>>Hi, everybody. Welcome back to Caesars in Las Vegas. My name is Dave ante. We're here with the chairman and CEO of snowflake, Frank Luman. Good to see you again, Frank. Thanks for coming on. Yeah, >>You, you as well, Dave. Good to be with you. >>No, it's, it's awesome to be, obviously everybody's excited to be back. You mentioned that in your, in your keynote, the most amazing thing to me is the progression of what we're seeing here in the ecosystem and of your data cloud. Um, you wrote a book, the rise of the data cloud, and it was very cogent. You talked about network effects, but now you've executed on that. I call it the super cloud. You have AWS, you know, I use that term, AWS. You're building on top of that. And now you have customers building on top of your cloud. So there's these layers of value that's unique in the industry. Was this by design >>Or, well, you know, when you, uh, are a data clouding, you have data, people wanna do things, you know, with that data, they don't want to just, you know, run data operations, populate dashboards, you know, run reports pretty soon. They want to build applications and after they build applications, they wanna build businesses on it. So it goes on and on and on. So it, it drives your development to enable more and more functionality on that data cloud. Didn't start out that way. You know, we were very, very much focused on data operations, then it becomes application development and then it becomes, Hey, we're developing whole businesses on this platform. So similar to what happened to Facebook in many, in many ways, you know, >>There was some confusion I think, and there still is in the community of, particularly on wall street, about your quarter, your con the consumption model I loved on the earnings call. One of the analysts asked Mike, you know, do you ever consider going to a subscription model? And Mike got cut him off, then let finish. No, that would really defeat the purpose. Um, and so there's also a narrative around, well, maybe snowflake, consumption's easier to dial down. Maybe it's more discretionary, but I, I, I would say this, that if you're building apps on top of snowflake and you're actually monetizing, which is a big theme here, now, your revenue is aligned, you know, with those cloud costs. And so unless you're selling it for more, you know, than it costs more than, than you're selling it for, you're gonna dial that up. And that is the future of, I see this ecosystem in your company. Is that, is that fair? You buy that. >>Yeah, it, it is fair. Obviously the public cloud runs on a consumption model. So, you know, you start looking all the layers of the stack, um, you know, snowflake, you know, we have to be a consumption model because we run on top of other people's, uh, consumption models. Otherwise you don't have alignment. I mean, we have conversations, uh, with people that build on snowflake, um, you know, they have trouble, you know, with their financial model because they're not running a consumption model. So it's like square pack around hole. So we all have to align ourselves. So that's when they pay a dollar, you know, a portion goes to, let's say, AWS portion goes to the snowflake of that dollar. And the portion goes to whatever the uplift is, application value, data value, whatever it is to that goes on top of that. So the whole dollar, you know, gets allocated depending on whose value at it. Um, we're talking about. >>Yeah, but you sell value. Um, so you're not a SaaS company. Uh, at least I don't look at you that that way I I've always felt like the SAS pricing model is flawed because it's not aligned with customers. Right. If you, if you get stuck with orphaned licenses too bad, you know, pay us. >>Yeah. We're, we're, we're obviously a SaaS model in the sense that it is software as a service, but it's not a SaaS model in the sense that we don't sell use rights. Right. And that's the big difference. I mean, when you buy, you know, so many users from, you know, Salesforce and ServiceNow or whoever you have just purchased the right, you know, for so many users to use that software for this period of time, and the revenue gets recognized, you know, radically, you know, one month at a time, the same amount. Now we're not that different because we still do a contract the exact same way as SA vendor does it, but we don't recognize the revenue radically. We recognize the revenue based on the consumption, but over the term of the contract, we recognize the entire amount. It just is not neatly organized in these monthly buckets. >>You know? So what happens if they underspend one quarter, they have to catch up by the end of the, the term, is that how it works or is that a negotiation or it's >>The, the, the spending is a totally, totally separate from the consumption itself, you know, because you know how they pay for the contract. Let's say they do a three year contract. Um, you know, they, they will probably pay for that, you know, on an annual basis, you know, that three year contract. Um, but it's how they recognize their expenses for snowflake and how we recognize the revenue is based on what they actually consume. But it's not like you're on demand where you can just decide to not use it. And then I don't have any cost, but over the three year period, you know, all of that, you know, uh, needs to get consumed or they expire. And that's the same way with Amazon. If I don't consume what I buy from Amazon, I still gotta pay for it. You know, so, >>Well, you're right. Well, I guess you could buy by the drink, but it's way, way more expensive and nobody really correct. Does that, so, yep. Okay. Phase one, better simpler, you know, cloud enterprise data warehouse, phase two, you introduced the, the data cloud and, and now we're seeing the rise of the data cloud. What, what does phase three look like >>Now? Phase, phase three is all about applications. Um, and we've just learned, uh, you know, from the beginning that people were trying to do this, but we weren't instrumental at all to do it. So people would ODBC, you know, JDBC drivers just uses as database, right? So the entire application would happen outside, you know, snowflake, we're just a database. You connect to the database, you know, you read or right data, you know, you do data, data manipulations. And then the application, uh, processing all happens outside of snowflake. Now there's issues with that because we start to exfil trade data, meaning that we started to take data out of snowflake and, and put it, uh, in other places. Now there's risk for that. There's operational risk, there's governance, exposure, security issues, you know, all this kind of stuff. And the other problem is, you know, data gets Reed. >>It proliferates. And then, you know, data science tests are like, well, I, I need that data to stay in one place. That's the whole idea behind the data cloud. You know, we have very big infrastructure clouds. We have very big application clouds and then data, you know, sort of became the victim there and became more proliferated and more segment. And it's ever been. So all we do is just send data to the work all day. And we said, no, we're gonna enable the work to get to the data. And the data that stays in more in place, we don't have latency issue. We don't have data quality issues. We don't have lineage issues. So, you know, people have responded very, very well to the data cloud idea, like, yeah, you know, as an enterprise or an institution, you know, I'm the epicenter of my own data cloud because it's not just my own data. >>It's also my ecosystem. It's the people that I have data networking relationships with, you know, for example, you know, take, you know, uh, an investment bank, you know, in, in, in, in New York city, they send data to fidelity. They send data to BlackRock. They send data to, you know, bank of New York, all the regulatory clearing houses, all on and on and on, you know, every night they're running thousands, tens of thousands, you know, of jobs pushing that data, you know, out there. It just, and they they're all on snowflake already. So it doesn't have to be this way. Right. So, >>Yes. So I, I asked the guys before, you know, last week, Hey, what, what would you ask Frank? Now? You might remember you came on, uh, our program during COVID and I was asking you how you're dealing with it, turn off the news. And it was, that was cool. And I asked you at the time, you know, were you ever, you go on Preem and you said, look, I'll never say never, but it defeats the purpose. And you said, we're not gonna do a halfway house. Actually, you were more declarative. We're not doing a halfway house, one foot in one foot out. And then the guy said, well, what about that Dell deal? And that pure deal that you just did. And I, I think I know the answer, but I want to hear from you did a customer come to you and say, get you in the headlock and say, you gotta do this. >>Or it did happen that way. Uh, it, uh, it started with a conversation, um, you know, via with, uh, with Michael Dell. Um, it was supposed to be just a friendly chat, you know, Hey, how's it going? And I mean, obviously Dell is the owner of data, the main, or our first company, you know? Um, but it's, it, wasn't easy for, for Dell and snowflake to have a conversation because they're the epitome of the on-premise company and we're the epitome of a cloud company. And it's like, how, what do we have in common here? Right. What can we talk about? But, you know, Michael's a very smart, uh, engaging guy, you know, always looking for, for opportunity. And of course they decided we're gonna hook up our CTOs, our product teams and, you know, explore, you know, somebody's, uh, ideas and, you know, yeah. We had some, you know, starts and restarts and all of that because it's just naturally, you know, uh, not an easy thing to conceive of, but, you know, in the end it was like, you know what? >>It makes a lot of sense. You know, we can virtualize, you know, Dell object storage, you know, as if it's, you know, an S three storage, you know, from Amazon and then, you know, snowflake in its analytical processing. We'll just reference that data because to us, it just looks like a file that's sitting on, on S3. And we have, we have such a thing it's called an external table, right. That's, that's how we basically, it projects, you know, a snowflake, uh, semantic and structural model, you know, on an external object. And we process against it exactly the same way as if it was an internal, uh, table. So we just extended that, um, you know, with, um, with our storage partners, like Dell and pure storage, um, for it to happen, you know, across a network to an on-prem place. So it's very elegant and it, it, um, it becomes an, an enterprise architecture rather than just a cloud architecture. And I'm, I just don't know what will come of it. And, but I've already talked to customers who have to have data on premises just can't go anywhere because they process against it, you know, where it originates, but there are analytical processes that wanna reference attributes of that data. Well, this is what we'll do that. >>Yeah. I'm, it is interesting. I'm gonna ask Dell if I were them, I'd be talking to you about, Hey, I'm gonna try to separate compute from storage on prem and maybe do some of the, the work there. I don't even know if it's technically feasible. It's, I'll ask OI. But, um, but, but, but to me, that's an example of your extending your ecosystem. Um, so you're talking now about applications and that's an example of increasing your Tam. I don't know if you ever get to the edge, you know, we'll see, we're not quite quite there yet, but, um, but as you've said before, there's no lack of market for you. >>Yeah. I mean, obviously snowflake it it's, it's Genesis was reinventing database management in, in a cloud computing environment, which is so different from a, a machine environment or a cluster environment. So that's why, you know, we're, we're, we're not a, a fit for a machine centric, uh, environment sort of defeats the purpose of, you know, how we were built. We, we are truly a native solution. Most products, uh, in the clouds are actually not cloud native. You know, they, they originated the machine environments and you still see that, you know, almost everything you see in the cloud by the way is not cloud native, our generation of applications. They only run the cloud. They can only run the cloud. They are cloud native. They don't know anything else, >>You know? Yeah, you're right. A lot of companies would just wrap something in wrap their stack in Kubernetes and throw it into the cloud and say, we're in the cloud too. And you basically get, you just shifted. It >>Didn't make sense. Oh. They throw it in the container and run it. Right. Yeah. >>So, okay. That's cool. But what does that get you that doesn't change your operational model? Um, so coming back to software development and what you're doing in, in that regard, it seems one of the things we said about Supercloud is in order to have a Supercloud, you gotta have an ecosystem, you gotta have optionality. Hence you're doing things like Apache iceberg, you know, you said today, well, we're not sure where it's gonna go, but we offering options. Uh, but, but my, my question is, um, as it pertains to software developments specifically, how do you, so one of the things we said, sorry, I've lost my train there. One of the things we said is you have to have a super PAs in order to have a super cloud ecosystem, PAs layer. That's essentially what you've introduced here. Is it not a platform for our application development? >>Yeah. I mean, what happens today? I mean, how do you enable a developer, you know, on snowflake, without the developer, you know, reading the, the files out of snowflake, you know, processing, you know, against that data, wherever they are, and then putting the results set, God knows where, right. And that's what happens today. It's the wild west it's completely UN uncovered, right? And that's the reason why lots of enterprises will not allow Python anything anywhere near, you know, their enterprise data. We just know that, uh, we also know it from streamlet, um, or the acquisition, um, large acquisition that we made this year because they said, look, you know, we're, we have a lot of demand, you know, uh, in the Python community, but that's the wild west. That's not the enterprise grade high trust, uh, you know, corporate environment. They are strictly segregated, uh, today. >>Now do some, do these, do these things sometimes dribble up in the enterprise? Yes, they do. And it's actually intolerable the risk that enterprises, you know, take, you know, with things being UN uncovered. I mean the whole snowflake strategy and promises that you're in snowflake, it is a, an absolute enterprise grade environment experience. And it's really hard to do. It takes enormous investment. Uh, but that is what you buy from us. Just having Python is not particularly hard. You know, we can do that in a week. This has taken us years to get it to this level, you know, of, of, you know, governance, security and, and, you know, having all the risks around exfiltration and so on, really understood and dealt with. That's also why these things run in private previews and public previews for so long because we have to squeeze out, you know, everything that may not have been, you know, understood or foreseen, you know, >>So there are trade offs of, of going into this snowflake cloud, you get all this great functionality. Some people might think it's a walled garden. How, how would you respond to that? >>Yeah. And it's true when you have a, you know, a snowflake object, like a snowflake, uh, table only snowflake, you know, runs that table. And, um, you know, that, that is, you know, it's very high function. It's very sort of analogous to what apple did, you know, they have very high functioning, but you do have to accept the fact that it's, that it's not, uh, you know, other, other things in apple cannot, you know, get that these objects. So this is the reason why we introduce an open file format, you know, like, like iceberg, uh, because what iceberg effectively does is it allows any tool, uh, you know, to access that particular object. We do it in such a way that a lot of the functionality of snowflake, you know, will address the iceberg format, which is great because it's, you're gonna get much more function out of our, you know, iceberg implementation than you would get from iceberg on its own. So we do it in a very high value addeds, uh, you know, manner, but other tools can still access the same object in a read to write, uh, manner. So it, it really sort of delivers the original, uh, promise of the data lake, which is just like, Hey, I have all these objects tools come and go. I can use what I want. Um, so you get, you get the best of both worlds for the most part. >>Have you reminds me a little bit of VMware? I mean, VMware's a software mainframe, it's just better than >>Doing >>It on your own. Yep. Um, one of the other hallmarks of a cloud company, and you guys clearly are a cloud company is startups and innovation. Um, now of course you see that in, in the, in the ecosystem, uh, and maybe that's the answer to my question, but you guys are kind of whale hunters, <laugh> your customers are, tend to be bigger. Uh, is the, is the innovation now the extension of that, the ecosystem is that by design. >>Oh, um, you know, we have a enormous, uh, ISV following and, um, we're gonna have a whole separate conference like this, by the way, just for, yeah. >>For developers. I hope you guys will up there too. Yeah. Um, you know, the, the reason that, that the ISV strategy is very important for, you know, for, for, for, for many reasons, but, you know, ISVs are the people that are really going to unlock a lot of the value and a lot of the promise of data, right? Because you, you can never do that on your own. And the problem has been that for ISVs, it is so expensive and so difficult to build a product that can be used because the entire enterprise platform infrastructure needs to be built by somebody, you know, I mean, are you really gonna run infrastructure, database, operations, security, compliance, scalability, economics. How do you do that as a software company where really you only have your, your domain expertise that you want to deliver on a platform. You don't wanna do all these things. >>First of all, you don't know how to do it, how to do it well. Um, so it is much easier, much faster when there is already platform to actually build done in the world of clout that just doesn't, you know, exist. And then beyond that, you know, okay, fine building. It is sort of step one. Now I gotta sell it. I gotta market it. So how do I do that? Well, in the snowflake community, you have already market <laugh>, there's thousands and thousands of customers that are also on self lake. Okay. So their, their ability to consume that service that you just built, you know, they can search it, they can try it, they can test it and decide whether they want to consume it. And then, you know, we can monetize it. So all they have to do is cash the check. So the net effecti of it is we drastically lowered the barriers to entry into the world, you know, of software, you know, two men or two women in a dog, and a handful of files can build something that then can be sold, sort of to, for software developers. >>I wrote a piece 2012 after the first reinvent. And I, you know, and I, and I put a big gorilla on the front page and I said, how do you compete with Amazon gorilla? And then one of my answers was you build data ecosystems and you verticalize, and that's, that's what you're doing >>Here. Yeah. There certain verticals that are farther along than others, uh, obviously, but for example, in financial, uh, which is our largest vertical, I mean, the, the data ecosystem is really developing hardcore now. And that's, that's because they so rely on those relationships between all the big financial institutions and entities, regulatory, you know, clearing houses, investment bankers, uh, retail banks, all this kind of stuff. Um, so they're like, it becomes a no brainer. The network affects kick in so strongly because they're like, well, this is really the only way to do it. I mean, if you and I work in different companies and we do, and we want to create a secure, compliant data network and connection between us, I mean, it would take forever to get our lawyers to agree that yeah, it's okay. <laugh> right now, it's like a matter of minutes to set it up. If we're both on snowflake, >>It's like procurement, do they, do you have an MSA yeah. Check? And it just sail right through versus back and forth and endless negotiations >>Today. Data networking is becoming core ecosystem in the world of computing. You know, >>I mean, you talked about the network effects in rise of the data cloud and correct. Again, you know, you, weren't the first to come up with that notion, but you are applying it here. Um, I wanna switch topics a little bit. I, when I read your press releases, I laugh every time. Cause this says no HQ, Bozeman. And so where, where do you, I think I know where you land on, on hybrid work and remote work, but what are your thoughts on that? You, you see Elon the other day said you can't work for us unless you come to the office. Where, where do you stand? >>Yeah. Well, the, well, the, the first aspect is, uh, we really wanted to, uh, separate from the idea of a headquarters location, because I feel it's very antiquated. You know, we have many different hubs. There's not one place in the world where all the important people are and where we make all the important positions, that whole way of thinking, uh, you know, it is obsolete. I mean, I am where I need to be. And it it's many different places. It's not like I, I sit in this incredible place, you know, and that's, you know, that's where I sit and everybody comes to me. No, we are constantly moving around and we have engineering hubs. You know, we have your regional, uh, you know, headquarters for, for sales. Obviously we have in Malaysia, we have in Europe, you know? And, um, so I wanted to