>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

>>Kirk focuses on the approach to modern data quality and how it can enable the continuous delivery of trusted data. Take it away. Kirk >>Trusted data has been a focus of mine for the last several years. Most particularly in the area of machine learning. Uh, I spent much of my career on wall street, writing models and trying to create a healthy data program, sort of the run the bank and protect the franchise and how to do that at scale for larger organizations. Uh, I'm excited to have the opportunity today sitting with me as Victor to have a fireside chat. He is an award-winning and best-selling author of delete big data and most currently framers. He's also a professor of governance at Oxford. So Victor, my question for you today is in an era of data that is always on and always flowing. How does CDOs get comfortable? You know, the, I can sleep at night factor when data is coming in from more angles, it's being stored in different formats and varieties and probably just in larger quantities than ever before. In my opinion, just laws of large numbers with that much data. Is there really just that much more risk of having bad data or inaccuracy in your business? >>Well, thank you Kirk, for having me on. Yes, you're absolutely right. That the real problem, if I were to simplify it down to one statement is that incorrect data and it can lead to wrong decisions that can be incredibly costly and incredibly costly for trust for the brand, for the franchise incredibly costly, because they can lead to decisions that are fundamentally flawed, uh, and therefore lead the business in the wrong direction. And so the, the, the real question is, you know, how can you avoid, uh, incorrect data to produce incorrect insights? And that depends on how you view trust and how you view, uh, data and correctness in the first place. >>Yeah, that's interesting, you know, in my background, we were constantly writing models, you know, we're trying to make the models smarter all the time, and we always wanted to get that accuracy level from 89% to 90%, you know, whatever we could be, but there's this popular theme where over time the models can diminish an accuracy. And the only button we really had at our disposal was to retrain the model, uh, oftentime I'm focused on, should we be stress testing the data, it almost like a patient health exam. Uh, and how do we do that? Where we could get more comfortable thinking about the quality of the data before we're running our models and our analytics. >>Yeah, absolutely. When we look at the machine learning landscape, even the big data landscape, what we see is that a lot of focus is now put on getting the models, right, getting it worked out, getting the kinks worked out, but getting sort of the ethics, right. The value, right. That is in the model. Um, uh, and what is really not looked at what is not focused enough that, um, is the data. Now, if you're looking at it from a compliance viewpoint, maybe it's okay if you just look at the model, maybe not. But if you understand that actually using the right data with the right model gives you a competitive advantage that your competitors don't have, then it is far more than compliance. And if it is far more compliance, then actually the aperture for strategy opens up and you should not just look at models. You should actually look at the data and the quality and correctness of the data as a huge way by which you can push forward your competitive advantage. >>Well, I haven't even trickier one for you. I think, you know, there's so much coming in and there's so much that we know we can measure and there's so much we could replay and do what if analysis on and kind of back tests, but, you know, do you see organizations doing things to look around the corner? And maybe an interesting analogy would be something like with Tesla is doing whether it's sensors or LIDAR, and they're trying to bounce off every object they know, and they can make a lot of measurements, but the advancements in computer vision are saying, I might be able to predict what's around the corner. I might be able to be out ahead of the data error. I'm about to see tomorrow. Um, you know, do you see any organizations trying to take that futuristic step to sort of know the unknown and be more predictive versus reactive? >>Absolutely. Tesla is doing a bit Lincoln, uh, but so are others in that space and not autonomous driving space, um, uh, Waymo, the, uh, the, the, uh, Google company that is, uh, doing autonomous driving for a long period of time where they have been doing is collecting training data, uh, through their cars and then running a machine learning on the training data. Now they hit a wall a couple of years ago because the training data wasn't diverse enough. It didn't have that sort of Moore's law of insight anymore, even though it was more and more training data. Um, and so the, the Delta, the additional learning was just limited. So what they then decided to do was to build a virtual reality called car crafting, which were actually cars would drive around and create, uh, uh, predictive training data. Now, what is really interesting about that is that that is isn't a model. It is a model that creates predictive data. And this predictive is the actual value that is added to the equation here. And with this extra predictive data, they were able to improve their autonomous driving quite significantly. Uh, five years ago, their disengagement was, uh, raped was every, uh, 2000 miles on average. And, uh, last year, uh, five years later, it was every 30,000 miles on average, that's