs, you know, what are they? It's like okay, I'm the security expert, I'm the network certified person. You're really, your users here, you know, they are the beacons of knowledge, they are the center of data, is really what they are. You know, Splunk's a tool, they're super excited about the product, but it's data at the center of what Splunk does and therefore, you're helping them in just such a critical aspect of what is happening in the industry today. >> Yeah, the key aspects of the keynote, of my keynote, were we are moving to a world where data is the product that people care about so the whole object is how do you make things happen with data and the people that can get that done increasingly are becoming the most valuable players on the field, so what infrastructure, what tooling, what capability exists that allows people from all departments, you know, we're very heavy within IT and security, but increasingly HR departments, finance departments, marketing departments, sales departments, manufacturing departments will not be successful without a really competent group of folks that understand how to make things happen with data and our job is to lower that bar so you don't have to go to Carnegie Mellon for four years and get a Masters in Computer Science and Data Science to be able to be that most valuable person on the field. >> I want to take a moment, I want to explain why I'm so bullish on Splunk. We had a conversation with Susan St. Ledger yesterday. Digital transformation is all about data. >> Yup. >> And you guys are all about data, there's the cliche which is "data is the new oil" and we've observed, well not really. I could put oil in my car, I can put oil in my house, I can't put it in both places, but data? I can use that same data in a lot of different use cases and that's exactly what you guys are doing now as you expand into line of business -- >> Yup. >> With Splunk Next. >> Yup. >> So you've announced that, you showed some cool demos today. I'd like you to talk about how you're going from your core peeps, the IT ops guys and the sec ops guys, and how, what your plan is to go to lines of business. More than just putting the data out there, you've come up with some new products that make it simpler, like business work flows, but what else are you doing from a go to market standpoint and a partnership standpoint, how do you see that playing out? >> Yeah, I think that the innovation on product, there are three key pillars that we're focusing on. Access data, any type of data, anywhere it lives. Make sure that we're driving actionable outcomes with that data, and acquisitions like Phantom and VictorOps have been a key pillar of that, but there's other things we're doing. And then, expand the capability of finding those outcomes to a much broader audience by lowering the bar. So the three key themes across the portfolio. But all of those are in service of the developers at a customer site, the developers in the ecosystem, to make it easier for them to actually craft a set of solutions that help a retailer, help a discrete manufacturer, help a hospital actually make things happen with data. 'Cause you could certainly start with a platform and build something specific for yourself but it's much easier if you start with a solution. And a lot of the emphasis we've been putting over the past two to three years is how do we up that platform game. And the many, many, 20 different product announcements that we rolled at this .conf and one of them that I'm also very excited about is our developer cloud where we've really enhanced the API layer that interacts with the different services that the entire Splunk portfolio represents. Not just the search and index pieces that people are familiar with but everything from orchestration to role based access to different types of visualization so a very broad API layer that's a well-mannered, restful set of APIs that allows third parties to much more crisply develop, excuse me, applications to compliment the 1800 apps that are already part of our Splunk base and right behind me is a developer pavilion where we've got the first hand full of early adopter OEM partners that are building their first sets of apps on top of that API framework. >> Dozens of them, it's actually worth walking around to see. Now, so that developer cloud is a lever, those developers are a lever for you to get into lines of business and build those relationships through the software, really, and through the apps. Same thing for IOT. >> Yup. >> Industrial IOT. Now, we've observed, and a lot of the IT companies that we see are trying to take a top down approach into IOT and we don't think it's going to work. It's, we talk about process engineers, it's operations technology people, they speak a different language. It's not going to be a top down, here, IT. >> A very different audience. >> It's going to be a bottoms up set of standards coming from the OT world. The brilliance of what you guys have, it's the data, you know, it's data coming off machines, data, you don't care. And so, you're in a good position to do a bottoms up in IOT and we heard some of that today. Now, there are some challenges. A lot of that data is still analog, okay, you can't really control that. A lot of the devices aren't instrumented, they're not connected, you can't control that. But once they become instrumented and connected and that analog data gets digitized, you're in a really good position, but then you got to build out the ecosystem as well. >> Yup. >> So talk about how you're addressing some of those challenges in industrial IOT. >> Yup, man, it's a great subject 'cause I think that the trying to rely on standards is the wrong approach. The velocity across this digital landscape is so high and my