 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hi everybody, this is Dave Vellante of theCUBE and welcome back to my CXO series. I've been running this, there's really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Cree and body is here as the CEO and founder of H2O. Cree, it's great to see you again. Thanks for coming on. Thank you for having us. Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword. Flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice. It's about survival. Your thoughts as to what you're seeing in the marketplace. Thanks for having us. I think first of all, kudos to the frontline health workers who have been relentlessly saving lives across the country and the world. And whatever we're doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart, I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital. And as a result, we're seeing tremendous transformation across our customers. And a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. So think about doctors and diagnoses, machines in some cases are helping doctors make diagnoses. They're sometimes making even better diagnosis, at least in forming. There's been a lot of talk about the models, you know, how the, I know you've been working with a lot of healthcare organizations. You may probably familiar with that, you know, the medium posts, the hammer and the dance and if people criticize the models, of course they're just models, right? You iterate models and machine intelligence can help us improve. So in this, you know, you talk about BC and post-C, how have you seen the data and machine intelligence informing the models, improving sort of what we know about this pandemic? I mean, it's changed literally daily. What are you seeing? Yeah, and I think it started with Wuhan and we saw the best application of AI trying to trace literally from Alipay to WeChat, tracked down the first folks who are spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. Supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID. In the prime center is hospital staffing. How many ventilators? It's the first few weeks of the COVID crisis as it evolved in the US. We're busy predicting working with some of the local healthcare communities to predict how many hospital staffing in hospitals will work, how many PPE and ventilators will be needed and so on so forth. But that quickly, and when the peak surge will be just with the beginning problems and many of our customers are beginning to do these models and iterate and improve and kind of educate the community to do practice social distancing. And that led to a lot of flattening the curve and you're talking about flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level. Now that we have somewhat brought an semblance of order to the reaction to COVID, I think what we are beginning to figure out is there going to be a second surge? What elective procedures that are postponed will be top of the mind for customers? And so this is the kind of things that hospitals are beginning to plan out for the second half of the year. And as businesses try to open up, certain things were highly correlated to surge in cases such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods. And so everyone essentially started working from home. And so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have. And so a lot of interesting, as one side you saw airlines go away, the other side you saw the likes of Zoom and online and video take off. So you're kind of seeing a real divide in the digital divide and that's happening. And AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. Yeah, and obviously these things, they get partisan, it gets political. I mean, our job as an industry is to report your job is to help people understand. I mean, let the data inform and then let public policy fight it out. So who are some of the people that you're working with that as a result of COVID-19, what's some of the work that H2O has done? I want to better understand what role you're playing. So one of the things we are kind of privileged as a company to come into the crisis with a strong balance and an ability to actually and the right kind of momentum behind the company in terms of great talent. And so we have 10% of the world's top data scientists in the form of Kaggle grandmasters in the company. And so we put most of them to work and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources. For example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy. And then we started looking at exodus from the cities by looking at mobility data that's publicly available, mobility data through the data exchanges. You're able to find which cities, which rural areas did the New Yorkers have left the city, which places did they go to? And what's it say, Californians when they left Los Angeles, which are the new places they have settled in. These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer. But if you go one step further, we started engaging with FEMA, we started engaging with the universities, like Imperial College London or Berkeley and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our driverless AI product as a recipe and made that accessible to our customers and testing to customers in healthcare who are trying to predict where the search is likely to come. But it's mostly about information, right? So the AI is at the end of it is all about intelligence and being prepared, predictive is all about being prepared. And then that's kind of what we did with the general, lots of typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise is that the simplest, very interpretable models are actually the most widely usable because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of backtesting that model against what really happened. Yeah, so I want to double down on that. So really two things I want to understand if you have visibility on it, it sounds like two, just in terms of the surge and the comeback, kind of what those models say based upon, we have some advanced information coming from the global market for sure, but it seems like every situation's different. What's the data telling you just in terms of, okay, we're coming into the spring and the summer months, maybe it'll calm down a little