a 15 K improvement. And that wasn't driven by a mysterious model. It was driven by predictive data. >>Right, right. You know, that's interesting. I, I'm also a fan of trying to use data points that don't exist in the data sets. So it sounds like they were using more data data that was derived from other sources. And maybe the most simple format that I usually get started with was, you know, what, if I was looking at data from Glassdoor and I wanted to know if it was valid, if it was accurate, but of course there's going to be numbers in the age, field and salary and years of experience in different things. But what if the years of experience and age and academic level of someone no longer correlates to the salary yet that correlation component is not a piece of data that even lives in the column, the row, the cell. So I do think that there's a huge area for improvement and just advancement in the role data that we see in collect, but also the data science metrics, something like lift and correlation between the data points that really helped me certify and feel comfortable that this data makes sense. Otherwise it could just be numbers in the field >>Indeed. And, and this challenge of, of finding the data and focusing on the right subset of the data and manipulating it, uh, in the right, in a qualitatively right way is really something that has been with us for quite a number of years. There's a fabulous, uh, case, um, a few years back, uh, when, um, in Japan, when there was the suspicion that in Sumo wrestling, there was match fixing going on massive max fiction. Um, and, and so investigators came in and they took the data from the championship bouts and analyzed them and, uh, didn't find anything. And, uh, what was, what was really interesting is then later researchers came in and read the rules and regulations of Sumo wrestling and understood that it's not just the championship bouts that matter, but it's also sometimes the relegation matches that matter. And so then they started looking at those secondary matches that nobody looked at before and that subset of data, and they discovered there's massive match fixing going on. It's just, nobody looked at it because nobody just, as you said, that connection, uh, between th those various data sources or the sort of causal connectivity there. And so it's, it's, it's really crucial to understand, uh, that, uh, driving insight out of data, isn't a black box thing where you feed the data in and get it out. It really requires deep thinking about how to wire it up from the very beginning. >>No, that's an interesting story. I kind of wonder if the model in that case is almost the, the wrestlers themselves or the output, but definitely the, the data that goes into it. Um, yeah. So, I mean, do you see a path where organizations will achieve a hundred percent confidence? Because we all know there's a, I can't sleep at night factor, but there's also a case of what do I do today. It's, I'm probably not living in a perfect world. I might be sailing a boat across an ocean that already has a hole in it. So, you know, we can't turn everything off. We have to sort of patch the boat and sail it at the same time. Um, what do you think the, a good approaches for a large organization to improve their posture? >>You know, if you focus on perfection, you never, you never achieved that perfection a hundred percent perfection or so is never achievable. And if you want some radical change, then that that's admirable. But a lot of times it's very risky. It's a very risky proposition. So rather than doing that, there is a lot of low hanging fruit than that incremental, pragmatic step-by-step approach. If I can use an analogy from history, uh, we, we, we talk a lot about, um, the data revolution and before that, the industrial revolution, and when we think about the industrial revolution, we think about the steam engine, but the reality is that the steam engine, wasn't just one radical invention. In fact, there were a myriad of small incremental invade innovations over the course of a century that today we call the industrial revolution. And I think it's the various same thing when the data revolution where we don't have this one silver bullet that radically puts us into data Nirvana, but it is this incremental, pragmatic step-by-step change. It will get us closer. Um, pragmatic, can you speak in closer to where we want to be, even though there was always more work for us left? >>Yeah, that's interesting. Um, you know, that one hits home for me because we ultimately at Collibra take an incremental approach. We don't think there's a stop the world event. There's, you know, a way to learn from the past trends of our data to become incrementally smarter each day. And this kind of stops us from being in a binary project mode, right. Where we have to wait right. Something for six months and then reassess it and hope, you know, we kind of wonder if you're at 70% accuracy today is being at 71% better tomorrow, right? At least there's a measurable amount of improvement there. Uh, and it's a sort of a philosophical difference. And it reminds me of my banking days. When you say, uh, you know, past performance is no guarantee of future results. And, um, it's a nice disclaimer, you can put in everything, but I actually find it to be more true in data. >>We have all of these large data assets, whether it's terabytes or petabytes, or even if it's just gigabytes sitting there on all the datasets to learn from. And what I find in data is that the past historical values actually do tell us a lot about the future and we can learn from that to become incrementally