view over the past 30 years, I think it's only accelerated now, is there's going to be more and more varieties of data with different formats than there's ever been, and we've seen it in the past five years. Just look at the variety of services on top of AWS, which didn't even exist ten years ago, but and they now have hundreds of services and there is no organizing principle across those services as far as data definition. So it's a very chaotic data landscape and I don't think there's any way to manage it other than to embrace the chaos and work a little bit more bottoms up, you know, grab this data, don't worry about cleansing it, don't worry about structuring it, just make sure you have access to it and then make sure that you've got tools like Splunk that allow you to play with the data and try and find the patterns and the value inside of that data, which is where I think we're very uniquely suited as a technology set. Helping the ecosystem come to that realization is a key aspect of what we're doing. We're trying to attack it the same way we attacked the IT security piece which is pick a handful of verticals and really focus on the players, both the marquis anchor tenants, the BMWs, the Siemens', the Deutsche Bahn railroads of the world, as customers. And through that, get access to the key influencers and consultants and advisors to those industries and start to get that virtuous circle of "I actually have more data than I think I have." Even though there's some analog machines, there's so many different ways to attach to the signal that those machines are emitting and it may not be bi-directionally addressable, but at least you can see what's happening within those machines without a full manufacturing floor rip and replace. And everyone is excited about doing that. The advisors to the industry are excited, the industry themselves are excited. We had BMW on stage who walked through how they're using Splunk to help on everything from product design all the way through to predictive maintenance and feedback on the quality of the cars that they're rolling out. We've all heard stories that there's more lines of code in the Ford F150 and these other vehicles than there is within Facebook right now, so we all are dealing with rolling and sitting in building's and house's data centers. How do you make sure that you're able to pay attention what's happened within that data center? So I think that that is as big or bigger of an opportunity than what we've done with IT and security, it just has its own pace of understanding and adoption. >> Carnival Cruise Line, another one, Stu. We had those guys on today and they basically look, they have a lot of industrial equipment on those ships, so they're excited. >> Yeah, absolutely. Alright, so Doug, we started the beginning talking about the last couple years, how we measure Splunk has changed. Going to more subscription models, talk about how many customers you have. I look at developers, I look at IOT, whole different set of metrics. So if you look at Splunk Next, how do we measure you, going forward? What is success for your team and your customers going forward? >> Yeah, and the whole orientation around Splunk Next, as I'm sure Susan covered, it's not a product, it's a messaging framework. People are so used to Splunk being all about the collection of data within the index and searching in said index, and we're increasingly moving, we're complementing the index, the index is a incredibly unique piece of IP for us. But there's a lot of other modalities that can complement what that index does and Splunk Next represents all of our investments in next generation technologies that are helping in with everything from stream processing to distributed compute capability, next generation visualizations, et cetera. The metric that I care about over time is customer adoption and customer success. How many use cases are being deployed at different customers? How many companies, both customers and partners, are incorporating Splunk in what they do every day? You're getting OEM Splunk, making Splunk a backbone of their overall health and success. And ultimately that needs to translate into revenue, so revenue and bookings will always be a metric that we care about, but I think the leading indicators within theses different markets of rate of adoption of technology and, more importantly, the outcomes that they're driving as they adopt this technology, are going to be increasingly important. >> Yeah, I just have to tell you, when you talk about your customers not only excited, but it's a deeper partnership when you talk to insurance company out of Toronto that, like, they're talking to the people that they insure about, should they be using Splunk and how do they do that. It just, a much deeper, and you know, deeper than a partnership model for your customers. >> It's one of the things I love about this conference, is it's, we were talking about earlier, it's hard to tell the customers from the employees, like, there's a, there's a, this whole belief and purpose that everybody shares, which I adore about being here. But when you look at a sea of data, we've thought traditionally looked at the data we manufacture, typically data that's historic and at rest from our ERP systems. This next wave is certainly all the data that's happening within our organizations but increasingly it's all the data that's available in the world at large. And whether it's insurance or automotive or oil and gas, the services that I'm going to have to deliver to customers require me to farm data outside of my walls, data inside my walls, combine those two, to come up with unique value added services for my customers. So it's great to hear that, that our customers are on that journey 'cause that's