get rid of this headquarters designation. >>And, you know, the, the, the other issue obviously is that, you know, we were obviously in California, but you know, California is, is no longer, uh, the dominant place of where we are resident. I mean, 40% of our engineering people are now in be Washington. You know, we have hundreds of people in Poland where people, you know, we are gonna have very stressed location in Toronto. Um, yeah. Obviously our customers are, are everywhere, right? So this idea that, you know, everything is happening in, in one state is just, um, you know, not, not correct. So we wanted to go to no headquarters. Of course the SCC doesn't let you do that. Um, because they want, they want you to have a street address where the government can send you a mail and then it becomes, the question is, well, what's an acceptable location. Well, it has to be a place where the CEO and the CFO have residency by hooker, by crook. >>That happened to be in Bozeman Montana because Mike and I are both, it was not by design. We just did that because we were, uh, required to, you know, you know, comply with government, uh, requirements, which of course we do, but that's why it, it says what it says now on, on the topic of, you know, where did we work? Um, we are super situational about it. It's not like, Hey, um, you know, everybody in the office or, or everybody is remote, we're not categorical about it. Depends on the function, depends on the location. Um, but everybody is tethered to an office. Okay. In words, everybody has a relationship with an office. There's, there's almost nobody, there are a few exceptions of people that are completely remote. Uh, but you know, if you get hired on with snowflake, you will always have an office affiliation and you can be called into the office by your manager. But for purpose, you know, a meeting, a training, an event, you don't get called in just to hang out. And like, the office is no longer your home away from home. Right. And we're now into hotel, right? So you don't have a fixed place, you know? So >>You talked in your keynote a lot about last question. I let you go customer alignment, obviously a big deal. I have been watching, you know, we go to a lot of events, you'll see a technology company tell a story, you know, about their widget or whatever it was their box. And then you'll see an outcome and you look at it and you shake your head and say, well, that the difference between this and that is the square root of zero, right. When you talk about customer alignment today, we're talking about monetizing data. Um, so that's a whole different conversation. Um, and I, I wonder if you could sort of close on how that's different. Um, I mean, at ServiceNow, you transformed it. You know, I get that, you know, data, the domain was okay, tape, blow it out, but this is a, feels like a whole new vector or wave of growth. >>Yeah. You know, monetizing, uh, data becomes sort of a, you know, a byproduct of having a data cloud you all of a sudden, you know, become aware of the fact that, Hey, Hey, I have data and be that data might actually be quite valuable to parties. And then C you know, it's really easy to then, you know, uh, sell that and, and monetize that. Cause if it was hard, forget it, you know, I don't have time for it. Right. But if it's relatively, if it's compliant, it's relatively effortless, it's pure profit. Um, I just want to reference one attribute, two attributes of what you have, by the way, you know, uh, hedge funds have been into this sort of thing, you know, for a long time, because they procure data from hundreds and hundreds of sources, right. Because they're, they are the original data scientists. >>Um, but the, the bigger thing with data is that a lot of, you know, digital transformation is, is, is finally becoming real. You know, for years it was arm waving and conceptual and abstract, but it's becoming real. I mean, how do we, how do we run a supply chain? You know, how do we run, you know, healthcare, um, all these things are become are, and how do we run cyber security? They're being redefined as data problems and data challenges. And they have data solutions. So that's right. Data strategies are insanely important because, you know, if, if the solution is through data, then you need to have, you know, a data strategy, you know, and in our world, that means you have a data cloud and you have all the enablement that allows you to do that. But, you know, hospitals, you know, are, are saying, you know, data science is gonna have a bigger impact on healthcare than life science, you know, in the coming, whatever, you know, 10, 20 years, how do you enable that? >>Right. I, I have conversations with, with, with hospital executives are like, I got generations of data, you know, clinical diagnostic, demographic, genomic. And then I, I am envisioning these predictive outcomes over here. I wanna be able to predict, you know, once somebody's gonna get what disease and you know, what I have to do about it, um, how do I do that? <laugh> right. The day you go from, uh, you know, I have a lot of data too. I have these outcomes and then do me a miracle in the middle, in the middle of somewhere. Well, that's where we come in. We're gonna organize ourselves and then unpack thats, you know, and then we, we work, we through training models, you know, we can start delivering some of these insights, but the, the promise is extraordinary. We can change whole industries like pharma and, and, and healthcare. Um, you know, 30 effects of data, the economics will change. And you know, the societal outcomes, you know, um, quality of life disease, longevity of life is quite extraordinary. Supply chain management. That's all around us right >>Now. Well, there's a lot of, you know, high growth companies that were kind of COVID companies, valuations shot up. And now they're trying to figure out what to do. You've been pretty clear because of what you just talked about, the opportunities enormous. You're not slowing down, you're amping it up, you know, pun intended. So Frank Luman, thanks so much for coming on the cube. Really appreciate your time. >>My pleasure. >>All right. And thank you for watching. Keep it right there for more coverage from the snowflake summit, 2022, you're watching the cube.

>> Narrator: From around the globe, it's theCUBE with digital coverage of Exascale day, made possible by Hewlett Packard Enterprise. >> Hey, welcome back Jeff Frick here with the cube come due from our Palo Alto studios, for their ongoing coverage in the celebration of Exascale day 10 to the 18th on October 18th, 10 with 18 zeros, it's all about big powerful giant computing and computing resources and computing power. And we're excited to invite back our next guest she's been on before. She's Dr. Arti Garg, head of advanced AI solutions and technologies for HPE. Arti great to see you again. >> Great to see you. >> Absolutely. So let's jump into before we get into Exascale day I was just looking at your LinkedIn profile. It's such a very interesting career. You've done time at Lawrence Livermore, You've done time in the federal government, You've done time at GE and industry, I just love if you can share a little bit of your perspective going from hardcore academia to, kind of some government positions, then into industry as a data scientist, and now with originally Cray and now HPE looking at it really from more of a vendor side. >> Yeah. So I think in some ways, I think I'm like a lot of people who've had the title of data scientists somewhere in their history where there's no single path, to really working in this industry. I come from a scientific background. I have a PhD in physics, So that's where I started working with large data sets. I think of myself as a data scientist before the term data scientist was a term. And I think it's an advantage, to be able to have seen this explosion of interest in leveraging data to gain insights, whether that be into the structure of the galaxy, which is what I used to look at, or whether that be into maybe new types of materials that could advance our ability to build lightweight cars or safety gear. It's allows you to take a perspective to not only understand what the technical challenges are, but what also the implementation challenges are, and why it can be hard to use data to solve problems. >> Well, I'd just love to get your, again your perspective cause you are into data, you chose that as your profession, and you probably run with a whole lot of people, that are also like-minded in terms of data. As an industry and as a society, we're trying to get people to do a better job of making database decisions and getting away from their gut and actually using data. I wonder if you can talk about the challenges of working with people who don't come from such an intense data background to get them to basically, I don't know if it's understand the value of more of a data kind decision making process or board just it's worth the effort, cause it's not easy to get the data and cleanse the data, and trust the data and get the right context, working with people that don't come from that background. And aren't so entrenched in that point of view, what surprises you? How do you help them? What can you share in terms of helping everybody get to be a more data centric decision maker? >> So I would actually rephrase the question a little bit Jeff, and say that actually I think people have always made data driven decisions. It's just that in the past we maybe had less data available to us or the quality of it was not as good. And so as a result most organizations have developed organize themselves to make decisions, to run their processes based on a much smaller and more refined set of information, than is currently available both given our ability to generate lots of data, through software and sensors, our ability to store that data. And then our ability to run a lot of computing cycles and a lot of advanced math against that data, to learn things that maybe in the past took, hundreds of years of experiments in scientists to understand. And so before I jumped into, how do you overcome that barrier? Just I'll use an example because you mentioned, I used to work in industry I used to work at GE. And one of the things that I often joked about, is the number of times I discovered Bernoulli's principle, in data coming off a GE jet engines you could do that overnight processing these large data but of course historically that took hundreds of years, to really understand these physical principles. And so I think when it comes to how do we bridge the gap between people who are adapt at processing large amounts of data, and running algorithms to pull insights out? I think it's both sides. I think it's those of us who are coming from the technical background, really understanding the way decisions are currently made, the way process and operations currently work at an organization. And understanding why those things are the way they are maybe their security or compliance or accountability concerns, that a new algorithm can't just replace those. And so I think it's on our end, really trying to understand, and make sure that whatever new approaches we're bringing address those concerns. And I think for folks who aren't necessarily coming from a large data set, and analytical background and when I say analytical, I mean in the data science sense, not in the sense of thinking about things in an abstract way to really recognize that these are just tools, that can enhance what they're doing, and they don't necessarily need to be frightening because I think that people who have been say operating electric grids for a long time, or fixing aircraft engines, they have a lot of expertise and a lot of understanding, and that's really important to making any kind of AI driven solution work. >> That's great insight but that but I do think one thing that's changed you come from a world where you had big data sets, so you kind of have a big data set point of view, where I think for a lot of decision makers they didn't have that data before. So we won't go through all the up until the right explosions of data, and obviously we're talking about Exascale day, but I think for a lot of processes now, the amount of data that they can bring to bear, is so dwarfs what they had in the past that before they even consider how to use it they still have to contextualize it, and they have to manage it and they have to organize it and there's data silos. So there's all this kind of nasty processes stuff, that's in the way some would argue has been kind of a real problem with the promise of BI, and does decision support tools. So as you look at at this new stuff and these new datasets, what are some of the people in process challenges beyond the obvious things that we can think about, which are the technical challenges? >> So I think that you've really hit on, something I talk about sometimes it was kind of a data deluge that we experienced these days, and the notion of feeling like you're drowning in information but really lacking any kind of insight. And one of the things that I like to think about, is to actually step back from the data questions the infrastructure questions, sort of all of these technical questions that can seem very challenging to navigate. And first ask ourselves, what problems am I trying to solve? It's really no different than any other type of decision you might make in an organization to say like, what are my biggest pain points? What keeps me up at night? or what would just transform the way my business works? And those are the problems worth solving. And then the next question becomes, if I had more data if I had a better understanding of something about my business or about my customers or about the world in which we all operate, would that really move the needle for me? And if the answer is yes, then that starts to give you a picture of what you might be able to do with AI, and it starts to tell you which of those data management challenges, whether they be cleaning the data, whether it be organizing the data, what it, whether it be building models on the data are worth solving because you're right, those are going to be a time intensive, labor intensive, highly iterative efforts. But if you know why you're doing it, then you will have a better understanding of why it's worth the effort. And also which shortcuts you can take which ones you can't, because often in order to sort of see the end state you might want to do a really quick experiment or prototype. And so you want to know what matters and what doesn't at least to that. Is this going to work at all time. >> So you're not buying the age old adage that you just throw a bunch of data in a data Lake and the answers will just spring up, just come right back out of the wall. I mean, you bring up such a good point, It's all about asking the right questions and thinking about asking questions. So again, when you talk to people, about helping them think about the questions, cause then you've got to shape the data to the question. And then you've got to start to build the algorithm, to kind of answer that question. How should people think when they're actually building algorithm and training algorithms, what are some of the typical kind of pitfalls that a lot of people fall in, haven't really thought about it before and how should people frame this process? Cause it's not simple and it's not easy and you really don't know that you have the answer, until you run multiple iterations and compare it against some other type of reference? >> Well, one of the things that I like to think about just so that you're sort of thinking about, all the challenges you're going to face up front, you don't necessarily need to solve all of these problems at the outset. But I think it's important to identify them, is I like to think about AI solutions as, they get deployed being part of a kind of workflow, and the workflow has multiple stages associated with it. The first stage being generating your data, and then starting to prepare and explore your data and then building models for your data. But sometimes I think where we don't always think about it is the next two phases, which is deploying whatever model or AI solution you've developed. And what will that really take especially in the ecosystem where it's going to live. If is it going to live in a secure and compliant ecosystem? Is it actually going to live in an outdoor ecosystem? We're seeing more applications on the edge, and then finally who's going to use it and how are they going to drive value from it? Because it could be that your AI solution doesn't work cause you don't have the right dashboard, that highlights and visualizes the data for the decision maker who will benefit from it. So I think it's important to sort of think through all of these stages upfront, and think through maybe what some of the biggest challenges you might encounter at the Mar, so that you're prepared when you meet them, and you can kind of refine and iterate along the way and even upfront tweak the question you're asking. >> That's great. So I want to get your take on we're celebrating Exascale day which is something very specific on 1018, share your thoughts on Exascale day specifically, but more generally I think just in terms of being a data scientist and suddenly having, all this massive compute power. At your disposal yoy're been around for a while. So you've seen the development of the cloud, these huge data sets and really the ability to, put so much compute horsepower against the problems as, networking and storage and compute, just asymptotically approach zero, I mean for as a data scientist you got to be pretty excited about kind of new mysteries, new adventures, new places to go, that we just you just couldn't do it 10 years ago five years ago, 15 years ago. >> Yeah I think that it's, it'll--only time will tell exactly all of the things that we'll be able to unlock, from these new sort of massive computing capabilities that we're going to have. But a couple of things that I'm very excited about, are that in addition to sort of this explosion or these very large investments in large supercomputers Exascale super computers, we're also seeing actually investment in these other types of scientific instruments that when I say scientific it's not just academic research, it's driving pharmaceutical drug discovery because we're talking about these, what they call light sources which shoot x-rays at molecules, and allow you to really understand the structure of the molecules. What Exascale allows you to do is, historically it's been that you would go take your molecule to one of these light sources and you shoot your, x-rays edit and you would generate just masses and masses of data, terabytes of data it was each shot. And being able to then understand, what you were looking at was a long process, getting computing time and analyzing the data. We're on the precipice of being able to do that, if not in real time much closer to real time. And I don't really know what happens if instead of coming up with a few molecules, taking them, studying them, and then saying maybe I need to do something different. I can do it while I'm still running my instrument. And I think that it's very exciting, from the perspective of someone who's got a scientific background who likes using large data sets. There's just a lot of possibility of what Exascale computing allows us to do in from the standpoint of I don't have to wait to get results, and I can either stimulate much bigger say galaxies, and really compare that to my data or galaxies or universes, if you're an astrophysicist or I can simulate, much smaller finer details of a hypothetical molecule and use that to predict what might be possible, from a materials or drug perspective, just to name two applications that I think Exascale could really drive. >> That's really great feedback just to shorten that compute loop. We had an interview earlier in some was talking about when the, biggest workload you had to worry about was the end of the month when you're running your financial, And I was like, why wouldn't that be nice to be the biggest job that we have to worry about? But now I think we saw some of this at animation, in the movie business when you know the rendering for whether it's a full animation movie, or just something that's a heavy duty three effects. When you can get those dailies back to the, to the artist as you said while you're still working, or closer to when you're working versus having this, huge kind of compute delay, it just changes the workflow dramatically and the pace of change and the pace of output. Because you're not context switching as much and you can really get back into it. That's a super point. I want to shift gears a little bit, and talk about explainable AI. So this is a concept that a lot of people hopefully are familiar with. So AI you build the algorithm it's in a box, it runs and it kicks out an answer. And one of the things that people talk about, is we should be able to go in and pull that algorithm apart to know, why it came out with the answer that it did. To me this just sounds really really hard because it's smart people like you, that are writing the algorithms the inputs and the and the data that feeds that thing, are super complex. The math behind it is very complex. And we know that the AI trains and can change over time as you you train the algorithm it gets more data, it adjusts itself. So it's explainable AI even possible? Is it possible at some degree? Because I do think it's important. And my next question is going to be about ethics, to know why something came out. And the other piece that becomes so much more important, is as we use that output not only to drive, human based decision that needs some more information, but increasingly moving it over to automation. So now you really want to know why did it do what it did explainable AI? Share your thoughts. >> It's a great question. And it's obviously a question that's on a lot of people's mind these days. I'm actually going to revert back to what I said earlier, when I talked about Bernoulli's principle, and