smarter tomorrow. And there's really a lot of value sitting there in the historical data. And it tells me at least a lot about how to forecast the future. You know, one that's been sitting on the top of my mind recently, especially with COVID and the housing market a long time back, I competed with automation, valuation modeling, which basically means how well can you predict the price of a house? And, you know, that's always a fun one to do. And there's some big name brands out there that do that pretty well. >>Back then when I built those models, I would look at things like the size of the yard, the undulation of the land, uh, you know, whether a pool would award you more or less money for your house. And a lot of those factors were different than they are now. So those models ultimately have already changed. And now that we've seen post COVID people look for different things in housing and the prices have gone up. So we've seen a decline and then a dramatic increase. And then we've also seen things like land and pools become more valuable than they were in the housing model before, you know, what are you seeing here with models and data and how that's going to come together? And it's just, is it always going to change where you're going to have to constantly recalibrate both, you know, our understanding of the data and the models themselves? >>Well, indeed the, the problem of course is almost eternal. Um, oftentimes we have developed beautiful models that work really well. And then we're so wedded to this model or this particular kind of model. And we can fathom to give them up. I mean, if I think of my students, sometimes, you know, they, they, they, they have a model, they collect the data, then they run the analysis and, uh, it basically, uh, tells them that their model was wrong. They go out and they collect more data and more data and more data just to make sure that it isn't there, that, that, that their model is right. But the data tells them what the truth is that the model isn't right anymore that has context and goals and circumstances change the model needs to adapt. And we have seen it over and over again, not just in the housing market, but post COVID and in the COVID crisis, you know, a lot of the epidemiologists looked at life expectancy of people, but when you, when you look at people, uh, in the intensive care unit, uh, with long COVID, uh, suffering, uh, and in ICU and so on, you also need to realize, and many have that rather than life expectancy. >>You also need to look at life quality as a mother, uh, kind of dimension. And that means your model needs to change because you can't just have a model that optimizes on life expectancy anymore. And so what we need to do is to understand that the data and the changes in the data that they NAMIC of the data really is a thorn in our thigh of revisiting the model and thinking very critically about what we can do in order to adjust the model to the present situation. >>But with that, Victor, uh, I've really enjoyed our chat today. And, uh, do you have any final thoughts, comments, questions for me? >>Uh, you know, Kirk, I enjoyed it tremendously as well. Uh, I do think that, uh, that what is important, uh, to understand with data is that as there is no, uh, uh, no silver bullet, uh, and there is only incremental steps forward, this is not actually something to despair, but to give and be the source of great hope, because it means that not just tomorrow, but even the day after tomorrow and the day after the day after tomorrow, we still can make headway can make improvement and get better. >>Absolutely. I like the hopeful message I live every day to, uh, to make data a better place. And it is exciting as we see the advancements in what's possible on what's kind of on the forefront. Um, well with that, I really appreciate the chat and I would encourage anyone. Who's interested in this topic to attend a session later today on modern data quality, where I go through maybe five key flaws of the past and some of the pitfalls, and explain a little bit more about how we're using unsupervised learning to solve for future problems. Thanks Victor. Thank you, Kurt. >>Thanks, Kirk. And Victor, how incredible was that?

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a NetApp storage, right? So workloads are going to shift from the business applications, and these business applications, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SaaS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud then come compute, Kubernetes. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a ETAP storage, right? So workloads are going to shift from the business applications, and this business App again, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SAS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud and encompass compute whoever it is. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back everybody Jeff Frick here with the cube. This is a special week it's Grace Hopper week, and Grace Hopper is the best name in tech conferences. The celebration of women in computing, and we've been going there for years we're not there this year, but one of the themes that comes up over and over at Grace Hopper is women and girls need to see women in positions that they can envision themselves being in someday. That is a really important piece of the whole diversity conversation is can I see people that I can role model after and I just want to bring up something from a couple years back from 2016 when we were there, we were there with Mimi Valdez, Christina Deoja and Dr. Jeanette Epps, Dr. Jeanette Epps is the astronaut on the right. They were there talking about "The Hidden Figures" movie. If you remember it came out 2016, it was about Katherine Johnson and all the black women working at NASA. They got no credit for doing all the math that basically keep all the astronauts safe and they made a terrific movie about it. And Janet is going up on the very first Blue Origin Space Mission Next year. This was announced a couple of months ago, so again, phenomenal leadership, black lady astronaut, going to go into space and really provide a face for a lot of young girls that want to get into that and its clearly a great STEM opportunity. So we're excited to have four terrific women today that well also are the leaders that the younger women can look up to and follow their career. So we're excited to have them so we're just going to go around. We got four terrific guests, our first one is Annabel Chang, She is the Head of State Policy and Government Regulations at Waymo. Annabel great to see you, where are you coming in from today? >> from San Francisco >> Jeff: Awesome. Next up is Inamarie Johnson. She is the Chief People and Diversity Officer for Zendesk Inamarie, great to see you. Where are you calling in from today? >> Great to be here. I am calling in from Palos Verdes the state >> Jeff: awesome >> in Southern California. >> Jeff: Some of the benefits of a virtual sometimes we can, we couldn't do that without the power of the internet. And next up is Jennifer Cabalquinto she is the Chief Financial Officer of the Golden State Warriors. Jennifer, great to see you Where are you coming in from today? >> Well, I wish I was coming in from the Chase Center in San Francisco but I'm actually calling in from Santa Cruz California today. >> Jeff: Right, It's good to see you and you can surf a lot better down there. So that's probably not all bad. And finally to round out our panelists, Kate Hogan, she is the COO of North America for Accenture. Kate, great to see you as well. Where are you coming in from today? >> Well, it's good to see you too. I am coming in from the office actually in San Jose. >> Jeff: From the office in San Jose. All right, So let's get into it . You guys are all very senior, you've been doing this for a long time. We're in a kind of a crazy period of time in terms of diversity with all the kind of social unrest that's happening. So let's talk about some of your first your journeys and I want to start with you Annabel. You're a lawyer you got into lawyering. You did lawyering with Diane Feinstein, kind of some politics, and also the city of San Francisco. And then you made this move over to tech. Talk about that decision and what went into that decision and how did you get into tech? 'cause we know part of the problem with diversity is a pipeline problem. You came over from the law side of the house. >> Yes, and to be honest politics and the law are pretty homogenous. So when I made the move to tech, it was still a lot of the same, but what I knew is that I could be an attorney anywhere from Omaha Nebraska to Miami Florida. But what I couldn't do was work for a disruptive company, potentially a unicorn. And I seized that opportunity and (indistinct) Lyft early on before Ride Hailing and Ride Sharing was even a thing. So it was an exciting opportunity. And I joined right at the exact moment that made myself really meaningful in the organization. And I'm hoping that I'm doing the same thing right now at Waymo. >> Great, Inamarie you've come from one of my favorite stories I like to talk about from the old school Clorox great product management. I always like to joke that Silicon Valley needs a pipeline back to Cincinnati and Proctor and Gamble to get good product managers out here. You were in the classic, right? You were there, you were at Honeywell Plantronics, and then you jumped over to tech. Tell us a little bit about that move. Cause I'm sure selling Clorox is a lot different than selling the terrific service that you guys provide at Zendesk. I'm always happy when I see Zendesk in my customer service return email, I know I'm going to get taken care of. >> Oh wow, that's great. We love customers like you., so thank you for that. My journey is you're right from a fortune 50 sort of more portfolio type company into tech. And I think one of the reasons is because when tech is starting out and that's what Zendesk was a few five years back or so very much an early stage growth company, two things are top of mind, one, how do we become more global? And how do we make sure that we can go up market and attract enterprise grade customers? And so my experience having only been in those types of companies was very interesting for a startup. And what was interesting for me is I got to live in a world where there were great growth targets and numbers, things I had never seen. And the agility, the speed, the head plus heart really resonated with my background. So super glad to be in tech, but you're right. It's a little different than a consumer products. >> Right, and then Jennifer, you're in a completely different world, right? So you worked for the Golden State Warriors, which everybody knows is an NBA team, but I don't know that everyone knows really how progressive the Warriors are beyond just basketball in terms of the new Chase Center, all the different events that you guys put on it. And really the leadership there has decided we really want to be an entertainment company of which the Golden State Warrior basketball team has a very, very important piece, you've come from the entertainment industry. So that's probably how they found you, but you're in the financial role. You've