where we all need to go to be successful. >> And there's a definitely alignment there. Doug, I know you're super busy, we got to go. Thanks so much for coming on theCUBE. Give you the last word, .conf 18 takeaways. >> (laughs) Unbelievable excitement and enthusiasm. A huge array of products that, I think, broaden the aperture of what Splunk does so dramatically that people are really trying to digest, "What should, how should I be thinking about Splunk moving forward?" And I'm, we started a whole series of transformations three years ago, and I'm really excited that they're all starting to land and I can't wait for the slow realization of the impact that our customers are counting on us to provide and that we'll increasingly be known for across the data landscape. >> Well and the landscape is messy and, as you said, the messiest part of that landscape is the data landscape. You guys are helping organize that, curate it. And hopefully we're helping curate some of the, from some of the noise and distracting to the signal to you on theCUBE. Doug, thanks so much for coming on theCUBE, great to see you again. >> Thank you Dave, thank you Stu, you guys do a great job. >> Thanks, we appreciate that. >> Thanks for being here with us. >> Alright, keep it right there, buddy. We'll be back with our next guest from .conf 18 from Orlando, we'll be right back. (digital music)

>> From San Francisco, it's theCUBE. Covering, Girls in Tech Catalyst Conference. Brought to you by, Girls in Tech. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisico at the Girls in Tech Catalyst Conference 2018. About 700 people, two day conference, single track, really a lot of stories about people's journey. Senior executive women, how they got to where they were, and advice for kind of younger getting started execs, mid tier execs. Mainly women, a bus load of kids they just brought in, and a couple of men. So, we're excited to have one of them men, he just got off of the stage. It's Charlie Bell, Senior Vice President from AWS. Charlie welcome. >> Thanks for having me here. >> So, you just participated in a really interesting event. You were interviewed by your recently graduated daughter. >> Yes. >> She's entering the tech field. >> Yes. >> So, what did she ask you? It's just interesting to get her perspective. Just graduated from Carnegie Mellon, Nikki said. >> Yeah. >> And is getting ready to start her first job at LinkedIn. What is she thinking now? >> Actually, into it. >> Excuse me, into it. As she's looking forward at the beginning of this journey. >> Yeah, I mean she was asking me the kind of questions that you know that anyone who's getting started, or early in their career might ask. It was questions like, how did you decide when you were going to change jobs. What advice would you give to somebody who wants to be a leader? How do you recognize leaders? It was pretty interesting. Caroline is really smart, curious, very similar probably to most of the kids graduating. And many of the folks early in their career. So, I thought a lot of her questions probably relevant to almost anybody. >> Well, I guess she's already, she going to start her first job in a couple of weeks and she's already asking the leadership questions. >> Yeah, yeah. >> So, clearly you've got to be a proud dad for that. She's ready to start movin' up the line. >> Yeah, yeah. >> And I'm curious was she interested in STEM subjects before college? Or, well she went to Carnegie Mellon so you wouldn't go there if you didn't have an interest. >> Yeah, she no, was always interested in math. So, she studied math, ya know that was her best subject in high school. And she did a few science fair projects. When she went to Carnegie Mellon as a math major. But, she actually has so many, ya know? Much of the subject here is about the crooked path we take. And we've all had those. As she got to college she realized well math actually wasn't the thing she wanted to do. And then she thought well, what I really, really love the statistics part of it. And then she realized well, wait a minute, there's this whole new thing, machine learning, where you can take this knowledge of statistics and apply it to programming and computers, and everything else. She got very excited about it. And I've got to tell ya, there's no happier moment in a parent's life than when your child says their going to study machine learning. You know they will eat the rest of their life. >> That's very true. But, it's also even more important, what I thought you were going to say, is when your child finds something that they're really passionate about. >> Of course. >> Whether it's machine learning or whatever, that's, ya know, I've got three at home myself. So fun, when they find the thing that draws them in. So, I'm curious have you been to any of these events before? >> No, I haven't been to any of these. Actually, Sandy Carter, one of our Vice Presidents suggested a talk here would be interesting. And with Caroline interviewing me it was super interesting. I actually don't get out that much. You haven't talked me ever. But, I'm on the engineering side. I live inside the halls and we build stuff, and don't usually get out to talk to people. >> Yeah, so I'd love to get your impression on the event in general, but also some of the sessions. In terms of what was goin' on this morning. >> Oh I thought it was awesome. Amy's talk, ya know, I resinated with a lot of that. I thought her advice on some of the tips for the folks in the room was spot on. Many of them are, we have this thing at Amazon we call leadership principles. Many of them are just totally aligned with the Amazon leadership principles, the way we think. So, yeah these talks have been both interesting and inspiring. >> Yeah, so much talk