just the ability sometimes when you do throw an algorithm at data, it might come the first thing it will find is probably some known law of physics. And so I think that really thinking about what do we mean by explainable AI, also requires us to think about what do we mean by AI? These days AI is often used anonymously with deep learning which is a particular type of algorithm that is not very analytical at its core. And what I mean by that is, other types of statistical machine learning models, have some underlying theory of what the population of data that you're studying. And whereas deep learning doesn't, it kind of just learns whatever pattern is sitting in front of it. And so there is a sense in which if you look at other types of algorithms, they are inherently explainable because you're choosing your algorithm based on what you think the is the sort of ground truth, about the population you're studying. And so I think we going to get to explainable deep learning. I think it's kind of challenging because you're always going to be in a position, where deep learning is designed to just be as flexible as possible. I'm sort of throw more math at the problem, because there may be are things that your sort of simpler model doesn't account for. However deep learning could be, part of an explainable AI solution. If for example, it helps you identify what are important so called features to look at what are the important aspects of your data. So I don't know it depends on what you mean by AI, but are you ever going to get to the point where, you don't need humans sort of interpreting outputs, and making some sets of judgments about what a set of computer algorithms that are processing data think. I think it will take, I don't want to say I know what's going to happen 50 years from now, but I think it'll take a little while to get to the point where you don't have, to maybe apply some subject matter understanding and some human judgment to what an algorithm is putting out. >> It's really interesting we had Dr. Robert Gates on a years ago at another show, and he talked about the only guns in the U.S. military if I'm getting this right, that are automatic, that will go based on what the computer tells them to do, and start shooting are on the Korean border. But short of that there's always a person involved, before anybody hits a button which begs a question cause we've seen this on the big data, kind of curve, i think Gartner has talked about it, as we move up from kind of descriptive analytics diagnostic analytics, predictive, and then prescriptive and then hopefully autonomous. So I wonder so you're saying will still little ways in that that last little bumps going to be tough to overcome to get to the true autonomy. >> I think so and you know it's going to be very application dependent as well. So it's an interesting example to use the DMZ because that is obviously also a very, mission critical I would say example but in general I think that you'll see autonomy. You already do see autonomy in certain places, where I would say the States are lower. So if I'm going to have some kind of recommendation engine, that suggests if you look at the sweater maybe like that one, the risk of getting that wrong. And so fully automating that as a little bit lower, because the risk is you don't buy the sweater. I lose a little bit of income I lose a little bit of revenue as a retailer, but the risk of I make that turn, because I'm going to autonomous vehicle as much higher. So I think that you will see the progression up that curve being highly dependent on what's at stake, with different degrees of automation. That being said you will also see in certain places where there's, it's either really expensive or it's humans aren't doing a great job. You may actually start to see some mission critical automation. But those would be the places where you're seeing them. And actually I think that's one of the reasons why you see actually a lot more autonomy, in the agriculture space, than you do in the sort of passenger vehicle space. Because there's a lot at stake and it's very difficult for human beings to sort of drive large combines. >> plus they have a real they have a controlled environment. So I've interviewed Caterpillar they're doing a ton of stuff with autonomy. Cause they're there control that field, where those things are operating, and whether it's a field or a mine, it's actually fascinating how far they've come with autonomy. But let me switch to a different industry that I know is closer to your heart, and looking at some other interviews and let's talk about diagnosing disease. And if we take something specific like reviewing x-rays where the computer, and it also brings in the whole computer vision and bringing in computer vision algorithms, excuse me they can see things probably fast or do a lot more comparisons, than potentially a human doctor can. And or hopefully this whole signal to noise conversation elevate the signal for the doctor to review, and suppress the noise it's really not worth their time. They can also review a lot of literature, and hopefully bring a broader potential perspective of potential diagnoses within a set of symptoms. You said before you both your folks are physicians, and there's a certain kind of magic, a nuance, almost like kind of more childlike exploration to try to get out of the algorithm if you will to think outside the box. I wonder if you can share that, synergy between using computers and AI and machine learning to do really arduous nasty things, like going through lots and lots and lots and lots of, x-rays compared to and how that helps with, doctor who's got a whole different kind of set of experience a whole different kind of empathy, whole different type of relationship with that patient, than just a bunch of pictures of their heart or their lungs. >> I think that one of the things is, and this kind of goes back to this question of, is AI for decision support versus automation? And I think that what AI can do, and what we're pretty good at these days, with computer vision is picking up on subtle patterns right now especially if you have a very large data set. So if I can train on lots of pictures of lungs, it's a lot easier for me to identify the pictures that somehow these are not like the other ones. And that can be helpful but I think then to really interpret what you're seeing and understand is this. Is it actually bad quality image? Is it some kind of some kind of medical issue? And what is the medical issue? I think that's where bringing in, a lot of different types of knowledge, and a lot of different pieces of information. Right now I think humans are a little bit better at doing that. And some of that's because I don't think we have great ways to train on, sort of sparse datasets I guess. And the second part is that human beings might be 40 years of training a model. They 50 years of training a model as opposed to six months, or something with sparse information. That's another thing that human beings have their sort of lived experience, and the data that they bring to bear, on any type of prediction or classification is actually more than just say what they saw in their medical training. It might be the people they've met, the places they've lived what have you. And I think that's that part that sort of broader set of learning, and how things that might not be related might actually be related to your understanding of what you're looking at. I think we've got a ways to go from a sort of artificial intelligence perspective and developed. >> But it is Exascale day. And we all know about the compound exponential curves on the computing side. But let's shift gears a little bit. I know you're interested in emerging technology to support this effort, and there's so much going on in terms of, kind of the atomization of compute store and networking to be able to break it down into smaller, smaller pieces, so that you can really scale the amount of horsepower that you need to apply to a problem, to very big or to very small. Obviously the stuff that you work is more big than small. Work on GPU a lot of activity there. So I wonder if you could share, some of the emerging technologies that you're excited about to bring again more tools to the task. >> I mean, one of the areas I personally spend a lot of my time exploring are, I guess this word gets used a lot, the Cambrian  explosion of new AI accelerators. New types of chips that are really designed for different types of AI workloads. And as you sort of talked about going down, and it's almost in a way where we were sort of going back and looking at these large systems, but then exploring each little component on them, and trying to really optimize that or understand how that component contributes to the overall performance of the whole. And I think one of the things that just, I don't even know there's probably close to a hundred active vendors in the space of developing new processors, and new types of computer chips. I think one of the things that that points to is, we're moving in the direction of generally infrastructure heterogeneity. So it used to be when you built a system you probably had one type of processor, or you probably had a pretty uniform fabric across your system you usually had, I think maybe storage we started to get tearing a little bit earlier. But now I think that what we're going to see, and we're already starting to see it with Exascale systems where you've got GPUs and CPUs on the same blades, is we're starting to see as the workloads that are running at large scales are becoming more complicated. Maybe I'm doing some simulation and then I'm running I'm training some kind of AI model, and then I'm inferring it on some other type, some other output of the simulation. I need to have the ability to do a lot of different things, and do them in at a very advanced level. Which means I need very specialized technology to do it. And I think it's an exciting time. And I think we're going to test, we're going to break a lot of things. I probably shouldn't say that in this interview, but I'm hopeful that we're going to break some stuff. We're going to push all these systems to the limit, and find out where we actually need to push a little harder. And I some of the areas I think that we're going to see that, is there We're going to want to move data, and move data off of scientific instruments, into computing, into memory, into a lot of different places. And I'm really excited to see how it plays out, and what you can do and where the limits are of what you can do with the new systems. >> Arti I could talk to you all day. I love the experience and the perspective, cause you've been doing this for a long time. So I'm going to give you the final word before we sign out and really bring it back, to a more human thing which is ethics. So one of the conversations we hear all the time, is that if you are going to do something, if you're going to put together a project and you justify that project, and then you go and you collect the data and you run that algorithm and you do that project. That's great but there's like an inherent problem with, kind of data collection that may be used for something else down the road that maybe you don't even anticipate. So I just wonder if you can share, kind of top level kind of ethical take on how data scientists specifically, and then ultimately more business practitioners and other people that don't carry that title. Need to be thinking about ethics and not just kind of forget about it. That these are I had a great interview with Paul Doherty. Everybody's data is not just their data, it's it represents a person, It's a representation of what they do and how they lives. So when you think about kind of entering into a project and getting started, what do you think about in terms of the ethical considerations and how should people be cautious that they don't go places that they probably shouldn't go? >> I think that's a great question out a short answer. But I think that I honestly don't know that we have a great solutions right now, but I think that the best we can do is take a very multifaceted, and also vigilant approach to it. So when you're collecting data, and often we should remember a lot of the data that gets used isn't necessarily collected for the purpose it's being used, because we might be looking at old medical records, or old any kind of transactional records whether it be from a government or a business. And so as you start to collect data or build solutions, try to think through who are all the people who might use it? And what are the possible ways in which it could be misused? And also I encourage people to think backwards. What were the biases in place that when the data were collected, you see this a lot in the criminal justice space is the historical records reflect, historical biases in our systems. And so is I there are limits to how much you can correct for previous biases, but there are some ways to do it, but you can't do it if you're not thinking about it. So I think, sort of at the outset of developing solutions, that's important but I think equally important is putting in the systems to maintain the vigilance around it. So one don't move to autonomy before you know, what potential new errors you might or new biases you might introduce into the world. And also have systems in place to constantly ask these questions. Am I perpetuating things I don't want to perpetuate? Or how can I correct for them? And be willing to scrap your system and start from scratch if you need to. >> Well Arti thank you. Thank you so much for your time. Like I said I could talk to you for days and days and days. I love the perspective and the insight and the thoughtfulness. So thank you for sharing your thoughts, as we celebrate Exascale day. >> Thank you for having me. >> My pleasure thank you. All right she's Arti I'm Jeff it's Exascale day. We're covering on the queue thanks for watching. We'll see you next time. (bright upbeat music)

>> Live from Toronto, Canada, it's theCUBE, covering Global Cloud and Block Chain Summit 2018. Brought to you by theCUBE. >> Hello everyone, welcome back to theCUBE live coverage in Toronto for the Block Chain-Cloud Convergence Show. This is the Global Cloud Block Chain Summit part of the Futurist Event that's going on the next two days after this. Our next guest is Kesem Frank, AION co-founder and CEO of MavenNet. Doing a lot of work in the enterprise and also block chain space around the infrastructure, making it really interoperable. Of course, Jenna Pilgrim, co-founder and COO of a new opportunity called Network Effects. Welcome to the cube, thanks for joining us. >> Thanks, thanks for having us. >> Thanks, John. >> You guys were just on a panel, The Real World Applications of Block Chain. IBM was on it, which, been doing a lot of work there. This is real world, low hanging fruit, block chain, everyone's pretty excited about. A lot of people get it, and some don't. Some are learning. So you've got the believers, the I want to believe, and then the nonbelievers. Let's talk about the I want to believe and the believers in block chain. Some real world applications going on. As it's evolving, so there's evolution of the standards, technology, but people are putting it to use. What's going on in the sector around some of the real world cases you guys talked about? >> I think we're seeing a lot of collaboration as far as real world applications go, because I think people are sort of starting to understand that if a distributed network is going to work or is going to be secure, it needs diversity and it needs mass scale. If lots of different parties can work together, then they can actually form a community that's really working. As far as real world applications, there's some really interesting one as far as supply chain. Kathryn Harrison at IBM talked about their pilot about shipping, bringing together the global supply chain of distribution. There's a bunch of interesting ones about food providence and bringing together different parties just to make sure that people know what they're eating and that they are able to keep themselves safe, so I think those are two definitely interesting ones. >> Kesem, block chain, supply chain, value chains, these are kind of key words that mean something together. >> Right. >> Making things work in a new way, making things more efficient, seems to be a trend. You're kind of in that world. Is it efficient? (laughing) How's the tech working? What are some of the core threshold issues that people have to get over? >> So you know, John, that's exactly the question to ask. A lot of folks out there are looking at block chain and the promise it represents, and the one big question that keeps echoing over and over is when is this going mainstream? When are we going to see something, a domain, a use case, that is actually natively on a block chain? I think that, essentially, we kind of owe it to ourselves and to everyone that cares about this stuff to ask what's working today, August 2018 and what is still kind of pending? I co-founded a project called AION. For us, interoperability is really one of the key facets that you need to be able to solve for to make block chains real. And again, here's the 60 second argument. If you're going to grow all these solutions that are centric around the use case, they solve for different pinpoints and different stakeholders care about them. They don't really create the cohesive kind of ecosystem until they can all talk to each other, and then you have to ask yourself is the original hypothesis where it's going to be one main net, one chain that's going to rule them all, and everybody gets to play on it and everybody deploys their Dapps on stuff like Fabric or R3 or Ethereum, or whatever it might be. That is absolutely not the way we're seeing enterprise actually shaping into this domain of block chain. What we're seeing is big consortiums that already have value, tangible today, out of doing stuff on chain, and the biggest thing to solve is how do I take, to Jenna's point around supply chain or food providence, whatever it is, how do I actually open it so I can now start writing insurance events, payment events, banking, underwriting, auditing, regulation? There's this gigantic ecosystem that needs to be enabled, and again we are actively saying it's not going to be by an organic model where you and I do everything on top of a single solution. There will be a multitude of solutions, and what we need to solve for is how do we convert them from disparate islands that don't talk to each other into a cohesive ecosystem? >> This is a great point. We were talking on our intro, and we talked last night on our panel, about standards. If you look at all the major inflection points where wealth was created and value was created around innovation and entrepreneurship and industry inflection points, there's always some sort of standard thing that happened. >> Right. >> Whether it's the OSI model during the early days of the internet to certain protocols that made things happen with the internet. Here, it's interesting because if you have one chain and rule the world, it's got to be up and running. >> Yeah. >> It's not. There's no one thing yet, so I see that trend the cloud has, private cloud, public cloud, but public cloud was first but people had data centers. >> Right. >> Both not compatible, now the trend is multi-cloud. You can almost connect the dots of saying multi-chain >> Right. >> Might be a big trend. >> Right. >> This is kind of what you're teasing out here. >> That's exactly what we're about, and I think it's very interesting, the point you're making about dissimilarities between the two domains. We are in a cloud convention, and to me it means two things. One, we absolutely see the mainstream people, the mainstream players in industry, starting to take this seriously. It used to be a completely disparate world where you guys are a bunch of crazies with your Bitcoin and ether and what not. They're definitely taking this seriously now. The second thing, when you think of cloud as a model, how cloud evolved, we used to have these conversations around are you crazy, you're telling me that my data is not going to be on premise? >> It's not secure, now it's the most secure. >> Oh my God! It's in the cloud, what's a cloud? (laughing) You think of the progression model that was applicable back then, right? 