always been in the financial role, not traditionally thought about as a lot of women in terms of a proportion of total people in that. So tell us a little bit about your experience being in finance, in entertainment, and then making this kind of hop over to, I guess Uber entertainment. I don't know even how you would classify the warriors. >> Sports entertainment, live entertainment. Yeah, it's interesting when the Warriors opportunity came up, I naturally said well no, I don't have any sports background. And it's something that we women tend to do, right? We self edit and we want to check every box before we think that we're qualified. And the reality is my background is in entertainment and the Warriors were looking to build their own venue, which has been a very large construction project. I was the CFO at Universal Studios Hollywood. And what do we do there? We build large attractions, which are just large construction projects and we're in the entertainment business. And so that sort of B to C was a natural sort of transition for me going from where I was with Universal Studios over to the Warriors. I think a finance career is such a great career for women. And I think we're finding more and more women entering it. It is one that you sort of understand your hills and valleys, you know when you're going to be busy and so you can kind of schedule around that. I think it's really... it provides that you have a seat at the table. And so I think it's a career choice that I think is becoming more and more available to women certainly more now than it was when I first started. >> Yeah, It's interesting cause I think a lot of people think of women naturally in human resources roles. My wife was a head of human resources back in the day, or a lot of marketing, but not necessarily on the finance side. And then Kate go over to you. You're one of the rare birds you've been at Accenture  for over 20 years. So you must like airplanes and travel to stay there that long. But doing a little homework for this, I saw a really interesting piece of you talking about your boss challenging you to ask for more work, to ask for a new opportunity. And I thought that was really insightful that you, you picked up on that like Oh, I guess it's incumbent on me to ask for more, not necessarily wait for that to be given to me, it sounds like a really seminal moment in your career. >> It was important but before I tell you that story, because it was an important moment of my career and probably something that a lot of the women here on the panel here can relate to as well. You mentioned airplanes and it made me think of my dad. My father was in the air force and I remember him telling stories when I was little about his career change from the air force into a career in telecommunications. So technology for me growing up Jeff was, it was kind of part of the dinner table. I mean it was just a conversation that was constantly ongoing in our house. And I also, as a young girl, I loved playing video games. We had a Tandy computer down in the basement and I remember spending too many hours playing video games down there. And so for me my history and my really at a young age, my experience and curiosity around tech was there. And so maybe that's, what's fueling my inspiration to stay at Accenture for as long as I have. And you're right It's been two decades, which feels tremendous, but I've had the chance to work across a bunch of different industries, but you're right. I mean, during that time and I relate with what Jennifer said in terms of self editing, right? Women do this and I'm no exception, I did this. And I do remember I'm a mentor and a sponsor of mine who called me up when I'm kind of I was at a pivotal moment in my career and he said you know Kate, I've been waiting for you to call me and tell me you want this job. And I never even thought about it. I mean I just never thought that I'd be a candidate for the job and let alone somebody waiting for me to kind of make the phone call. I haven't made that mistake again, (laughing) but I like to believe I learned from it, but it was an important lesson. >> It's such a great lesson and women are often accused of being a little bit too passive and not necessarily looking out for in salary negotiations or looking for that promotion or kind of stepping up to take the crappy job because that's another thing we hear over and over from successful people is that some point in their career, they took that job that nobody else wanted. They took that challenge that really enabled them to take a different path and really a different Ascension. And I'm just curious if there's any stories on that or in terms of a leader or a mentor, whether it was in the career, somebody that you either knew or didn't know that was someone that you got kind of strength from kind of climbing through your own, kind of career progression. Will go to you first Annabel. >> I actually would love to talk about the salary negotiations piece because I have a group of friends about that we've been to meeting together once a month for the last six years now. And one of the things that we committed to being very transparent with each other about was salary negotiations and signing bonuses and all of the hard topics that you kind of don't want to talk about as a manager and the women that I'm in this group with span all types of different industries. And I've learned so much from them, from my different job transitions about understanding the signing bonus, understanding equity, which is totally foreign to me coming from law and politics. And that was one of the most impactful tools