about culture and it's funny you talked about the leadership principles and ya know we're a huge Andy Jassy fan. We've had him on a lot. But, I think one of my favorite times is he sat down on a fireside chat. Saw his in San Francisco a couple of years ago and really exposed to the audience some of the philosophies that operate behind Amazon. And how people make decisions and I think you brought it up here that it's okay to change your mind, if you're leader when you get new data. His whole thing about the power point and the six page narrative, and the way you guys execute in clearly such a well oiled machine, in terms of the way especially at AWS, you guys just keep rolling, and rolling, and rolling out new features, features, features. A lot of great lessons I think, in that Amazon culture. But, here all we keep hearing about is culture, culture, culture, culture, culture. So, you livin' it everyday. >> Yeah, well it's a gift that keeps on giving. I mean if the company has a good culture it's how everybody that comes in, how everybody pulls at the same oars, and it's really the fabric of a long term business. Andy said it many times, we all want a business that outlasts us. And the way you create that is through culture. >> Right, right, and just in the manacle focus on customer which I think is such a unique arduous trait, and Amazon trait. And I think that's like my favorite part about the new grocery store in Seattle. The fact that it was optimizing a process that nobody in the grocery store business probably ever really thought about very much. Which is i don't like to stand in line. So, to come at it, really from a customer perspective as apposed to a product perspective or competitive perspective, really I think is a big piece of the engine that just keep AWS just rollin' along. >> Yep, working from the customer backwards, it's the only way to live. >> With the press release before you make a new product, and it just goes on, and on, and on. >> Alright, so Charlie give me the last word before we let you go. What are you workin' on, what's exciting, what ya people will be keepin' an eye out for as you're whisked away in the halls, not coming out? What can we, what are some berries for the balance of 2018? >> Well, we still, as much as we've done so far, we still got a lot coming in machine learning. And across the board, I mean for me the exciting thing at AWS is our customers, we have such a broad set of customers right now with so many different needs. That we hear so many new things and it just inspires us to do brand new businesses and brand new services. So, it's just a lot of areas. Analytics, compute, storage, everything else like, there's a lot comin'. So, reinventing should be every bit as exciting as it was last year. >> Just got to find more space for ya, Vegas got to get a little bit bigger. And we'll be in DC next week for Summit Public Sector with Teresa Carlson and the crew also puts on a great event. >> Oh Teresa's so much fun. >> Alright, well thanks for takin' a few minutes of your day, we really appreciate it. And congrats to your daughter. >> Aw thank you, yes. >> Alright, thanks for watching. I'm Jeff Frick, we're at Girls in Tech Catalyst. Thanks for watching. (upbeat music)

>> Hi, I'm Peter Burris and welcome to another CUBE conversation. Today we're going to talk about a really interesting topic. At least it's interesting to me. And that is, if we go back, and the old adage that when you automate bad process or bad business, you just get more bad business at scale. And, when we think about customer service over the last number of years or customer engagement over the last number of years, in many respects we've done a great job of automating really bad practices. And all that has led to is an increased frustration amongst consumers who are trying to utilize an engagement form if they want, more digital engagement, but end up being even more frustrated because it still takes the same amount of time and it still has the same failure rates. And to discuss that today, we've got Mahesh Ram, who's the founding partner of Solvvy, to talk a bit about some of these transformations that are taking place in terms of how digital engagement's going to change the way that businesses interact with consumers. Mahesh, welcome to theCUBE. >> Oh, it's great to be here. I'm a fan of theCube and honored to be here. So, Mahesh, let's start. Tell us a little bit about yourself and tell us a little bit about Solvvy. Sure, my background is in technology and I've built two successful start-ups in the past. The last one was a company that was acquired by Pearson in 2012, focused on automation for non-native English speakers. But my entire career has been spent really thinking about ways in which we can use technology to make people's lives better and improve existing workflows and processes. And so, it's why Solvvy attracted me, why it's so exciting, and I think that this is the most interesting thing I've ever done in my career, so I'm excited about that. >> Now Solvvy has a pretty decent reputation as being a thought we are in this domain of not just cutting the cost of engagement, but actually improving the quality of engagement. How does it do that? >> I think it's a great question. It starts with the mission of the company, I think. That's the easiest way to say it. Our mission is to enable every interaction between consumers and business to be effortless. Anywhere, any time, and any channel. So if you start with that mission, you really start to focus in on what's most important. What's most important is to deliver that amazing experience for that end user or that consumer, and at the same time, drive down the operational cost for the business, i.e. improve their efficiency. And so our