10 years, 15 years back, where we started privately and we tell them OK, we'll take this side step of hybrid and then fully public. Took them a while, took them almost 20 years to get their heads around it. >> There's no one trajectory. What's interesting about block chain and crypto with token economics, there's no one trend you can map an analog to, you can't say this is going to be like this trend of the past. It's almost developing it's own kind of trajectory. A lot of organic community involvement. Different tech involvement. >> Totally. >> Different engineering mindsets coming together. You're seeing an engineering-led culture big time going on. That's propelling it up to the conversations of let's lay down the pipes, let's start running apps, but I'll do it within a two year window (laughing). >> I think the big thing to understand about that is yes, you need a whole host of developer talent to build distributed systems, but at the end of the day those systems still have to be used by people. They still have to be used by society, you still have to understand how to talk to your chief executives about what's happening within your company or what your tech teams are doing. There's a growing need for marketers, for PR people, for people who speak, I don't want to say plain English, but people who understand how-- >> Translate it to the real world. >> Yeah, they need to translate it, and how to bridge the gap between legacy systems and how do you take what you were doing before and transform it to a distributed ledger system? How do you do that without just paving the cow path? >> It's interesting, it's almost intoxicating, 'cause you got two elements that get people excited. You got the token economics, which gets people to go, "Whoa," the economics and the liquidity of money and/or value creation capture equations completely changing some of the business model stuff, which could be translated to software and Dapps and software general stuff or SaaS, et cetera. Then you got the plumbing or the networking side of it where things like latency, interoperability, absolutely matter, so with all that going on in real time, it's kind of happening at 30,000 feet and trying to change the airplane engine out. People are failing, and so there's some false promises, there's also false hopes that have not been achieved, so this clouds up the real big picture which is this is an innovative environment. We're seeing that trend. But when you get to the end of the day, what are people working on, to me, is the tell sign. Kesem, what's your project, talk about AION and the work you're doing, specifically give some examples of some of the things that you're doing in the trenches. >> Sure. >> What are you trying to solve, what are some examples you're running into and how does that relate to how things might evolve going forward? >> Sure, so there is a multitude of different problems that we work on but if you want to stick just to the fundamentals? Let's take one gigantic issue that everyone's kind of tackling from different perspectives, let's talk about scale. Scale is, especially in block chains especially challenging just because of how the technology works. How decentralized can you get before you're faced with gigantic latencies and before transaction cost are kind of through the roof? When you think about it, that is all a result of how we kind of contemplate these early stage networks. It was always the one network that is going to scale to infinity. Absolutely not the way it's going to work out. So from my perspective, again, sticking to this one issue, if you could actually give me a decentralized rail that maintains consensus throughout two networks, I can now actually have two trusted kind of go-tos instead of always putting the full brunt of the throughput on one single network. For us, that's kind of a no brainer application to interoperability. If you could actually give me all these trusted networks that work in tandem, I could now start splicing throughputs across many different parallel kind of rails. Not to similar than how we can solve for super computing. We understood there is a limit on how fast can a single CPU go and we started going wide. >> That's an interesting point, I want to just double click on that for a second because if you think about it, why would I have multiple rails and multiple systems? Maybe the use cases are different for them. >> Correct. >> You don't want to have to pick one cloud or one chain to rule them all because it's not optimized. We saw that with monolithic systems and cloud is all about levels of granularity and micro service and micro everything, right? >> Correct. >> And I would also say that gets into a security issue as well, right? You're talking about multiple layers but you also will have multiple layers of permission. You'll have multiple layers of how much information someone can see and what I think is emerging, if data is the new oil, then what's emerging is for the first time we're now able to trust data that we do not own. For corporations who say, "I don't know to market to you "if I don't know everything about you." But at the end of the day, they want to be able to leverage your data but they don't need to secure it and I think that cybersecurity issue is a huge, huge thing that's definitely coming. >> I want to get both of your thoughts on this, because we were talking about this last night. We were riffing on the notion that with cloud compute and data really drove scale. So Amazon is a great example and their value now is things like Kinesis and Aurora, some of their fastest growing services. You got SageMaker, probably will be announced at re:Invent coming up as the fastest growing service, right now it's Aurora. All data concepts. So the dataization really made cloud, great. >> True. >> Okay what's the analog for crypto and block chain? Tokenization is an interesting concept. There's almost an extension of cloud where you're saying, hey, with tokenization, the tokenization phase, how do you explain that to a common person? You say, is token going to be the token and the money aspect of and the economics the killer app? How's it transverse the infrastructures, plural? >> Yeah, or is the wallet going to be the browser? Or how are all of these things happening? >> How do you make sense of this? What's your reaction to that trend? >> So I actually get excited when I think about what token, on the most profound level, actually means. When you kind of think of where value happens in the context of these gigantic enterprises, right? You think of Apple, Amazon, Google, Facebook, any of them, and you kind of think of what the product is, it's all about the data and it's all about how do you convince people to give up data so they can monetize on it. And then you have two distinct, like literally gigantic groups of stakeholders at play. You have the users, that essentially get something free, right? I get to post on Facebook or I get to write an e-mail on Gmail. Then you have the stakeholders that actually extract all that value from my activities. A token, I think most profoundly represents, how do we actually get to a unified group where the user himself is the stakeholder that gets to extract the data? And again, the proposition is pretty straightforward. The more you use a network and the more the network becomes valuable and grows, the more value the token that drives at it. >> So it changes the value capture equation? >> Correct, different model altogether. >> The value creators get to capture the value and obviously network effects plays a big part in this? >> Yes. >> Which is your wheelhouse. (laughing) >> Yeah, definitely. I think it really comes down to core principles. Now you're able to really get down, to what Kesem was talking about, about when you're designing a token or if you're designing an incentive mechanism, you're really going down to the sort of deep game theory of why people do specific things and if we can financially incentivize people to do good rather than punish them or fine them for doing bad then we can actually create value for everyone. We're designing a new economy that now has the ability to propel itself in a fair and prosperous way, if done correctly, obviously that's the disclaimer afterwards, but. >> I love what you're saying there because if you look at collective intelligence a lot of the AI concepts came around from collective intelligence, predictive analytics, prescriptive analytics all came around using data to create value. I always talk about fake news because we have a cloud of media business that's kind of tokenized now but fake news it two things, it's payload, fake news, the fake content and then the infrastructure dynamics that they arbitraged, with network effects. They targeted specific people, fake payload, but the distribution was a network effect. Again, this was the perverse incentive that no one was monitoring, there was no- >> Well and I think in that case, yes there is news that is inherently false information but then there's also a whole spectrum of trueness, if you want to call it that so now we have this technology that allows us to overlay on top of that and say, "Well what is the providence of my information?" And with different layers of block chain systems you're actually able to prove the providence of your information without exposing the user's privacy and without exposing the whole supply chain of the media because there's like media buyers, go through all kinds of hands. >> And we believe the answer to fake news, frankly, is data access, collective intelligence and something like a block chain where you have incentive systems to filter out the fake news. >> Totally. >> Exactly. >> Reputation systems, these things are not new concepts. >> It's all about stake at the end of the day, right? It's how do you keep a stakeholder accountable for their action? You need backing so I think we're definitely on the same page. >> I love, I could talk about fake news all day because we think we can solve that with our CUBEcoin token coming out soon. I want to shift gears and talk about some of the examples we've seen with cloud. >> Sure. >> And try to map that to some navigation for people in how to get through the block chain token world. One of the key things about the cloud was something they called shadow IT. Shadow IT was people who said, hey, you know what? I could just put my credit card down and move this non core thing out in this cloud and prove to my boss, show them, not pitch 'em on the Power Point deck, to say look it, I just did this for that cost in this timeframe, and that started around 2009/2010 timeframe, the early digerati or the clouderati kind of did that but around 2012 it became, wow, this shadow IT is actually R and D practice. >> Mm-hmm. >> Right. >> You started to see that now, so the question that we see for people evaluating in the enterprise is how do you judge what's a good project? Certainly people are kicking the tires and doing a little bit, I won't call it shadow IT, but they're taking on some projects as you were talking about on the panel. How should they, the enterprises in general, the large companies, start thinking about how to enable a shadow IT-like dynamic and how should they evaluate the kind of projects? I think that's an area people just don't know what to look for. Your thoughts? >> I want to add a premise to that, because I think that's absolutely the right question to ask. We also need to add the why. Why should we, as people that do native crypto currency, even care about enterprises? A lot of people kind of theorized when Bitcoin was created to say it was anti institutional is an understatement, right? Aren't we meant to kill enterprise? The thing is, I don't think it's going to be a big bang. I don't think it's going be we wake up and nobody's using banking anymore or nobody's using the traditional healthcare or government and you know whatever insurance policies. We care about block chain in the context of enterprise because we think block chain is a fundamentally better model of doing things. It kind of does away with the black box where I need to be in business, I need to blindly trust you and it introduces a much more transparent and democratic model of doing things. We absolutely want to introduce and make block chain mainstream because that's important for us. When you think of how we do it, to your question, AION is all about interoperability, right? We create a solution that helps scale and helps different networks, decentralized networks, communicate to each other. What we also do with MavenNet, the company I run, is essentially make that enterprise friendly. It's extremely hard to do adoption and implementation within an enterprise, they're very immune to change. >> Antibodies as they say. >> Oh. >> The antibodies to innovation, they kill innovation. >> Totally, so going back to your original question, it all starts with a P and L. If somebody is going to authorize, you know, an actual production system in enterprise for block chain, it needs to create a tangible value, a tangible return, quickly and that's the key. The model that actually scales is you start by flushing out inefficiency plate. You show the enterprise how you could actually achieve, I don't know 20%/30%, that's the order of magnitude that they care about, efficiency by moving some part of your value chain on top of a block chain. >> It has to have an order of magnitude difference or so. I mean cloud was a great example, too, it changes the operating model. >> Yeah. >> They achieve what they wanted to achieve faster and more efficiently and operated it differently. >> Correct. >> And people were starting at it like a three headed monster like what is this thing, right? The cloud thing. And throwing all kinds of fud out there, but ultimately at the end of the day, it's a new operating model for the same thing that they're trying to do with the old stuff. >> Mm-hmm. >> I mean, it's almost that simple. >> Yeah, I think in some cases you need to really, in my previous life at the Block Chain Research Institute, we encouraged a lot of our clients to really take a step back and say, well will I actually, A, will I have this problem in eight years or seven years or 20 years or 50 years, if we're really fundamentally building a new financial system or a new way of doing things that is fundamentally different? Are we building it on old technology? We need to make sure that, and that's why you've seen banks were the first in the door to say, "Yeah, payments, that sounds great, that sounds great." But the real applications that we're seeing from banks are in loyalty, they're in AMLKYC, they're in the sort of fringe operations. Something like payments is going to take a really long time to push through because of those legacy systems because payments is the fundamentals of what banks do. >> This is an interesting point, I want to get your thoughts to end the segment because I think one of the things that we've certainly seen with cloud that over the generational shifts that have happened, the timeframe for innovation is getting shorter and shorter, so timeframe is critical so if the communities are fumbling around hitting that time to value, it seems to be trending to faster and we don't want to hear slower because these systems are inadequate, they're antiquated. >> Mm-hmm. >> These are the systems that are disrupted so the timing of, whether it's standards, or interoperability or business models, operating models, they got to be faster. >> Yeah. >> That's the table stakes. >> I think it all comes down to collaborative governance. >> People have to figure out block chain faster. >> Yeah. >> What's holding us back? Or what's accelerating us? What's the key for the community at large from the engineering community and the business community to make it go faster? Your thoughts? >> Right, so I think we're still searching for the next killer app. If Bitcoin is the reason we're all sitting here today and I profoundly believe that. >> Yeah. >> What is the next thing that drives change on a global scale? That's kind of what we're trying, collectively as an industry, to figure out. Sure, many kind of roadblocks on the way. Some of them educational, perceptional, regulation, technology, but the next big wave that's going to accelerate us to the next ten years of block chain is that next killer app. Organizations such as myself, Jenna, that's our day job, we wake up and that's what we do. >> I mean I've always said, and Dr. Wong, who's the founder of Alibaba Cloud agreed with me, I've been saying that the TCPIP protocol, that standard really enabled a lot of interoperability and created lots of diverse value up the stacks of the OSI model, Open Systems Interconnect, seven layer model, actually never got standardized. It's kind of stopped at TCPIP and that was good, everyone snapped at the line, that created massive value. >> But that's a collaborative governance thing. That's people coming together and saying that these are the standards that we wish to adhere to. >> We need the moment right now. >> Yeah, so you see organizations like the Enterprise Ethereum Alliance coming out with a prospective list of standards that they think the community should adhere here. You know you have the ERC20 standard, you have all these different organizations, the World Economic Forum is playing a role in that and the UN is playing a role, especially when it comes to identity and those kind of really big, societal issues but I think that it comes down to that everyone plays a role that I'm doing my best, I think it's going to be somewhere in the realm of data so that's where I've chosen to sort of make my course. >> I think this is a good conversation to have, and I think we could continue it. I mean, I read on Medium, everyone's reading these fat protocols, thin protocols but at the end of the day what does that matter if there's no like scale? >> Yeah. >> You can have all the fat protocols you want, more of a land grab I would say but there's certainly models but is that subordinate or is that the cart before the horse? This is the conversation I think is in the hallways. >> Totally agree, totally agreed. >> Guys, thanks so much for coming on theCUBE, really appreciate it. Breaking down real world applications of block chain we're at the Global Cloud and Block Chain Summit. It's an inaugural event and think it's going to be the kind of format we're going to see more of, cloud and block chain coming together. Collision course or is it going to come in nicely and land together and work together? We'll see, of course theCUBE's covering it. Thanks for watching. Stay with us for more all day coverage. Part of the Futurist Conference coming up the next two days. We're in Toronto, we'll be back with more after this short break. (theCUBE theme music)

>> SiliconANGLE Media presents theCUBE! Covering the Alibaba Cloud annual conference. Brought to you by Intel. Now, here's John Furrier... >> Hello everyone, welcome to exclusive coverage with SiliconANGLE, Wikibon, and theCUBE here in Hangzhou, China for Alibaba Cloud's annual event here in Cloud City, the whole town is a Cloud. This is their event with developers, music festivals, and again, theCUBE coverage. Our next guest is Dhiraj Mallick, who is the Vice President of the Data Center Group, and the General Manager of Innovation, Pathfinding, and Architecture Group. That's a mouthful. Basically the CTO of the Data Center Group, trying to figure out the next big thing. >> That's right, John. >> Thanks for spending the time. >> It's my pleasure. >> We're here in China, it's-- You know in the U.S., we're looking at China, and we say okay, the fourth largest Cloud, Alibaba Cloud? >> Yes. >> Going outside of Mainland China, going global. You guys are strategic partners with them. >> Yes. >> They need a lot of compute, they need a lot of technology. Is this the path that you're finding for Intel? >> Yeah, so we've been collaborators with Alibaba for over 10 years, and we view them as a very strategic partner. They're one of the Super Seven, which is our top seven Cloud providers, and certainly in China, they're a very relevant customer for many years. We engage with them on a variety of fronts. On the technology side, we engage with them on what their key pinpoints are, what is the problems they want to be solving three to five years out, and then we co-develop, or co-architect solutions with them. >> So, I want to get your take on the event here in China, and how it relates to the global landscape, because I, it's my first time here, and I was taken back by the booth. I walked through Alibaba's booth, and obviously Jack Ma is inspirational. Steve Jobs like the culture, and artistry and science coming together, but I walked through the booth, it's almost too good to be true. They've got Quantum Computing, a Patent Wall, they've got Hybrid Cloud, they got security, they have IoT examples with The City Brain, a lot of great tech here at Alibaba Cloud. >> So I think the technologies that they're investing in are very, very impressive. Most cloud companies are probably not as far along as them, and looking at such a broad range of technologies, the Brain Project is really exciting, because it's going to be the Nexus of smart cities, both in China, as well as globally. The second thing that's very interesting is their research and investments in Quantum. While Quantum is not here today, it's certainly on the frontier, and Intel also has significant investments in sort of unpacking where Quantum will go, and what promises it offers to address. >> What I find interesting is that also hearing the positioning of, I kind of squint through the positioning, they're almost talking Cloud-native, DevOps, but they have all this goodness under the hood, and they're kind of talking IT-transitioning to Data Technology. Everything's about data to these guys, not just collecting data, using data with software. Now, that's really critical, because isn't that software-defined, data-driven is a hot trend? >> Yes, software-defined and data-driven is a very hot trend, in fact at Intel our CEO and us all believe that we've entered the data economy, and that the explosion in data is, and the thirst for analyzing that data to be able to drive smart business analytics is really the key to this digital revolution. I was reading an industry report by one of the analysts that said by 2019 there would have been over 100 billion dollars spent on business intelligence. And so, the real key is this data economy. >> The intersection of things, and even industrial internet, IIot, Industrial Iot, with artificial intelligence AI, intelligence Intel inside that word, interesting play on words-- >> Yes. >> Is coming together, and we've covered what you guys were doing on Mobile World Congress this year, where 5G was clearly an end-to-end architecture. You got FPGAs, all this goodness here going on. So that's 5G, and that's going to fuel a lot of IoT if you think of it like that way, but now AI. >> Yes. >> It's Software. How does that connect? Because that's the path we see forward on the Wikibon analyst side, we see software eating the world, but data eating software. And now you got 5G creating more data. >> Yeah, so the way we look at it at Intel is, we have data-center technologies that are fueled by the growth at the Edge by IoT devices, because they're creating demand for more processing capability to be able to unpack and analyze that information, and it's a self-fulfilling circle. We call it the virtual cycle of growth, because the data center feeds IoT demand and then IoT feeds the data center. And so it's the combination of those. What 5G does, is 5G forms the connectivity fabric between the data center and the Edge. It allows data to be pre-positioned at the correct places in the network, so that you minimize latencies through the network, and can process or do the analytics