that I've ever had was a group of people that I could be open with talking about salary negotiations and talking about how to really manage equity. Those are totally foreign to me up until this group of women really connected me to these topics and gave me some of that expertise. So that is something I strongly encourage is that if you haven't openly talked about salary negotiations before you should begin to do so. >> It begs the question, how was the sensitivity between the person that was making a lot of money and the person that wasn't? And how did you kind of work through that as a group for the greater good of everyone? >> Yeah, I think what's really eye opening is that for example, We had friends who were friends who were on tech, we had friends who were actually the entrepreneurs starting their own businesses or law firm, associates, law firm partners, people in PR, so we understood that there was going to be differences within industry and frankly in scale, but it was understanding even the tools, whether I think the most interesting one would be signing bonus, right? Because up until a few years ago, recruiters could ask you what you made and how do you avoid that question? How do you anchor yourself to a lower salary range or avoid that happening? I didn't know this, I didn't know how to do that. And a couple of women that had been in more senior negotiations shared ways to make sure that I was pinning myself to a higher salary range that I wanted to be in. >> That's great. That's a great story and really important to like say pin. it's a lot of logistical details, right? You just need to learn the techniques like any other skill. Inamarie, I wonder if you've got a story to share here. >> Sure. I just want to say, I love the example that you just gave because it's something I'm super passionate about, which is transparency and trust. Then I think that we're building that every day into all of our people processes. So sure, talk about sign on bonuses, talk about pay parody because that is the landscape. But a quick story for me, I would say is all about stepping into uncertainty. And when I coach younger professionals of course women, I often talk about, don't be afraid to step into the role where all of the answers are not vetted down because at the end of the day, you can influence what those answers are. I still remember when Honeywell asked me to leave the comfort of California and to come to the East coast to New Jersey and bring my family. And I was doing well in my career. I didn't feel like I needed to do that, but I was willing after some coaching to step into that uncertainty. And it was one of the best pivotal moment in my career. I didn't always know who I was going to work with. I didn't know the challenges and scope I would take on, but those were some of the biggest learning experiences and opportunities and it made me a better executive. So that's always my coaching, like go where the answers aren't quite vetted down because you can influence that as a leader. >> That's great, I mean, Beth Comstock former vice chair at GE, one of her keynotes I saw had a great line, get comfortable with being uncomfortable. And I think that its a really good kind of message, especially in the time we're living in with accelerated change. But I'm curious, Inamarie was the person that got you to take that commitment. Would you consider that a sponsor, a mentor, was it a boss? Was it maybe somebody not at work, your spouse or a friend that said go for it. What kind of pushed you over the edge to take that? >> It's a great question. It was actually the boss I was going to work for. He was the CHRO, and he said something that was so important to me that I've often said it to others. And he said trust me, he's like I know you don't have all the answers, I know we don't have this role all figured out, I know you're going to move your family, but if you trust me, there is a ton of learning on the other side of this. And sometimes that's the best thing a boss can do is say we will go on this journey together. I will help you figure it out. So it was a boss, but I think it was that trust and that willingness for him to stand and go alongside of me that made me pick up my family and be willing to move across the country. And we stayed five years and really, I am not the same executive because of that experience. >> Right, that's a great story, Jennifer, I want to go to you, you work for two owners that are so progressive and I remember when Joe Lacob came on the floor a few years back and was booed aggressively coming into a franchise that hadn't seen success in a very long time, making really aggressive moves in terms of personnel, both at the coaches and the players level, the GM level. But he had a vision and he stuck to it. And the net net was tremendous success. I wonder if you can share any of the stories, for you coming into that organization and being able to feel kind of that level of potential success and really kind of the vision and also really a focus on execution to make the vision real cause vision without execution doesn't really mean much. If you could share some stories of working for somebody like Joe Lacob, who's so visionary but also executes so very, very effectively. >> Yeah, Joe is, well I have the honor of working for Joe, for Rick Welts to who's our president. Who's living legend with the NBA with Peter Guber. Our leadership at the Warriors are truly visionary and they set audacious targets. And I would say from a story the most recent is, right now what we're living through today. And I will say Joe will not accept that we are not having