vision for the company, is to take our intelligent AI and machine learning automation technology which is world-class and is better than anything else on the market and apply it to deliver on a vision which is we want these interactions between the consumer and the business to be successfully completed in five minutes or less. >> Five minutes? >> Yes, and today it's measure in hours, eight hours, 12 hours, 24 hours. That's the vision and we're well on the way to accomplishing that. >> Alright, so as a thought leader, give us an example of how business is doing that, and then we'll get into some of the technology questions. But, first off, what is the competitive advantage of being able to complete a client engagement under five minutes versus eight hours? >> Well, first of all, I think, again, if we put the end-user, the consumer, at the focal point, we're talking about a fundamental change in what they expect from business. They expect immediacy, they expect accuracy, they want you to respect their time. In fact, I think some of the latest analyst reports says that valuing consumer's time is the single biggest driver to brand loyalty. So if you've got that situation, you've got an obligation to the consumer to deliver what they want. Well, now put yourself in the shoes of the consumer, which we all are. I'm a consumer, I come to a business, I'm asking you a question about a product, a service, a defect, anything, an order that's missing. I expect to get an answer very quickly because my time is precious and I know that someone like me has asked that question in the past. Why has it not been possible in the past for me as a consumer to get that answer right away, leveraging the expertise that has already happened in an enterprise? And when I do that, when Solvvy is able to enable that for the business, there's multiple benefits. The consumer is happier, their CSAT goes up, their customer satisfaction goes way up. Their time is respected, they get their answer in a minute or less, as opposed to hours. The business is happy because there's no ticket created, there's no need for a human agent to go back and forth with you, ask you a bunch of things, and maybe come back to you six hours later and now you're upset. Maybe you switched brands in the meanwhile because you're so angry of having to wait. So, the benefit is, I sometimes say to our customers, "It's the magic X", the CSAT goes up, and the cost goes down and that's never been possible before Solvvy. >> How does it work? >> How it works is very simple. The first thing we do is we engage with the business. So, the business is our customer, right? They buy the product or they buy our SATS platform. It's a SATS platform built on AI and machine learning technology that was developed by my two co-founders during their PHD work at Carnegie Mellon. So, at it's core, is the ability to understand natural language expressions of issues, by the end user, by the consumer. So typically people give us their life story, but they're asking for a refund. The ability to parcel that in that conversation and say I think you want a refund, let me help you get that is a very powerful piece of IP. So we go to companies and we say, "Just tell us where all of your knowledge assets are." You don't have to touch it, don't create anything new, don't build a new silo because they already have the silo, and we simply go out and index it all, learn from it, and start building a knowledge graph for that business. It's specific to how that business handles resolutions, but it also learns how customers have asked questions in the past, and how agents have answered it. So again, your best expertise is captured and used in that knowledge graph. We then say, "In less than an hour, "in one line of java scripts, here's a model "you can put in front of your consumer." You can put it anywhere you want, and it says when you need help, click on it, pops up, on mobile you can speak the question, and tells the consumer, "Just tell me what your issue is." It understands the intension of that question or the issue, and then goes in the knowledge graph, and says, "Hmmm, can I find an answer "in this knowledge graph that can help you help yourself?" And if so, it matches it. And it's actually giving you a specific resolution. It's not making you wade through pages of material. It's saying, "Here's the three steps you need to do "to reset your account." Now that is instant and immediate for the consumer. They don't have to hunt, they don't have to search. And it says, "Have I helped you?" And we're putting the power in the hands in the consumer. We're saying, "We don't want it to be false fiction." We're saying, you the consumer can say, "Nope, this didin't help me," and now the company can then guide you to the right flow. They can get you on a chat, if you're a V.I.P. user, maybe they get you on a phone call, whatever it might be. But, by putting the consumer at the center, by delivering real value to them, we've accomplished both sides, right? CSATS higher, the cost goes down, because we are actually self-serving anywhere from 15 to 40% of the tickets or issues that used to cost the business money, being self-served now, and so that's a pretty miraculous transformation for the business and for the consumer. >> Well, in today's world, attention is everything. Every, as you said, every experience, every engagement has to be a source of value to the customer. And so, not only do you get a better customer, but you presumably also get a richer set of interactions because the customer now believes that the system actually is helpful, is useful. Does that data then go back into the system, so that it becomes even knowledgeable about the nature of the problems, the nature of the resolutions, anticipatory