on it as quickly as you possibly can. >> So we were talking before we came on camera about Jack Ma, they call him Jackie Ma here, keynote being very inspirational, and talking moving to a new industrial era, a digital economy, all that good stuff, very, very inspirational. Let's translate that into the data center transformation, because we're seeing the data center and the Cloud with Hybrid Cloud become really critical to support what you were just talking about which is, how do you put it all together? It sounds so easy, but it really is difficult. >> It is, and so our vision is that in order to be able to fulfill this data economy, we will need to have five key innovations in the data center. The first innovation, in no particular order, is that the data center will be frictionless. And what I mean by frictionless, is that there will be zero to low latencies in order to provide that real-time experience at the Edge. So latency is extremely critical, and the way we believe that that can be achieved is by moving from copper to light. And Intel has significant investments in leadership products and silicon photonics that will enable switches to be based on photonics. It'll enable CPUs, and server hosts to be based on light. So we believe that light is a critical aspect to this success. The second aspect of frictionless is the need for liquid cooling and that was in the keynotes from Simon Hu this morning, that the liquid cooling is going to be essential to be able to enable a lot more horsepower in these data centers to be able to handle the volume of data that's coming. >> So you guys obviously with the photonics and the liquid cooling, you guys have been working on this in your labs for a long time, it's great R&D, but you need the connective tissue because with 5G you're now talking about a ubiquitous RF cloud, powering autonomous vehicles. We're seeing the Brain Project here, ET Brain, the City Brain-- >> Yes. >> Which is essentially IoT and big data being a big application that they're showcasing. What's the connective tissue? How does that work, from the data center, to the Edge? What's Intel's position? How do you see it? And what's going to unfold in front of our eyes? >> Yeah, so two things, so number one, I believe that the data center is boundary-less. It's not based on four physical walls. It's a connected link between the data center, and all the Edge devices that you called IoT. In order to fulfill this, you have to have 5G technology. We're invested in Silicon, in radio technologies, as well as in driving the 5G industry in consortia, to be able to bring 5G solutions to market. We think that 5G, as well as a tiered architecture between the Edge to the center, where you do some processing at the Edge, the radio stations, some in intermediate data centers, and then some in the back end Cloud data center, is what's going to be essential, and Intel has significant investments, both in developing this distributed hierarchical architecture, as well as in 5G. >> That's a great point. I want to just unpack that, and double-click on it a little bit, because you mentioned data at the Edge, and you also said earlier, low latency. Okay, a lot of people have been talking about, it costs you speed and time to move data around. So there's no real one general architecturing, where you have to kind of decide the architecture for the use case. >> Yes. >> So, the beauty is in the eye of the beholder, whoever has the workloads or the equipment. >> Yes. >> How do you look at that, because now you're thinking about, if I don't want to move data around, maybe you shouldn't, maybe you want to move data around. How does that fit with the Cloud of model, because we're seeing Cloud being a great use case for IoT in one instance, and maybe not in another. How do you think about that? How should practitioners think about the data architecture? >> Yeah, so our vision is that the Cloud changes from a centralized Cloud, to a distributed Cloud, and is amorphoused between the Edge where the IoT devices are, and the backend, and the way to think about it perhaps, is to say that storage as people have envisioned it, as being centralized, that paradigm has to change, and storage has to become distributed, such that data is available at different points in the network, and my vision is that you don't want to move data around, you want to minimize data movement for most use cases, and you want to have it pre-positioned on the 5G network, and you want to move the compute to the data, that's more energy-efficient. >> So I got to ask you, as someone who's doing the path-finding, which is the future path for Intel, and innovation and architecture. I was talking with some practitioners recently at another event, and trying to find someone, because I don't speak Chinese very well. But they asked me the same question. It matters what's in my Cloud. And what they mean by their Cloud, either on-premise private Cloud that they're putting together, operating model of their business, now going Cloud-like. But also as they pick their Cloud provider, they want to have multi-Cloud, and so what's in their Cloud, and their Cloud provider's matters. You guys are the inside of the Cloud across many spectrums, Intel. >> Yes. >> How should a customer think about that question? What's in my Cloud? Why should it matter, and it should matter. What's your take on that, and what should they look for? >> Yeah, so my take is that for years we've had the debate of whether it's public Cloud, or private Cloud, or on-prem Cloud. Our view is that the world is Hybrid, which is why we are big supporters of Alibaba, and the Hybrid Cloud movement, and as such, if it's Hybrid, it sort of suggests that the end state is that there'll be about an equal amount of applications that run on public versus private, and so I think the number of applications have an affinity to move into the public Cloud, like mail, and then there's other applications that you might care more about the compliance and security that you would say have an affinity to being on-prem. >> Also you mentioned that there's no walls, it's boundary-less in the data center. Okay, there's no door, there's no mote, you can't put a firewall on that door, unlimited access surface area for security. Obviously security hacks are big. We found out today that Israel had hacked, and notified the NSA. Hacking is a huge problem. Equifax is going to be another one. How should customers protect themselves? >> It's a very fair question John. This is one of the side-effects of saying that the data center will be boundary-less. We now have to have security technologies that can, we've effectively expanded the attacks of security in a significant way, but I don't think the answer is to say we need to move backwards and not adopt this boundary-less Cloud. I think we want to adopt it, and we want to develop technologies. So at Intel, we are developing multiple isolation technologies that allow different VM and container tenants to be isolated from other tenants. >> And this was your point earlier, making the device more intelligent, whether that's more on-board memory, and more chips. >> Yes. >> That's what you were kind of referring to, is that right? >> That's correct. >> Okay great, so I want to get one kind of off-the-wall question, since I have you on here. It's just a brain trust here from Intel, which it's great to have him here. Distributed computing has been around for awhile, we know all about that. Network effects, distributed computing, the computer industry. But now we're seeing a trend with decentralization. Blockchain is one shining example. Russia just banned cryptocurrency. This poses a architectural challenge. What's your thoughts on the decentralization, and distributed architectures that are emerging? Opportunity is scary. How should customers think about decentralization? >> Well certainly there's a security challenge, as we just spoke, related to this. But I think the computer industry has oscillated, depending on the era and the needs between centralized and decentralized a number of times now. And we're going through an era where decentralization makes sense, because we expect 30 to 50 billion devices at the Edge, and so you can't handle that with a centralized model, primarily due to three reasons, number one, just moving that volume of data would be very expensive to do over the network. Second there'll be a number of applications that are latency-sensitive. And third, you might care about data federation, and crossing country boundaries in a number of cases. So I think for the use case that we have with IoT, we have to adopt decentralized and distributed. >> So, if The Brain is processing and data, and you've got plenty of it at Intel with more compute power, what's the central nervous system, the metadata? >> Well, actually look at the central nervous system as the 5G distributed network that enables the end-points, or the nerve endings if you will, to be connected to the spinal cord. >> Okay so a final question for you, I really appreciate you spending the time. >> Sure, it's been a pleasure. >> Intel's been a wave company in its generation, and obviously Moore's law, it's not well documented. It seems that Moore's law is every year some journalist claims Moore's law is dead, and that it never goes away, so we expect more and more innovation coming from Intel. You guys have surfed many waves. In your opinion, what waves are coming? Because it feels like the waves are big now, but a lot of people think that there's bigger waves coming. That the big wave set is coming in. What's the technology wave that you're looking at from a path-finding, innovation standpoint, that customers should look for, maybe prepare for. It could be further out coming in. What's the big wave coming in, obviously AI was seeing these things. What's your focus on that? >> So, a number of them. I think, you know distributed computing is not a solved problem yet. But certainly it needs to be solved to be able to address these end-point challenges. Another great example I think, is around visual computing. So in the past, most of the type of data that people handled, was textual. But that's moving to visual very rapidly, and there's so many examples. You brought up the City Brain Project as an example. But video and analyzing images, requires a different kind of art. Different compression techniques. If a human doesn't need to see it, you perhaps don't have to have as high a resolution, and so there's a number of ships in the assumption space. And so I think for me, visual computing is a great opportunity, as well as a wave, that's coming at us. >> And the software too. So the final question, final, final question. Alibaba here, are connecting the dots. You can see where it's going. How do you see the Cloud service provider opportunity, because obviously they're a Cloud service provider on paper, but they're big, they're a Native Cloud now, like with the big guys like Amazon, Google, Microsoft. But we're seeing an emergence of new class of Cloud service provider. Certainly our research is showing that what was a very thin neck in the power laws, now expanding into a much bigger range, where VARs and value-edited software developers are going to start doing their own Cloud-like solutions with the Native Clouds, because they need horizontally scalable data infrastructure, connective tissue, and Edge devices from Intel, but they're going to provide software expertise that's vertically specialized, whether it's traffic, IoT, or oil and gas, or financial, Fintech. The specialism of application developers combined with horizontally scalable Cloud, it seems like a renaissance in the Cloud service provider market. Do you see that as well, and how should the industry think about this potential renaissance? >> So I think there's two possibilities. One is for the vast majority of functions that people run in the public Cloud, I think one possibility is that there's a consolidation amongst a few players. But I think your point's a very good one. That they are specialized services that companies are able to provide, where they're able to carve out a niche, and become a Cloud provider for that particular set of functions, as well as there's a second reason that motivates regional Cloud providers to succeed, again, because of data federation requirements, as well as local proximal, proximity to the end-points. I think these two phenomena are likely to drive the emergence of regional Clouds, as well as specialized Clouds, like you described to perform certain functions. >> And potentially a new kind of ecosystem development. >> Yes. >> And this is, then you guys are all about ecosystems, so is Alibaba. >> That's right. >> Dhiraj, thanks so much for coming on theCUBE, this is exclusive CUBE coverage with SiliconANGLE, and Wikibon here in China with Intel's booth here. Talking about AI, and the future of the data center and Cloud. I'm John Furrier, thanks for watching.

>> Announcer: Live from Las Vegas, it's the Cube! Covering NAB 2017, brought to you by HGST. >> Hey welcome back everybody Jeff Rick here with The Cube, We are getting towards the end of day three at NAB 2017, and we've talked to a ton of people from security, and storage, and applications, and now we get to talk to a creator. And really excited to have Victoria Nece on, she's a project manager for adobe After Effects, welcome. >> Thank you it's great to be here. >> Absolutely, been getting a little background on you, you were just really an animator and Adobe was smart enough to say "Hey this girl's got her shit together, we should bring her inside and have her help with the team at a bigger level." Instead of all the little things you were doing. >> Yeah so I was a motion designer mostly for documentary for a long time. And I got really into writing my own scrips and extensions and I used to say I like to make After Effects do stuff it wasn't supposed to do, and now it's my job to help make it do those things. >> Okay so what are some of the new things you said that you know, luckily we're past the official release date, you can actually talk about things >> Yes. >> So what are some of the new things? >> Uh, so we have a great new release, just came out last week, last Wednesday we're super proud of it, it's available to anyone who has creative cloud subscription. And a big thing, and this is across After Effects and Premier, is a new thing called the essential graphics panel. It allows you to make really elaborate- anything you want to do in After Effects you can go fully advanced motion graphics, and then choose the properties in editor you want to be able to change. So I can say, I'm designing something but it's on brand, I don't want you to change the color, but you can change the text, you can reposition something on the screen, we can change the background color, do all of those kind of things, and I can add those controls in After Effects and when I save those as a motion graphics template, it gets packed up and someone can use it in Premier and change those things live in the timeline with no rendering, so. >> It's really interesting just the whole collaboration, you know, kind of aspect. It used to be so much, you know, an individual sitting down on their hopefully very big machine with a lot of memory and compute, you know, working on Adobe. But now, it's really more of a collaborative effort. There's not a lot of people just working independantly all by themselves on the machine. >> True. >> Especially with Cloud and some of these really higher performance applications. >> Yeah it's actually been really interesting to watch what's happened. We have a beta service called Team Projects and I've been doing press demos where I'm in Seattle and one of my colleagues is in Germany and we're collaborating live on the same projects, I'm on After Effects, he's in Premier, I make a change, it shows up right in his timeline he doesn't even have to open After Effects, doesn't have to import anything, and it's all really seemless. And we've actually, we've all been collaborating the whole time but now you can do it without all those extra steps of rendering, and sending a file, and downloading the file, and importing it, and then adding it. Now that can all just happen in one click. >> It's like Google Docs versus Word. >> Yeah, right. >> Save and attach a file and send, hopefully you remember to save the file. >> Alright and the other thing you're really excited about is character animator. >> Yes. >> So what's going on there? >> So for people who don't know, character animator is a new application from the original creators of After Effects. It's a separate application that allows you to do real time live animation using your webcam and your microphone and also even use a touch screen, keyboard, mouse, basically hardware you already have, to power a character that starts off as a Photoshop or Illustrator file, and character animator brings it to life. We've seen some really amazing stuff people are doing with it. >> So real time live animation, so that seems like completely impossible, cause back in the day that's all we would hear about, is you know you have to render render render render render to get this animations stuff going. But now you're saying you've got it broken down so that we can do it live. >> There's this great line from The Simpsons that animation is rarely done live, it's a terrible strain on the animators wrists, and we're working to change that (laughs). It's a lot of fun and also you look at the screen and your character looks back at you, it's this really amazing experience working in it. And we've been working to make it easier to use, easier to get started, we've added workspaces so now it actually walks you through the process of getting characters set up and rigged and then a different space for performing. But it's, character animation's fun. >> And then now you're bolting that onto all these various live video distribution services. >> Mhm, we've added Mercury transmit support, which means you can go out to broadcast hardware, you can connect to absolute stream, to Facebook live, Youtube live, we're seeing things like Steven Colbert's The Late Show they use character animator to do cartoon Trump and he's improving live with a cartoon character and it's all happening in real time. >> (laughs) So as you look back and this is all fascinating and it's great, now you've got the power of the whole company to kind of make many of your visions come true. Where does it go next? It just seems like the creative opportunity, or the tools for the creator, are just exploding. >> I think there's a lot of cool stuff we can do, but for me one of the biggest things is anything we can do to save people time, and to save people doing the boring stuff, I want to give people more space to create. >> Right. >> So, don't have to think about verging, you don't have to think about all those outputs, but all the stuff about- get that out of the way, get the data entry out of the way so you can actually focus on the stuff you really want to be doing. >> And what about 360 and VR and all those crazy new technologies which are all over these halls. >> It's everywhere. Premier's got some really cool stuff this release, they've got Ambisonic audio so you can actually do VR, 360 footage and the sound comes from the right place in the shot as you turn your head. >> Ambisonic v- >> Ambisonic audio. >> Ambisonic audio. >> So there's some really cool stuff happening there. And then on the After Effects side we have some amazing partners who have been doing super cool stuff with VR, their tools are really evolving, and it's a really nice seemless workflow working with them. >> (laughs) So where does it go next? >> Oof. >> Anywhere, right? >> Anywhere really. >> No it's just amazing how again these tools that really put everything in the power of basically anybody's hands. It's kind of this whole democratization theme which we continue to hear over and over again. >> We've really focused a lot on trying to get just the tools you need right now to get you most of the way there, super simple, and then when you need to go deep, you can go deep. We're not limiting you to the simple tools, but everything's right in context, right in front of you, the stuff you change the most is right there. And then when you need to go in and tweak and get to the pro level it's another step down. And so we're trying to really build that kind of a workflow so that you have sound and graphics and color all right in edit and then you have the big pro apps for when you need to do the fancy stuff. >> The heavy lifting. And I wonder, Victoria, you talked about the community, cause Adobe's got a really active community, you guys have a huge show that brings everybody together, you obviously came out of that community into the mothership. How important is this, you know, kind of an active community around the creative process, tools you mentioned you even wrote your own scripts. >> Mhm it's, I love the After Effects community in particular they're my friends and a show like this, I see people I have really great friends that I only see once or twice a year at these kind of shows, but it's such a great strong global community that we stay in touch throughout the year, and our users really drive where we're going with things. A lot of the features in this release of After Effects, I could tell you by name who's been asking for them for years and who's super excited to see something in there. >> Okay, so if I see you again in 2018 can you give us a hint as to maybe what we'll see? Don't get in trouble. >> I might get in trouble. But we've got some really cool stuff under way. >> Alright, well we'll keep an eye, and you guys over on the table, you got to learn how to do this talking creative animator thing. I could think of some people that we might want to chin up not the real Donald Trump, but some other people. (laughs) >> Alright Victoria, well thanks for spending a few minutes with us and again, congrats on the new relase. >> Thank you, it's really great to be here. >> Alright Victoria Nece, I'm Jeff Rick you're watching the Cube from NAB 2017. Thanks for watching.