bit. Everybody says we fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time without understanding that we're still iterating every day? Well, I think, I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper-local response to COVID-19 is what we've seen. Santa Clara, which is the county I'm in, is different from San Francisco, right? So the beginning to see, like we saw in Brooklyn, it's very different, and Bronx very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about the US. You see the likes of Brazil, which we were worried about has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree. A large number of our user base there are back active. You can see the traffic patterns there. So two months after their last of the search cases, the business and economic activity is back and thriving. And so you can kind of estimate from that that this can be done, where we can actually contain the rise of active cases. And it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this. And they've done essentially masked everybody. And as a result, they're back thinking about opening offices later this month. So I think that's a very, very local response, hyper-local response. No one country and no one community has a symmetrical with other ones. And I think we have a unique situation where in the United States you have a very, very highly connected world, highly connected economy. And I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. Yeah, so you can't just take Norway and apply it, or South Korea and apply it. Every situation is different. And then I want to ask you about the economy in terms of, how much can AI actually, how can it work in this situation where you have, for example, okay, so the Fed, yes, it started doing asset buys back in 2008, but still very hard to predict. I mean, at the time of this interview, the stock market's up 900 points. It's been very, very difficult to predict that. Some event happens in the morning. Somebody, you know, Powell says something positive and it goes crazy. But just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data. Do you not, in order for AI to be reasonably accurate, how does it work? And how does, at what pace can you iterate and improve on the models? So I think that's exactly where I would say continuous modeling, it's sort of continuously learning containers. That's where the vision of the world is headed towards where data is meaning you build a model and then iterate, try it out and come back. That kind of rapid continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model into production once a year or once every quarter. I think what we're beginning to see is the kind of, where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of found. And so upskilling and trying to kind of bring back these jobs back, both into kind of the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airline slash hotel industries. So trying to now bring back the sense of confidence and we'll take a lot more kind of testing, a lot more masking, a lot more social empathy. I think some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for, we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of the same kind of thinking has to kind of pervade before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it similar to the 1918, where we had a second bump or a lot of little bumps and that's kind of where your doubly shaped pieces. But governments are trying very strong, very, very well. We're seeing stimulus dollars being pumped through banks. So some of the use cases we're looking for banks is which small, medium business, especially in unsecured lending, which business to lend to, and there's so many applications to that have come to banks across the world. It's not just in the U.S. And banks are caught up with the problem of which, what's going concern for this business to kind of, are they really accurate about the number of employees they are saying they have to then the next level problem around forbearance and mortgage outside of the things that are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers was far go, they have a question, which branch to open, right? Sort of that itself, it needs a different kind of modeling. So everything has become a very, highly good segmented models. And so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. We'll talk a little bit about your business. You have been on a mission to democratize AI since the beginning, open source, explain your business model, how you guys make money. And then I want to help people understand basic historical comparisons and current comparison. Yeah, that's great. I think the last time we spoke, probably at the Spark Summit, I think Dave, and we were talking about sparkling water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale. And that's been a tremendous brand for us. Over the last couple of years, we've essentially built a platform called Driverless AI, which is a licensed software and that automates machine learning models. We took the best practices of all these data scientists and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models, or the best recommendation engines. And so we started augmenting traditional data scientists with this automatic machine learning called AutoML, which essentially allows them to build models without necessarily having the same level of talent as these great tackle grandmasters. And so that has democratized the allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft, or Amazon, or some of these top tier AI houses, like Netflix and others. So what we've done is democratize not just the algorithms at the open source level, now we've made it easy for kind of rapid adoption of AI across every branch inside a large organization, also across small organizations which don't have the access to the same kind of talent. Now, third level, what we brought to market is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more licensed software. And to give you an idea, Dave, from a small standpoint, now majority of our software sales is coming from closed-source software. We've made that transition. We still make our open source widely accessible. We continue to improve it. A large chunk of the teams are improving and participating in building