about how to improve things, and maybe product people can get visibility in this stuff? Is that kind of where all this goes? >> It's a very organic system and it learns constantly. It think that's the really powerful thing about it. So, it learns many things. So it learns when you ask me questions. It learns if I have not given you a good answer. It actually learns from the negative. I still passed you to the agent because then it follows it all the way through and says, "How did the agent answer?" And, it learns from that interaction. And so because we know we can't self-serve every question that a consumer has, but we're getting better, better, and better. In fact, our self-service rates have doubled just in the last 12 months, because of the machine learning and the ability to learn. And we actually learn across all the businesses we do business with. We learn things for example that consumer review show more than a paragraph of text, they don't engage in self-service. We show bullets, they are much more likely to interact. Those are implicit learnings that system uses to more accurately to give you responses. But there's another flip side to this which is when we see 100% of the conversations between the consumer and your business, we're now able to go to business and give them categorical views of what's actually going on once their product or service is shipped to the consumer, which they've never had before. We're now able to say to them, "We think that payment "page that people are using to renew might be broken "because there seems to be a lot "more issues associated with that." Now that's something that the engineer who built that page may not know, or if the person said, "That's broken," they'd say, "How do you know? "Show me the data." And now you can actually go with a data driven model and say, "We can tell you. "This is 14% of the issues this week, "and two weeks ago, it was two percent. "Can you tell me what's changed?" Or you can put a dollar value on it. "This product seems to be defective "and it's costing us money "because we keep having to do returns. "Here's the number of situations where that's happened "in the last week, it's costing us "two million dollars a year, fix it." That's the kind of incite that the vps of customer experience or customer support have had to spend hundreds of hours to try to massage and get, and it doesn't give them a seat at the table with the strategy with product and marketing. >> But every company has been talking about the need to build their community, where basically a community is defined by folks who have something in common and are taking common actions. But one of the challenges has been, is how do I provide value so that I get that type of interaction? Let me ask you a question. Are we ultimately suggesting, we all seem to be getting to a point where the quality of engagement is such, and while it keeps costs low, that it might actually catalyze even greater engagement with the customer base so that you learn not just initially, you not only learn something about a product, or for example, you might actually learn things about how to facilitate adoption, because customers are willing to engage more often and more deeply as a consequence of a good experience in using Solvvy related type technologies? >> It is the opportunity to use that customer engagement when they're contacting your business about an issue or problem, the opportunity is first I have to take care of your issue. You won't listen to me if I don't take care of your issue well, but if I do that, I have an enormous opportunity to educate you. How can you do better with the product or service I sold you" Perhaps you need something that's on top of that. Maybe you're a free user and by subscribing to the premium product, you'd get all the benefits that you're frustrated about. And maybe that's an time to give you an offer. So, I think that notion of personalized recommendation is something that is actually never been possible before with the old systems. The idea was that support was kind of a backwater in many ways, which it should never have been. And in fact, the leading brands like Zappos realized quickly that by winning on that basis, you could actually dominate the market. But, it was often the case that the people in support felt like goal keepers. Just keep the issues away, but in fact, now in an integrated world, it's very difficult with subscription based businesses for example, to know when you're buying and when you're asking for support. It's subscription service, I could cancel at any time. So now I'm engaging with your brand. I'm asking a question, "Hey how do I get "more of x,y,z shipped to my house?" It's an enormous opportunity to not only answer my question, but then suggest things, recommend things, play books, so if you think about that experience, how would I enhance the consumer experience, that interactive conversational flow is the perfectplace to do it. >> I would think it would also allow you to envision other types of engagement, because as long as the consumer finds it valuable, to have that conversation, then they'll be willing to enter into that conversation. Well, so let me step back, where does this all go? Because we've been talking about being able to do this for a number of years, and as I said in the preamble, in many respects, all we did was digitize bad process, but now we're talking about bringing technology to bear and dramatically improving the process. Five minute resolution, pretty good. As a consumer, I'd like that, so where does this go? What's the limit of utilizing these technologies to incorporate or enhance engagement? >> So, let me illustrate with an example that I think is very compelling of the power of how this is going to