>> Announcer: Live from Las Vegas, it's theCUBE. Covering NAB 2017. Brought to you by HGST. >> Welcome back to The Cube, we are live from NAB 2017 on day three, live from Las Vegas. Excited to be joined by my next guest from Adobe, Laura Williams Argilla. Welcome to The Cube. >> Thank you so much for having me. >> You are the director of Product Management for Professional Video. >> Laura: Yes, I am. >> And you've been, you are focused on digital video and storytelling. It sounds like that's been a long-time passion of yours. >> Yes, I actually was raised in a family, my dad was a video person as well. He worked with educational technologies and helping connect people in remote areas with more populated areas for educational purposes. And he always had video gear around the house and was very passionate about watching movies and making television. And so he got me indoctrinated pretty young. And by the time I graduated from high school, I knew that I wanted to do something with media. And so I went to school for broadcasting. >> Wow, that's fantastic. So speaking of connectivity that your dad was able to facilitate, tell us about what Adobe is doing here at NAB 2017. What's the Creative Cloud? >> So the Creative Cloud is the suite of Adobe tools. And it is a collection of all of the tools that enable creativity from digital imaging to motion pictures to Photoshop and all of the core creative tools, and a collection of services that help enable the connection between those tools. At NAB this year, we're announcing the Creative Cloud additions, or updates to the video products including After Effects, Premiere, Premiere Pro, AME, Audition, and Speedgrade Prelude. The whole bundle. >> The whole bundle. So talk to us about the target audience for Creative Cloud. Is it the wannabe YouTube star? Or are we talking about broad spectrum or is it more focused on the kind of like the individual filmmaker? >> With the Creative Cloud, we actually have a really broad range of customers who we target. We target everybody from the aspiring YouTube creator who's just starting their channel, all the way up to some of the major motion pictures. Deadpool was edited in Premiere Pro, Hail, Caesar! by the Cohen Brothers was also edited in Premiere Pro, as was Gone Girl. And we continue to just see amazing adoption. Also, Premiere is broadly used in broadcasting environments, but that doesn't preclude us from also being incredibly functional for individuals or small groups. >> So if we look at kind of those target audiences as maybe the large and the small separately for a second, walk us through for the aspiring YouTuber, what are some of the benefits that person is going to get in comparison to the benefits that a creator of Deadpool would get for example? >> Sure, so I think, in general, there's a lot of overlap because they're both trying to tell stories, right? So you both start with raw footage and shape that into the story that you're trying to tell, and those tools work whether you're working on a motion picture or you're working on a YouTube channel. But I think there's certain things that we've introduced, like this year at the show, motion graphic templates, which give the opportunity to work with really powerful motion graphic effects in Premiere using simple sliders, the essential sound panel which also dramatically simplifies some of the most common audio corrections that a YouTuber or anybody would make, but especially for someone who maybe doesn't have the technical depth of being able to jump into Audition and figure out all those parameters. This is a single slider for adjusting multiple parameters to increase the overall quality of their audio with one quick move. For the broadcast and the high-end motion picture end, one of the things that we're really proud of with Adobe is that we work well with partners. We have a huge ecosystem of third-party partners, everything from asset management systems to audio enrichment systems, that you can access directly through Adobe through system panels that they can create to give direct access in our tools. And it really makes the workflow so much easier because you're not having to pop in and out of a system to get work done. >> One of the things that kind of popped up when you were talking about the commonality of benefits from the aspiring individual to a studio is how they gain efficiencies from this. Talk to us a little bit more about, with respect to the partner ecosystem, how the partnering with Adobe helps enable efficiencies across this whole production process. >> Absolutely. So one of the best examples that I can give for efficiency is the asset management systems that we can enable to have direct access for users inside of Premiere. So if I'm working with any number of asset management systems, instead of having to go and use a web interface or a client interface to access my files, that can be presented as though it is part of Premiere. So it feels like I'm getting just a panel, like a window that has a view directly into my asset management system, which makes it feel like a much more cohesive part of that workflow, and also it saves me the time. And as a former editor, I know that you lose thought process when you have to jump out of what you're doing to go get that asset and come back. With this process, the interface doesn't change. You get to stay right in Premiere and go pull the assets that you need for that. And it just makes it so much easier and so you end up spending a lot less time with the jumping between, getting back to the good state and remembering what you were doing also. >> That's a really interesting point that you bring up about how we look at technology as this facilitator, as this enabler, but also the cognitive process that an individual is responsible for whatever part of it has to go through is also facilitated by offloading some of these tasks and making it automated and simpler. That's not something that I think we've heard this week or kind of talked about it in that context, but that's quite important. >> It's very important, and I think as a creative person, you want to remain in your creative space as long as possible and you don't want to go into the administrative space of asset management. You want that to be handed to where you're working. And I do think that that constant shift of focus is really difficult to manage and stay in that productive space. So I think, to me, that's one of the biggest benefits of having these interconnected tools. >> Speaking of other benefits within Adobe from a content volume perspective, you guys are providing access to over 75 million stock images, videos, 3D assets, graphics. What does Adobe's cloud look like to be able to facilitate this quick access to things like that? >> So we have a really powerful architecture behind our cloud. Each part of the system is established to best serve that type of use, and the acquisition of Adobe Stock has been one of our prides and joys because it is, again, the direct access to millions of images and videos and you can access those directly through your product. So if I'm in Premiere and I need a stock image, I can search for stock images inside of Premiere and I can place that image and test it, it'll be watermarked. I can show it to you, say does this work? You say yes, and I can buy it without having to go through the process of replacing that image. I just click, buy, and it changes the image in place, letting me know that I've now purchased it or licensed it, which is, again, a huge time saver. But the infrastructure behind the cloud is really, wow, (laughs) it's large and scalable and we have incredible uptime service. We're very, very fortunate with the way that we've been able to manage that architecture. >> Do you find any of, is security a concern for, or are you finding it now that there's so much proficiency in, not only cloud technologies, but cloud users, that it's really not nearly as big of a concern as it was before? >> I think there used to be a lot more concern about it, and Adobe has made security a first priority for cloud assets, especially when we understand that your creative material is so much a part of your income, and it's yours, it's proprietary. You don't want other people to have access to it unless you choose to share it. So we have a full security team focused on making sure our assets remain safe. But in the past few years, we've seen an enormous shift in people's willingness to put assets in the cloud and data in the cloud. And I think as people become more comfortable with it because of the known quantity of what internet security looks like, what data security looks like, they're more comfortable with it and then they're able to reap the benefits of having that connective workflow, that they are not forced to manage, upgrade, maintain. >> Exactly. >> Yeah. >> Offloading that is always fantastic. Well Laura, thank you so much for stopping by theCUBE and sharing your wisdom of all your years of expertise at Adobe, and also before when you were kind of groomed by your dad. It was great to have you on the program today. >> Thank you so much for having me. It was a pleasure. >> Good. And we thank you for watching. Stick around, we're live from NAB 2017 on day three. I'm Lisa Martin. We'll be right back. (calm and smooth electronic music)

>> Narrator: Live from Las Vegas. It's the CUBE. Covering NAB 2017, brought to you by HGST. >> Welcome back to the CUBE live in Las Vegas at the NAB show. We're having a great day so far. Very excited to introduce you to my next guest, Kevin Baillie, cofounder and VFX supervisor at Atomic Fiction and the CEO of Conductor Technologies. Never a boring day for you with those two titles, I can imagine. >> No, I like to joke that I like to make sure that I always have the most exciting job in the world so I had to pick three to make sure that I never have a down moment spoil that, that day >> Wow, I am impressed. So you just spoke at the virtual NAB conference last month on the visual effects in the cloud, power, and control. Something that I found very interesting was that six years ago, you were kind of on an island going "I have this hunch about cloud." Tell us about, what was that hunch, why did you have it, and what has it generated so far? >> Yeah, yeah, that's a great question. The hunch was less of like, "Hey cloud looks like a great opportunity." It was more of like knowing what wasn't working in the industry as it was at that time. There were all kinds of companies that were kind of like having financial troubles or having a hard time delivering projects, tons of bankruptcies and just really sad stories everywhere. And we looked at the market and said, "There's a ton of work here, this doesn't make sense." Some of the best entertainment is being made right now and it all relies on visual effects, what's wrong? And the further we broke down the problem, the more we realized that like fixed infrastructure within a market that naturally ebbs and flows, it just didn't, there wasn't a match there. So, through that problem, we looked for solutions and cloud was a very obvious one at that point. So we just made the jump. >> And tell us about Atomic Fiction versus Conductor Technologies. Chicken, egg, which one came first? And how are they collaborating together? >> Atomic Fiction came first. It was almost seven years ago at this point that we started Atomic. And we looked for any kind of a way to use cloud. We started using an AWS directly, we then used a tool called Zync. And as we grew, we found that the needs of the company were changing so radically that nothing that was out there could actually keep up with our pace of growth. We had all this customized pipeline that we couldn't find a way to like get it into the cloud. So we built our own and that was called Conductor. And after, I think we were working on like Game of Thrones and The Walk and had just started on Deadpool that we realized it was working so well that we decided to spin it off as it's own company and make a go for actually turning it into a product that could help everybody in the same way that the cloud had helped Atomic Fiction. >> Fantastic, one of my favorite movies is The Walk. I was looking at your website and you think as the viewer, "How did they film this?" You know, this day and age, so much is CGI. Talk to us about what realtime cloud rendering is. How does it enable a movie like The Walk or Deadpool to have that awe inspiring, jaw dropping reaction from the audience? >> Well I think a large portion of bringing that jaw dropping reaction to the audience and that level of realism is being able to run productions in the way that they want to be run. And what I mean by that is, let's take a movie like The Walk where you have to recreate 1974 New York and the Twin Towers, and all these different lighting scenarios. That means we have to build every building, every rain gutter, every hotdog stand in the street down to exacting detail, and that just takes a lot of time. So we spent a ton of time, probably the first three quarters of the schedule just building the city, building the city. And we couldn't render anything at that point And it wasn't only until the very end of the show that we were able to say, "alright, now we have New York is there, let's just put it on the screen." But that takes millions of hours of computing to get that done. The Walk for example, it used 9.1 million processor hours of rendering. That's over a thousand years on a single processor to get it done. So if we hadn't had the cloud, we would have had to been like, "Oh what can we render first "so we don't bottleneck at the end of the schedule?" And really kind of like trying to bend production into the box that we, of fixed infrastructure that we have. But with the cloud, we don't have to do that. We can say, we can go as big as we want to at the very end of the show and get it done if that's what makes sense for the show. Because that's what makes sense for the show, the creative just ends up being that much better. The same was true for Deadpool, the same is true for Star Trek. These movies, they just sort of, you want to craft love into the beginning part of it so the stuff you generate at the end is as beautiful as it can be. >> So is cloud really freeing production from being able to operate in the way that it needs to operate? >> Yeah, yeah, exactly. Because the traditional model is, a visual effects company builds a data center and stuffs it full of computers. In best case, with like three weeks lead time you can like rent a bunch of racks of computers and like shove them in a closet somewhere and get your project done. It ends up being expensive and painful. You need a big team to man all that stuff. Whereas with cloud, we can say, "Hey, I need a thousand computers three minutes from now." And boom, a thousand computers spin up out of nowhere. And the great thing that we've done with Conductor as well is we've gone and negotiated per minute software licensing with Autodesk and the Foundry and IsoTropic and Chaos Group. All these big software vendors in the industry. So not only can you get compute by the minute, you can also get all the software that you need by the minute, right. So you can have three thousand nodes running Autodesk to Arnold, and you, but you run it for 42 minutes and you only pay for 42 minutes of three thousand licenses of Arnold, right. So it's really transformative from a flexibility standpoint. >> And the cost model really flips it on it's head. >> And by the way, the artists get the result back faster. Because you can scale up so big and get the result back to them so quickly without any cost penalty, they see the fruits of their labor while the ideas are still fresh in their head, which is like a huge, like, intangible benefit which has real economic benefits. >> Absolutely, one of the things and themes that we've heard of today is that speed is key. Absolutely critical to whatever is going to happen or whether or not on a shoot, a vision changes direction. And without having the power of the cloud to facilitate something on a dime, there's delays, which all adds up to economic impact. >> Yeah, and you know, back on one of our earliest projects rendered in the cloud, Flight. The Robert Zemeckis movie with Denzel Washington. That exact thing happened, where it was like at the very end, he, Zemeckis realized that he needed this extra set of like a hundred visual effects shots. And if it hadn't have been for the cloud, we would have had to say, "No, sorry we can't do these." "We have to find somebody else to do them." But because the ability of the cloud to accommodate that last minute creative epiphany, we were able to actually do the work. So it really is truly transformative and allowed us to bring in, you know, hundreds of thousands of dollars of extra revenue that we wouldn't have been able to do otherwise. >> Absolutely. In terms of some of the public cloud providers, tell us who you're working with on that end. >> Yeah, so we're working with Google right now, using Google Compute Engine on the back end. And we're actually moving forward with Microsoft and Azure. Adding it as an option later in the year. So, hopefully at the end of the year, we'll be able to support all the large cloud providers. And be able to say, "Hey, Studio X. "We know you have an affinity for Google right now, "but on the next project maybe you need "a very specific GPU type." Or there's a company in China that needs to do some work and Google isn't there. Now Azure is your thing, right. So, I think that the world of cloud providers competing against one another is going to be really beneficial for everyone in our industry for sure. And we want to be there to facilitate a little bit of like, choose whoever's best, right. >> Right, giving you the ability to really be like agnostic on the back end. >> Yeah that's exactly right. >> So as we look at these massive resources that studios are generating, creating such interactive films, what are some of the precautions that you see and you can help them mitigate against leveraging the power of cloud. >> Well, one of the benefits of cloud is you only have to pay for what you use, just like electricity, right. One of the downsides of cloud is you have to pay for what you use, right. So, if you're not careful about the render you put in the cloud or the simulation you put in the cloud, or how long you keep data in the cloud, things can get really expensive really quickly. So, one of the things we did, and this is actually why we kind of spun Conductor off as it's own company. And we just raised our Series A round of funding back in December to build the team out, because a lot of this stuff is really complicated, is one of the big efforts, in kind of a post funding world for Conductor, is on analytics and being able to use data to help people drive production better. So you know, in the very beginning, we have cost limits where you can say, "On this shot, I don't want to spend "more than a thousand dollars." Or, "I never want this artist to be able to spend "more than fifteen hundred bucks a day." But in the future, I think that there is kind of like cloud buzz-wordy things that actually come into real play here where we can use machine learning to detect when things are taking too long and alert people. We can tell people how much a render is going to cost before they even submit it maybe. We can use computer vision to check for bad things happening in the middle of a render before a human ever has a chance to lay eyes on it. So there's all kinds of stuff we can do with data to help mitigate some of the downsides of cloud and hopefully only leave people with like great insights to help them run production better. >> That's fantastic. One of the things that really interests me is the machine learning and the artificial intelligence. To be able to look at whether it's a broadcast outlet or a film studio, to be able to take a look at and evaluate the value and additional revenue streams that can come. But also, in your case, maybe even leveraging AI and machine learning to make certain processes faster thereby lowering costs. >> Yeah, we can actually make proactive suggestions based on, like, you know, thousands or millions of data points and say like, "Hey if you tweak this value on your shading rate here, "you're going to end up with a great visual "and not spend any more time, or actually spend less." So things like that and then also working together with production management systems. Like the guys at Autodesk have a product called Shotgun that deals with schedules and artist assignments. And they can have all the schedule information. We have all the sort of infrastructure information. If we correlate those two data sets together, then we'll be able to actually proactively tell somebody when we think a shot is running behind schedule. Or a shot needs more optimization. And I mean, there's all kinds of things that we can use just purely using data and a trained machine learning model to actually help people run their entire business better, not just an individual shot. >> Right, well, six years ago, when you had this hunch, you said there were some skeptics around there. One, you must feel pretty validated by now, but are you kind of one of the go-to guys, go-to companies of this is how to do it properly? These are all of the advantages, economic advantages, etc, that we can provide? >> Yeah, I think that there were definitely people that told me I was absolutely crazy when I first got started. Some of them are actually using Conductor now, so that's kind of like good. >> That must feel good right? >> Yeah, it's a good validation point and they had a lot of reasons for thinking that we were insane, cause we kind of were. But we just sort of believed deep down that it was going to work. So, yeah, I mean now, I think we're in a great position to help people. And for me, and you know, this is always like a thing that I sometimes get a hard time for, but I'm so passionate about this industry moving into the cloud that I'm just as happy to talk to somebody about how to do it maybe on their own if they're trying to do it on a small scale. Or what our competitors might be doing. Really, through that, I've kind of, we've found a space where we don't really have any competitors yet and we're breaking new ground. Really servicing the sort of medium and enterprise scale customers, and that kind of flexibility and scale and security that they kind of need. So it's sort of interesting in this, in a way, this sort of like selfless, just being excited about cloud has helped us to find a market that we can really and truly add insane value to. >> Wow, that is fascinating. Well, your passion for it is evident. Thank you so much Kevin for joining us on the CUBE. >> Yeah, thank you so much. >> Have a great time at the rest of the show and we'll see you on the CUBE sometimes soon. >> I always do, thank you again. >> Excellent, we want to thank you for watching. Again, we are live at NAB Las Vegas. Stick around. We will be right back.