the communities. But I think from a business model standpoint, as of last year, 51% of our revenues are now coming from closed-source software and that change is continuing to grow. And this is the point I wanted to get to. So, the open-source model was Red Hat, the one company that succeeded wildly and it was put it out there open-source, come up with a service to maintain the software. You got to buy the subscription, okay, fine. And everybody thought that you were going to do that. They thought that's what Databricks was going to do and that changed. But I want to take two examples. Hortonworks, which kind of took the Red Hat model in cloud era, which has IP. And neither really lived up to the expectation. But now there seems to be sort of a new breed. I mentioned you guys, Databricks, there are others that seem to be working. You with your licensed software model, Databricks with a managed service. And so it's becoming clearer that there's got to be some level of IP that can be licensed in order to really thrive in the open-source community to be able to fund the committers that you have to put forth to open-source. I wonder if you could give me your thoughts on that narrative. So on driverless AI, which is the closed-source platform I mentioned, we opened up the layers above in open-source as recipes. So for example, different companies build their zip codes differently, right? So the domain-specific recipes, we put about 150 of them in open-source again on top of our driverless AI platform. And the idea there is that open-source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model. It allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SAS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community-building exercise as opposed to a business model, right? So that's kind of the heart of open-source is allowing that freedom for our end users and the customers to kind of innovate at the same level of that Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture as opposed to a consumer culture around software. Now, if you look at how the Red Hat model and the others have tried to replicate that, the difficult part there was if the product is very good, customers are self-sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies get pulled into a lot of services which is a reasonably difficult business to scale. So I think what we chose was instead a great product that builds a fantastic brand that makes AI. We were one of the first or second.ai domain and for us to see thousands of companies which are .ai and AI first and even more companies adopting AI and talking about AI as a major wave that was possible because of open source. If you had chosen closed source and many of our peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately is a fast and fast rise up to become unicorns. In that race, you're essentially sacrificed building a long ecosystem play and that's kind of what we chose to do and that took a little longer. Now if you think about the, how do you truly monetize open source? It takes a little longer and it's much more difficult sales machine to scale. Instead of our open source business actually is reasonably positive with our business because it makes more money than we spend on it. But trying to teach sales teams how to sell open source, that's a rate limiting step. And that's why we chose and also explaining to the investors how open source needs to be invested in as you go closer to the IPO markets. That's where we chose, let's go into licensed software model and scale that as a regular business. So I've said a few times, it's kind of ironic that this pandemic hit, this is we're entering a new decade. You know, we've kind of, we're exiting the era. I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what should we expect out of AI this decade? If those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I say just, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Should we expect more from AI this decade? Well, I mean, if you think about the decade of 2010, 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conversations are digital. And AI is eating software. I mean, a lot of cloud transitions are happening and they are now happening much faster rate, but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data-first, data-driven applications that use AI. Instead of rule-based software, you're beginning to see pattern recognition, AI-based software, and you're seeing that in space. And of course, that is just the tip of the iceberg. AI has been with us for a hundred years and it's going to be ahead of us another hundred years, right? So as you see, the discovery rate at which, it is really fundamentally a math movement. And in that math movement at the beginning of every century, it leads to a hundred years of phenomenal discovery. So AI is essentially making discoveries faster. AI is producing entertainment, AI is producing music, AI is producing choreography. We're seeing AI in every walk of life. AI summarization of Zoom meetings, you're beginning to see a lot of the AI-enabled ETF picking of stocks. Actually, if you're beginning to see, we reprise 20,000 bonds every 15 seconds using H2 AI, corporate bonds. And one of our customers is on the fastest growing stock. Mostly AI is powering a lot of these insights in a fast-changing world, which is globally connected. No one of us is able to combine all the multiple dimensions that are changing. And AI has that incredible opportunity to be a partner for a hospital looking at how the second half will look like to for physicians looking at what is the sentiment of what is the surge to expect to kind of what is the amount of money looking at the sentiment of the customers. AI is the ultimate money ball you can pay in business. And I think it's just showing its depth at this point. Yeah, I mean, I think you're right on. I mean, basically AI is going to invert every piece of software, every application or those tools going to have much use. Three, we got to go, but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. Stay safe and best of luck to you guys. Thank you guys, thank you so much. Welcome and thank you for watching everybody. This is Dave Vellante for the CXO series on theCUBE. We'll see you next time.