change our world. So, one of our customers is Eero, the smart wifi system. You're probably familiar with it. One of the most innovative products on the planet. Now, we've been working with Eero for well over a year, and they just published a case study of what we've accomplished with them. So, we have self-served 45% of the issues that would have come into them, that have conversations that come into them regarding issues, and that's a fantastic number. They had never seen anything close to it. And that's a great outcome for the business and the consumer. Under one minute is the average time for resolution for those 45%. Imagine again, how much time I've saved you, me, all of us as consumers of Eero. But the better story that I like is two weeks ago, we got a call from the CEO of one of the leading mid-western electronics distributors in the world, and he had said, "I'm going to have my support team, "customer experience team contact you guys, "because I was at home, I bought an Eero smart wifi system, "I went home and tried to install it, and I had trouble. "And I went on, and Solvvy gave me the exact steps "it took to solve the issue, "and I never had to contact them, "and I was able to get the wifi up and running in minutes, "and I was on my way. "And I'm delighted with my Eero system, "and it was because of this interface." And he said, "I think my company should be using it too." And, that was one of many, many catalytic events for us, that realized, wow, we're touching over 200 million consumers with our service. We're reaching all the way out, and we're extending these brand's promise into the consumers' homes, into their devices. _ Two hundred million? >> Two hundred million. >> So that's literally 10% of the population that's online. >> If you're talking about the world leading brands, so we're working with the leading brands that are reaching these people, so by extension Solvvy is as well. And so, you're talking about companies, leading gaming companies, on-demand companies, consumer electronics. These are all companies that self-service automation. And it's intelligent automation, right? It doesn't require a lot of work from the business. As I said, we implement in less than an hour. With one line of java script, we've developed very powerful, unsupervised machine learning models, that can just take all that transcript date from all the past conversations that consumers have had with your business, automatically learn the best stuff from it, and then be able to show the users the right issues. So the customer journey is where we're so focused, right? Because the customer journey is opportunity for the brand to really create a market-leading position and we're enhancing that conversation. >> Fantastic. >> Mahesh Ram, founding CEO of Solvvy, thank you very much for coming on theCube. It's been a great a great conversation about the evolution of customer service, and where it goes. >> It's my pleasure, an honor to be on theCUBE. >> So once again, this is Peter Burris, this has been a CUBE conversation, until next time.

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE covering autonomous vehicles. Brought to you by Western Digital. (upbeat electronic music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Autotech Council Autonomous Vehicles event here at Western Digital. It's part of our ongoing work that we're doing with Western Digital about #datamakespossible and all the really innovative and interesting things that are going on that at the end of the day, there's some data that's driving it all and this is a really crazy and interesting space. So we're excited for our next guest. He's Mark DeSantis. He's the CEO of RoadBotics. Mark, great to see you. >> Welcome. >> Thanks, thanks for having me, Jeff. >> So just to give the quick overview of what is RoadBotics all about? >> Sure, we use a simple cellphone as a data collection device. You put that in the windshield, you drive, it records all the video and all that video gets uploaded to the Cloud and we assess the road's surface meter by meter. Our customers would be Public Works departments at the little town to a big city or even a state, and we apply the same principles that a pavement engineer would apply when they look at a piece of pavement. Looking for all the different subtle little features so that they can get, first of all, get an assessment of the road and then they can do capital planning and fix those roads and do a lot of things that they can't do right now. >> So I think the economics of roads and condition of roads, roads in general, right? We don't think about them much until they're closed, they're being fixed, they're broken up, there's a pothole. >> Mark: Yeah. >> But it's really a complex system and a really high value system that needs ongoing maintenance. >> That's right. I always use the example of the Romans who built a 50,000 mile road network across Europe, the Middle East, and Africa. Some of those roads, like the Appian Way, are still used today. They were very good road builders and they understand the importance of roads. Regrettably, we take our roads for granted. The American Society for Civil Engineers annually rates infrastructure and we're rated about 28% of our nation's 11 million lane miles as poor. Unfortunately, that's- >> Jeff: 28%? >> 28%. And that really means that you need to invest, we'll need to invest at least a million to two million bucks a mile to get those roads back into shape. So we take our roads for granted. I'm enjoying this conference and there's one point that I want to make that I think is very poignant, is the AV revolution will also require a revolution in the maintenance and sustenance of our road network, not just the United States but everywhere in the world. >> So