hi Jeff Rick here with the cube welcome were excited to to get out and talk to startups people that are founding companies when they come out of stealth mode we're in a great position that we get a chance to talk to him early and we're really excited to have a cute conversation with karthik rao the founder and CEO of signal effects just coming out of stealth congratulations thank you Jeff so how long you've been working behind the scenes trying to get this thing going yeah we've been at it for two years now so two years a founder and I started the company in February of 2013 so excited to finally launch and make our product available to the world all right excellent congratulations that's always a great thing we've launched a few companies on the cube so hopefully this will be another great success so talk a little bit about first off you and your journey we have a lot of entrepreneurs that watch a show and I think it's it's an interesting topic as to how do you get to the place where you basically found in launched a company yeah absolutely I started my career at a company at a cloud company before cloud really exists this is a market there's a company called loud cloud oh yeah Marc Andreessen right recent horse or two of the company and we were trying to do what the public cloud vendors are doing today before the market was really all that big and before the technologies really existed to do it well but that was my first introduction to cloud o came out of college and that's where I met my co-founder Phillip Lou as well Phil and I were both working on the monitoring products at loud cloud from there I ended up at VMware for a good run of about seven years where I ran product had always wanted to start a company and then a couple of years ago Phil and I thought the timing was right and we had a great idea and decided to go build signal effects together okay so what was kind of the genesis of the idea you know a lot of times it's a cool technology looking for a problem to solve a lot of times it's a problem that you know and if I only had one of these they would solve my problems so how did the how did that whole process work yeah it was rooted in personal experience my co-founder phil was at Facebook for several years and was responsible for building the monitoring systems at Facebook and through our personal experience and what we'd seen in the marketplace we had a fundamental belief and a vision that monitoring for modern applications is now an analytics problem modern applications are distributed they're not you know a single database running on is system you know even small companies now have hundreds of VMs running on public cloud infrastructure and so the only way to really understand what's happening across all of these distributed applications is to collect the data centrally and use analytics and so that was our fundamental insight when we started signal effects what we saw in the marketplace was that most of the monitoring technologies haven't really evolved in the past 15 or 20 years and they're still largely designed for traditional static enterprise applications where if you get an alert when an individual node is down or a static thresholds been passed that's enough but that doesn't really work for modern apps because they're so distributed right if one node out of your twenty nodes is having a problem it doesn't necessarily mean that your application is having a trough having a problem and so the only way to really draw that insight is to collect the data and do analytics on it and that's what signal okay really because that distributed nature of modern of modern apps and modern architecture yes there are three things that are fundamentally different number one modern applications are distributed in nature and so you really have to look at patterns across many systems number two they're changing for more frequently than traditional enterprise apps because they're hosted for the most part route applications so you can push changes out every day if you want to and then third they're typically operated by product organizations and not IT organizations so you have developers or DevOps organizations that are actually operating the software and those three changes are quite substantial and require a new set of products right and so the other guys are just they're still kind of in the you know fire off the pager alert something is going down it's very noisy yes when you're firing off alerts every time an individual alert goes off when you've got thousands of a DM and we all know that the trend these days is towards micro services architectures you know small componentized you know containers or VMs and so you don't have to have a very sophisticated large application to have a lot of systems it's so do you fit into other existing kind of infrastructure monitoring systems or kind of infrastructure management systems so I'm sure you know it's another tool right guys got to manage a lot of stuff how does that work yeah we are focused on the analytics part of the problem okay so we collect data from any sources so our customers are typically sending us data you know infrastructure data that they're collecting using their own agents we have agents that we can provide to collect it a lot of the developers are instrumenting their own metrics that they care about so for example they might care about latency metrics and knowing Layton sees by customer by region so they'll send us all that data and then we provide a very rich analytics solution and platform for them to monitor all of this and and in real time detect patterns and anomalies so you just said you have customers but you coming out stealth so you have some beta customers already yes we have great customers already now just beta customers right are great console customers awesome yes congratulation thank you very much they're very excited about our product and we you know they range from small startups to fairly large web companies that are sending in tens of billions of data points every day into signal effects right right and again in the interest of sharing the knowledge with all of our entrepreneurs out there you know when did they get involved in the process how much of the kind of product development definition did they did they participate in you said you've been at it for a couple years yeah we've had a lot of conviction about this space from the very beginning because we our team had solved this problem for themselves and in previous experiences but we did include we've been in beta for about six months but better to launch and so over the course of those six months we recalibrated based on feedback we got from customers but on the whole we you know are we philosophy and the approach that we took was was pretty much validated by the early customers that we engaged with okay excellent and so um I assume your venture funded we are can you can you talk about who your who your backers are yes we raised twenty eight and a half million dollars eight million dollars yeah twenty-eight point five million dollars from andreessen horowitz okay with Ben Horowitz on our board okay and Charles River ventures with a lurker on our board and how big are you now time in terms of the company well we're just getting started now right at this is 1 million all that money - well we we've got a great group of engineers or our company is you know and still in the few dozen people stage at this point ok we're planning to invest aggressively in building out our team both on R&D and on the go-to-market side this excellent once you detect patterns and anomalies what's kind of the action steps you work with with other systems to swap stuff out together because now I hear like it's these huge data centers they don't swap out this they don't swap out machines they swap out racks it's soon they'll be swapping out data centers so what are some of the prescriptive things that people are using they couldn't do before by using your yeah I'll give you a great example of that one of our early beta customers they do code pushes very aggressively you know once a week they'll push out changes into their environment and they had a signal effects console open which and we're a real-time solution so every second they're seeing updates of what was happening in their infrastructure they pushed out their code and they immediately detected a memory leak and they saw their memory usage just growing immediately after they did their code Bush and they were able to roll it back before any of their users noticed any issues and so that's an example of these days a lot of problems introduced into environments are human driven problems it's a code push it's a new user gets onboard it or a new customer gets onboard and all of a sudden there's 10x the load onto your systems and so when you have a product like signal effects where you can in real time understand everything that's happening in your environment you can quickly detect these changes and determine what the appropriate next step is and that appropriate next step will depend on your application and who you are and what you're building right so our key philosophies we get out of your way but we give you all of the insights and the tools to figure out what's happening in your arm right it's interesting that really kind of two comes from from your partners you know kind of Facebook experience right because they're pushing out new code all the time when there's no fast and break things right right exactly and then you're at VMware so you know kind of the enterprise site so what if you could speak a little bit about kind of this consumerization of IT on the enterprise side and not so much the way that the look and feel of the thing works but really taking best practices from a consumer IT companies like Facebook like Amazon that really changed the game because it used to be the big enterprise software guys had the best apps now it's it's really flipped for people like Google and Netflix and those guys have the best apps and even more importantly they drive the expectation of the behavior of an application every Enterprise is finally getting it and then are they really embracing it we're definitely seeing a growth in new application development I think you know when I spend a lot of time talking to CIOs at enterprises as well and they all understand that in order to be competitive you have to invest in applications it's not enough to just view IT as a cost center and they're all beginning to invest in application development and in some cases these are digital media teams that are separate from traditional IT and other places it's you know they're they're more closely tied together but we absolutely see a kind of growth in application development in many of these end up looking a lot like the development teams that we see here in the Bay Area you know and companies that are building staffs and consumer cloud apps yeah exciting time so you should coming out of stealth what's kind of your your next kind of milestone that you're looking forward to you have a big some announcements you got show you're gonna kind of watch out we're we're we're gonna see you make a big splash well for us it's it's steadily building our business and so we hope to you know we're launching now and we've got a lot of great customers already and hope to sign on several more and help our customers build great applications about that's our focus again congratulations two years that's a big development project Karthik thank growl the founder and CEO of signal effects just launching their company coming out of stealth we'd love to get them on the cube share the knowledge with you guys both the people that are trying to start your own company take a little inspiration as well as as the people that need the service tomorrow with the cloud with a modern application thanks a lot thank you Jeff thank you you're watching Jeff Rick cube conversation see you next time

>>we're back. >>This is Dave Volante. I'm with Wiki bond dot Oregon. This is Silicon angle's the cube where we extract the signal from the noise. We go into the events, we're bringing you the best guests that we can find. And we're here at the AWS summit. Amazon is taking the cloud world by storm. He was on, invented the cloud in 2006. They've popularized it very popular of course with developers. Everybody knows that story. Uh, Amazon appealing to the web startups, but what's most impressive is the degree to which Amazon is beginning to enter the enterprise markets. I'm here with my cohost Jeff Frick and Jeff, we heard Andy Jassy this morning just laying out the sort of marketing messaging and progress and strategies of AWS. One of the things that was most impressive was the pace at which they put forth innovations. We talked about that earlier, but also the pace at which they proactively reduce prices. Uh, that's different than what you'd see in the normal sort of enterprise space. Talk about that a little bit. >>Yeah. Again, I think it really speaks to their strategy to lock up the customer. It's really a lifetime value of the customer and making sure that they don't have a really an opportunity or a reason to go anywhere else. So as we discussed a little bit earlier, they leverage, you know, kind of the pure hardware economics of, of decreasing a computing power, decreasing storage, decreasing bandwidth, but then they also get all the benefits of scale. And I think what's in one of the interesting things that Andy talked about and kind of his six key messages was that it's actually cheaper to rent from them because of the scale than it is to buy yourself. And I know that's a pretty common knock between kind of a build or buy, um, kind of process you go through and usually you would think renting at some scale becomes less economical than if you just did it yourself. But because their scale is so massive because of the flexibility that you can bring, uh, computing resources to bear based on what you're trying to accomplish really kind of breaks down the, uh, the old age old thought that, you know, at scale we need to do it ourselves. >>Well, and that's the premise. Um, I think, and, uh, let's Brits break down a little bit about that, that analysis and, and Andy's keynote. So he put forth some data from IDC which showed that, uh, the Amazon cloud is cheaper than the, uh, a, a so-called private cloud or an in house on premise installation. You know, I certainly, there's, it's, it's a, it's an, it's depends, right? It really depends on the workload. That's somewhat of an apples to orange is going on here and the types of workloads that are going down in the AWS cloud, granted he's right and that they're running Oracle, they're running SAP, but the real mission critical workloads, what he calls mission critical aren't the same as what, you know, Citi would call mission critical. Right? So to replicate that level of mission criticality, uh, would probably almost most certainly be more expensive rental versus owning the real Achilles heel of, of, of any cloud, not just Amazon. >>Cloud really is getting data out. Um, moving data, right? Amazon's going to charge you not to get data in. They're gonna charge you to store it there to exercise, you know, compute. Uh, and then, but they're also gonna charge it if you wanted to take it out. That's expensive. The bandwidth costs and the extrication costs are expensive. Uh, the other issue with cloud again is data movement. It takes a long time to move a terabyte, let alone multiple terabytes. So those are sort of the two sort of Achilles heels of, of cloud. But that's not specific to Amazon's cloud. That's any cloud. Yeah. So we've got a great lineup today. Um, let's see. We've got Ariel Kelman coming on, uh, and I believe he's in the house. So we're going to take a quick break. Quick break. Right now we right back with Ariel Kelman, who's the head of marketing at AWS. Keep right there. This is the cube right back. >>we lift out all the programs out there and identified a gap in tech news coverage. Those shows are just the tip of the iceberg and we're here for the deep dive, the market beg for our program to fill that void. We're not just touting off headlines. We also want to analyze the big picture and ask the questions that no one else is asking. We work with analysts who know the industry from the inside out. So what do you think was the source of this missing? So you mentioned briefly there are, that's the case then why does the world need another song? We're creating a fundamental change in news coverage, laying the foundation and setting the standard, and this is just the beginning. We looked on all the programs out there and identified a gap in tech news coverage. There are plenty of tech shows that provide new gadgets and talk about the latest in gaming, but those shows aren't just the tip of the iceberg. And we're here for the deep dive. >>Okay, >>Dave Olanta. I'm with Wiki bond.org and this is Silicon angle's the cube where we extract signal from the noise. We bring you the best guest that we can find. We go into events like ESPN goes into sporting events, we go into tech events, we find the tech athletes and bring to you their knowledge and share with you our community. We're here at Moscone in San Francisco at the AWS summit. We're here with Arielle Kellman who's the head of worldwide marketing for AWS. Arielle, welcome to the cube. Thanks for having me, Dave. Yeah, our pleasure. I really appreciate you guys having us here. Great venue. Uh, let's see. What's the numbers? It looks like you know, many, many thousands, well over 5,000 people here by four or 5,000 people here. We're doing a about a dozen of these around the world, one to 4,000 people to help educate our customers about all the new things we're doing, all the new partners that are available to help them thrive in the AWS cloud. >>It's mind boggling the amount of stuff that you guys are doing. We just heard NG Jesse's keynote, for those of you who saw Andy's keynote at reinvent, a lot of similar themes with some, some new stuff in there, but one of the most impressive, he said, he said, other than security, one of the things that we're most proud of is the pace at which we introduce new services. And he talked about this fly wheel effect. Can you talk about that a little bit? Sure. Well, there's kind of two different things going on. The pace of innovation is we're really trying to be nimble and customer centric and ultimately we're trying to give our customers a complete set of services to run virtually any workload in the cloud. So you see us expanding a broader would additional services. And then as we get feedback we add more and more features. >>Yeah. So we're obviously seeing a big enterprise push. Uh, Andy was, was very, I thought, politically correct. He said, look, there's one model which is to keep charging people as much as you possibly can. And then there's our model, which is we proactively cut prices and we passed that on to customers. Um, and, and he also stressed that that's not something that's not a gimmick. It's not a sort of a onetime thing. Can you talk about that in terms of your philosophy and your DNA? It's just our philosophy. It's actually a lot less dramatic than is often portrayed in the press. Just the way we look at things as we're constantly trying to drive efficiencies out of our operations. And as we lower our cost structure, we have a choice. We can either pocket those savings as extra margin or we can pass those savings along to our customers in the form of lower prices. >>And we feel that the ladder is the approach that customers like and we want to make our customers happy. So this event, uh, we were talking off camera, you said you've been doing these now for about two years. You do re-invent once a year. That's your big conference out in Vegas and it's a very, very large event, very well attended. And you do these regionally and in and around the world, right. Talk about that a little bit. We do about a dozen of these a year. Um, we did, uh, New York a couple of weeks ago, London, Australia and Sydney. I'm going to go to India and Tokyo, really about a dozen cities in the world and it's a little tactic. I'm not going to beat all of them, but you know, the focus is to really, uh, deliver educational content. Uh, we'll do about maybe 12 to 16 technical breakout sessions all for free, uh, for, for customers and people who want to learn about AWS for the first time. >>And the, and the audience here is largely practitioners and partners, right? Can it talk about the makeup a little bit? Sure. It's a pretty diverse set of people. Um, we have a technical executives like CEOs and architects and we have lots of developers and then lots of people from our, our partner ecosystem of integrators wanting to, um, you know, brush up on the latest technologies and skills and a lot of people who just want to learn about the cloud and learn about AWS. I think there are a lot of misconceptions about AWS and I'd like to just tackle some of those with you if I may. So let me just sort of, let's list them off and you can respond. Yeah, we'll let our audience to sort of decide. So the first is that AWS has only tested dev workloads. Can you talk about that a little bit? >>Sure. Um, well test and dev local workloads are very popular. We saw, we covered that in the keynote. Um, and it's often a place where it organizations will start out with AWS, but it is by no means the most popular or most dominant workload. We have a lot of people migrating, uh, enterprise apps to the cloud. Um, if you look at, uh, in New York, uh, in our summit we talked about Bristol Myers Squibb, uh, running all of their, um, clinical trial simulations and reducing the amount of time it takes to run a simulation by 98%. Uh, if people are running Oracle, SharePoint, SAP, pretty much any workload in the cloud. And then another popular use is building brand new applications, uh, for the cloud. You can miss, some people call them cloud native applications. A good example is the Washington post who built an app called the social reader that delivers their content to Facebook and now as more people viewing their content, their than with their print magazines and they just couldn't have done that, uh, on premises. >>So, uh, the other one I want to talk about, we're going to do some serious double clicking on security so we don't have to go crazy on it, but, but there's a sort of common perception that the cloud is not secure. What do you guys say about that? Yeah, so, um, really our number one priority is security. You're looking at a security, operational performance, uh, and then our pace of innovation. But with security, um, what we want to do is to give enterprises everything they need to understand how our security works and to evaluate it and how it meets with their requirements for their projects. So it really all starts with our, our physical security, um, our network security, the access of our people. They're all the similar types of technologies that our customers are familiar with. And then they also tend to look at all the certifications and accreditations, SAS 70 type two SOC one SIS trust. >>I ATAR for our government customers. And then I think it was something a lot of people don't understand is how much work we've put into the security features. It's not just is the cloud secure, but can I interact and integrate, uh, your security functionality with all of my existing systems so we can integrate with people's identity and access systems. You could have a private dedicated connection from your enterprise to AWS with direct connect to, I really encourage anyone who has interest in digging into our security features to go to the security center and our website. It's got tons of information. So I'm putting on the spot. Um, what percent of data centers in the world have security that are, that is as good or better than AWS. It'd be an interesting thing for us to do a survey on. But if you think about security at the infrastructure layer down is what we take care of. >>Now when you build your application, you can build a secure app or non-secure app. So the customer has some responsibility there. But in terms of that cloud infrastructure, um, for a vast majority of our customers, they're getting a pretty substantial upgrade in their security. And here's something to think about is that, um, we run a multitenant service, so we have lots and lots of customers sharing that infrastructure and we get feedback from some of the most security conscious companies in the world and government agencies. So when our customers are giving us a enhancement request, and let's say it is, uh, an oil company like shell or financial services company like NASDAQ, and we implement that improvement because there's always new requirements. We implement that all of our hundreds of thousands of customers get those improvements. So it's very hard for a lot of companies to match that internally, to stay up to speed with all the latest, um, requirements that people need. >>Yeah. Okay. So, uh, and you touched on this as well as the compliance piece of it, but when you think of things like, like HIPAA compliance for example, I think a lot of people don't realize that you guys are a lead in that regard. Can you talk about that a little bit more? Yeah. So, uh, we have a lot of customers running HIPAA compliant, uh, workloads. Um, there's, there's one company or the, the Schumacher group, which does emergency room staffing out of Lafayette, Louisiana. And we, companies like that are going through the process. They have to follow their internal compliance guidelines for implementing a HIPAA compliant plan app. It's actually, it's more about how you implement and manage the application than the infrastructure, which is part of it. But we, we satisfy that for our customers. Let's talk a little bit about SLA. That didn't come up at least today in Andy's keynote, but it didn't reinvent and he made a statement at reinvent. >>He said, we've never lost a piece of business because of SLS. And that caught my attention and I said, okay, interesting. Um, talk about, uh, the criticisms of the SLA. So a lot of people say, wow, SLA, not just of Amazon's cloud, but any public cloud. I mean, SLA is a really a, in essence, a, an indication of the risk that you're able to take and willing to take. What are your customers tell you about SLS? The first thing is we don't hear a lot of questions about SLS from our customers. Some customers, it's very important that we have SLA is for most of our services, but what they're usually judging us on is the operational track record that we provide and doing testing and seeing how we operate and how we perform. Uh, and, uh, we had an analyst from IDC recently do a survey of a bunch of our customers and they found that on average the average app that runs on AWS had 80% less downtime than similar apps that are running on premises. >>So we have a lot of anecdotal evidence to suggest that our customers are seeing a reliability improvement by migrating their apps to AWS. You're saying don't judge us on the paper, judge us on our actual activities in production and in the field. Typically what most of our customers are asking for is they want to dig into the actual operational features and, and a track record. Now the other thing I want to address is the so called, you know, uh, uh, exit tax, right? It's no charge to get my data in there. I keep my data in there. You, you, you charged me for storing it for exercise and compute activity, but it's expensive to get it out. Um, how do you address that criticism? Well, our pricing is different for every service and we really model it around our customers to both really to really satisfy a broad set of use cases. >>So one example I think you may be talking about is I would Amazon glacier archive service, which is one penny per gigabyte per month. And for an archive service, we figured that most people want to keep their data in there for a long period of time so that we want to make it as cheap as possible for people to put it in. And if you actually needed to pull it out, the reason is because you may have had some disaster or you accidentally deleted something and that you are going to be, uh, you're going to be retrieving data on a far less frequent basis. So on an overall basis for most customers it makes sense that we could have done is made the retrieval costs lower and then made the storage costs higher. But the feedback we got from customers is, you know, archiving a majority of customers may never even retrieve that data at all. >>So it ended up being cheaper for a vast majority of our customers. I mean that's the point of glacier. If you put it there, you kind of hope you never have to go back and get it. Um, the other thing I wanted to ask you about is some of the innovations that we've seen lately in the industry, like a red shift, right? The data warehouse, you mentioned glacier. It was interesting. Andy said that glacier is the fastest growing service in terms of customers. Red shift was the fastest growing service, I guess overall at NAWS. So Redshift is an interesting move for you guys. Uh, that whole big data and analytics space. What if you could talk about that a little bit? If you talk to it, executives in the enterprise and even startups now, they have to analyze lots of data. Building a big data warehouse is, is one of the best examples of how much the pain of hardware and software infrastructure gets in the way of people. >>And there's also a gatekeeping aspect to it. If you're working in a big company and you want to run, you have a question and a hypothesis, you want to run queries against terabytes and petabytes of data, you pretty often have to go and ask for permission. Can I borrow some time from the data warehouse? No, no, no, no. You're not as important. Well, what are customers going to go, Hey, I'm going to go load the data, load a petabyte of data, run a bunch of analysis, and shut it down and only pay for a few hours. So it's not just about making a cheaper, it's about making use of technology possible where it was just not possible in feasible and cost prohibited before. Yeah, so that's an important point. I mean, it's not, it's not just about sort of moving workloads to the cloud, you know, the old saying a my mess for less. >>It's about enabling new business processes and new procedures and deeper business integration. Um, can you talk about that a little bit more? Add a little color to that notion of adding value beyond just moving workloads out of, you know, on premise into the cloud to cut costs, cut op ex, but enabling new business capabilities. When you remove the infrastructure burden between your ideas and what you want to do, you enable new things to be possible. I think innovation is a big aspect of this where if you think about if you reduce the cost of failure for technology projects so much that approaches zero, you change the whole risk taking culture in a company and more people can try out new ideas and companies can Greenlight more ideas because if they fail it doesn't cost you that much. You haven't built up all this infrastructure. So if you have more ideas that are, that are cultivated, you end up with more innovation. >>Whereas before people are too afraid to try new things. So I'm a reader of of Jeffrey's a annual letters. I mean I think they're great. They're Warren buffet like in that regard. One of the exact emphasizes, you know this year was the customer focus. You guys are a customer focused organization, not a competitive focused organization. And again, you got to recognize that both models can work, right? Can you talk about that a little bit? Just the church of the culture. Yeah, I mean when, you know, starts out with how we build our products. Anyone who has a new idea for a product, first thing they got to do is write the press release. So what our customers are going to see is it valuable to them. And then we get come get products out quickly and then we iterate with customers. We don't spend five years building the first version of something. >>We get it out quickly. Uh, sort of the, the, the lean startup, if you heard of the minimum viable product approach, get it out there and get feedback from customers. Uh, and iterate. We don't spend a lot of time looking at what our competitors are doing cause they're not the ones that pay our bill. They're not the ones that can hire and fire us. It's the customers. So I'm you've seen this thing come, you know, quite a ways. I mean, you were at Salesforce, right? Um, which I guess started at all in 99. You could sell that, look at that as the modern cloud sort of movement was, wasn't called cloud. And then you guys in 2006 actually announced what we now know is, you know, the cloud, where are we in terms of, you know, the cloud, you know, what ending is it? To use the sports analogy, I don't know what ending is it, but you know, it's an amazing time where there's such a massive amount of momentum of adoption of the cloud from every type of company, every type of government agency. >>But yet still, when you look at the percentage of it spend or you go talk to a large company and you say, even with all these projects, what percentage of your total projects, there's still tremendous growth ahead of us. Yeah. So, um, there's always that conversation about the pie charts. 70% of our, our effort is spent on keeping the lights on. 30% is spent on, on innovation. And I don't know where that number came from but, but I think generally anecdotally it feels about right. Um, talk about that shift. Yeah. Well I mean your customer base, you talk to any CIO, they don't like the idea of having 80% of their staff and budget being focused on keeping the lights on and the infrastructure would they like to do is to really shift the mix of what people are working on within their organization. It's not about getting rid of it, it's about giving it tools so that every ounce of effort they're doing is geared towards delivering things to the business. >>And that, that, that's what gets CIO is excited about the cloud is really shifting that and having a majority of their people building and iterating with their end users and with their customers. So we talked about the competition a little bit. I want to ask you a question in general, general terms, you guys have laid out sort of the playbook and there's a lot more coming. We know that, uh, but you know this industry quite well. You know, it's very competitive. People S people see what leaders are doing and they all sort of go after it. Why do you feel confident that AWS will be able to maintain its lead and Kennedy even extend its lead in why? Well, there's a couple things that we sort of suggest for customers to look at. I think first of all is the track record and experience of when you're looking at a cloud provider, have they been in this business for a long time? >>Do they have a services mentality where they've had customers trust them for their, for applications that really they trust their business on? Um, and then I think secondly, is there a commitment to innovation? Is there a pace of new features and new technologies as requirements change? And I think the other, the other piece that our customers really give us a lot of feedback on is that they can count on us Lauren prices, they can count on a real partnership as we get better at this and we're always learning as we get better and we reduce our cost structure, they're going to get to benefit and lower their costs as well. So I think those are kind of big things. The other thing is, is the customer ecosystem I think is a big part of it where, um, you know, this is technology. Uh, people need advice, they need, uh, best practices. >>They often need help. And I'm in a kind of analogy I make is if I have a problem with my phone, with my iPhone, I can probably close my eyes and throw it, I'm going to hit someone who also has an iPhone. I can ask them for help. Well, if you're a startup in San Francisco or London or if you're an enterprise in New York or Sydney, odds are that your colleagues, if they're doing cloud, they're doing it with AWS and you have a lot of people to help you out. A lot of people to share best practices with. And that's a subtle but important point is as, as industry participants begin to aggregate within your cloud, there's a data angle there, right? Because there's data that potentially those organizations could share if they so choose to a, that is a, that is a value. And as you say, the best practice sharing as well. >>I have two last questions for you. Sure. First is, is what gets you excited in this whole field? I think it's like seeing what customers are doing. I mean, that's the cool thing about, uh, offering cloud infrastructure is that anything is possible. Like we met Ryan, uh, who spoke from atomic fiction. These guys are the world's first digital effects agency that's 100% in the cloud. And to see that they made a movie and all the effects like the Robertson mech, his flight film without owning a single server, um, it's just, it's amazing. And to see what these guys can do, how happy they are to have a group of 30, 40 artists that, um, can say yes when the director says I want it to do differently. I want to add, go from 150 to 300 shots and to see how happy and excited they are. >>I mean that, that's what motivates me. Yeah. Okay. And then my last question, Ariel, is, um, you know, what keeps you up at night? What worries you? Well, I think, you know, the most important thing that we can't forget is to really keep our fingers on the pulse of the customers and what they want, and also helping them to figure out what they want next. Because if we don't keep moving, then we're not going to keep pace with what the customers want to use the cloud for. All right, Ariel Kelman thanks very much. Congratulations on the Mason's progress and we'll be watching and, and really appreciate, again, you having us here. Appreciate your time coming on. Good luck with the rest of the tour. I hope you don't have to do every city. It sounds like you don't, but, uh, but if it sounds like you've enjoyed them, so, uh, congratulations again. Great. All right. This is Dave Milan to keep it right there. This is the cube. We'll be back with our next guest right after this word.