it's interesting, and doing some research before we got together in terms of the active maintenance that's not only required to keep a road in good shape but if you keep the active maintenance in position, those roads will last a very long time. And you made an interesting comment that now the autonomous vehicles, it's actually more important for those vehicles, not only for jolting the electronics around that they're carrying, but also for everything to work the way it's supposed to work according to the algorithms. >> Andrew Ang, who's an eminent computer scientist, machine learning, we were spun out of Carnegie Mellon and he was a graduate of that program, recognized early on that the quality of the roads made all the difference in the world for these vehicles to move around. We, in turn, were spun out of Carnegie Mellon, out of that same group of AV researchers, and in fact, the impetus for the technology was to be able to use the sensing technology that allows a vehicle to move around to assess the quality of roads. And it's road inspection, really, is an important part of road maintenance. The ability to go look at an asset. Interestingly, it's an asset whose challenge is not the fact that it can't be inspected, it's the sheer size of the asset. When you're talking about a small town that might have a 60-mile road network, most and the vast majority of inspection is visual inspection. That means somebody in a car riding very slowly looking down and they'll do that for tens, thousands, hundreds of thousands of miles, very hard to do. Our system makes all that very, much more efficient. The interesting thing about autonomous vehicles is they'll have the capacity to use that data to do that very assessment. So for our company, we ultimately see us embedded in the vehicle itself, but for the time being, cellphones work fine. >> Right. So I'm just curious, what are some of those leading indicator data points? Because obviously we know the pothole. >> Mark: Yeah. >> By then things have gone too far but what are some of the subtle things that maybe I might see but I'm not really looking at? (laughs) >> Well, I think I've changed you right now and you don't know it. You're never going to look at a road the same- >> Oh, I told you, I told you. (laughs) >> After you hear me talk for the next three minutes. I don't look at roads the same and I'm not a civil engineer nor am I a pavement engineer, but as the CEO of this company I had to learn a lot about those two disciplines. And in fact, when you look at a piece of asphalt, you're actually looking for things like alligator cracks, which sort of looks like the back of an alligator's skin. Block cracks, edge cracks, rutting, a whole bunch of things that pavement engineers, frankly, and there is a discipline called pavement engineering, where they look for. And those features determine the state of that road and also dictate what repairs will be done. Concrete pavement has a similar set of characteristics. So what we're looking for when we look at a road is, I always say that, people say, "Well, you're the pothole company." If all you see are potholes, you don't have a business. And the reason is, potholes are at the end of a long process of degradation. So when you see a pothole, there are two problems. One is, you can certain blow out a tire or break an axle on that pothole but also it's indicative of a deeper problem which means the surface of the road has been penetrated which means you to dig up that road and replace it. So if you can see features that are predictive of a road that's just about to go bad, make small fixes, you can extend the useful life of that asset indefinitely. >> Right. So before I let you go, unfortunately, we're just short on time. >> Mark: Yeah. >> I would love to learn about roads. I told you, I skateboard so I pay a lot of attention to smooth roads. >> Mark: (laughs) And you'll pay even more now. >> Now I'll pay even more and call the city. (chuckles) But I want to pivot off what happened at Carnegie Mellon and obviously academic institutions are a huge part of this revolution. >> Yeah, yeah. >> There's a lot of work going on. We're close to Stanford and Berkeley here. Talk a little bit about what happens... It's happening at Carnegie Mellon and I think specifically you came out of the Robotics Institute in something called the Traffic21 project. >> Yeah, Traffic21 is funded by some local private interests who believed that the various technologies that are, really, CMU is known for around computer science, robots, engineering, could be instrumental in bringing about this AV revolution. And as a consequence of that, they developed a program early on to try to bring these technologies together. Uber came along and literally hired 27 of those researchers. Argo, now... Argo, Ford's autonomous vehicle now, is big in Pittsburgh as well. On any given day, by my estimate, it's not an official estimate here, there are about 400 autonomous vehicles, Ford and Uber vehicles, on Pittsburgh's streets every single day. It's an eerie experience being driven around by a completely autonomous Uber vehicle, believe me. >> I've been in a couple. It's interesting and we did a thing with a company called Phantom. They're the ones that step if your Uber gets stuck. >> Oh, yeah. >> Which is interesting. (laughs) So really interesting times and exciting and I will go and pay closer attention for the alligator patterns (laughs) on my route home tonight. (laughs) All right, Mark, thanks for stopping by and sharing the insight. >> Thanks again, Jeff. Appreciate you having me. >> All right, he's Mark, I'm Jeff. You're watching theCUBE from the Autotech Council Autonomous Vehicles event in Milpitas, California. Thanks for watching. (upbeat electronic music)