games with fans. I agree he is so committed to trying to solve for this and he has really put the organization sort of on his back cause we're all like well, what do we do? And he has just refused to settle and is looking down every path as to how do we ensure the safety of our fans, the safety of our players, but how do we get back to live entertainment? And this is like a daily mantra and now the entire organization is so focused on this and it is because of his vision. And I think you need leaders like that who can set audacious goals, who can think beyond what's happening today and really energize the entire organization. And that's really what he's done. And when I talked to my peers and other teams in there they're talking about trying to close out their season or do these things. And they're like well, we're talking about, how do we open the building? And we're going to have fans, we're going to do this. And they look at me and they're like, what are you talking about? And I said, well we are so fortunate. We have leadership that just is not going to settle. Like they are just always looking to get out of whatever it is that's happening and fix it. So Joe is so committed His background, he's an epidemiologist major I think. Can you imagine how unique a background that is and how timely. And so his knowledge of just around the pandemic and how the virus is spread. And I mean it's phenomenal to watch him work and leverage sort of his business acumen, his science acumen and really think through how do we solve this. Its amazing. >> The other thing thing that you had said before is that you basically intentionally told people that they need to rethink their jobs, right? You didn't necessarily want to give them permission to get you told them we need to rethink their jobs. And it's a really interesting approach when the main business is just not happening, right? There's just no people coming through the door and paying for tickets and buying beers and hotdogs. It's a really interesting talk. And I'm curious, kind of what was the reception from the people like hey, you're the boss, you just figure it out or were they like hey, this is terrific that he pressed me to come up with some good ideas. >> Yeah, I think when all of this happened, we were resolved to make sure that our workforce is safe and that they had the tools that they needed to get through their day. But then we really challenged them with re imagining what the next normal is. Because when we come out of this, we want to be ahead of everybody else. And that comes again from the vision that Joe set, that we're going to use this time to make ourselves better internally because we have the time. I mean, we had been racing towards opening Chase Center and not having time to pause. Now let's use this time to really rethink how we're doing business. What can we do better? And I think it's really reinvigorated teams to really think and innovate in their own areas because you can innovate anything, right?. We're innovating how you pay payables, we're all innovating, we're rethinking the fan experience and queuing and lines and all of these things because now we have the time that it's really something that top down we want to come out of this stronger. >> Right, that's great. Kate I'll go to you, Julie Sweet, I'm a big fan of Julie Sweet. we went to the same school so go go Claremont. But she's been super aggressive lately on a lot of these things, there was a get to... I think it's called Getting to 50 50 by 25 initiative, a formal initiative with very specific goals and objectives. And then there was a recent thing in terms of doing some stuff in New York with retraining. And then as you said, military being close to your heart, a real specific military recruiting process, that's formal and in place. And when you see that type of leadership and formal programs put in place not just words, really encouraging, really inspirational, and that's how you actually get stuff done as you get even the consulting businesses, if you can't measure it, you can't improve it. >> Yeah Jeff, you're exactly right. And as Jennifer was talking, Julie is exactly who I was thinking about in my mind as well, because I think it takes strong leadership and courage to set bold bold goals, right? And you talked about a few of those bold goals and Julie has certainly been at the forefront of that. One of the goals we set in 2018 actually was as you said to achieve essentially a gender balance workforce. So 50% men, 50% women by 2025, I mean, that's ambitious for any company, but for us at the time we were 400,000 people. They were 500, 6,000 globally. So when you set a goal like that, it's a bold goal and it's a bold vision. And we have over 40% today, We're well on our path to get to 50%, I think by 2025. And I was really proud to share that goal in front of a group of 200 clients the day that it came out, it's a proud moment. And I think it takes leaders like Julie and many others by the way that are also setting bold goals, not just in my company to turn the dial here on gender equality in the workforce, but it's not just about gender equality. You mentioned something I think it's probably at as, or more important right now. And that's the fact that at least our leadership has taken a Stand, a pretty bold stand against social injustice and racism, >> Right which is... >> And so through that we've made some very transparent goals in North America in terms of the recruitment and retention of our black African American, Hispanic American, Latinex communities. We've set a goal to increase those populations in our workforce by 60% by 2025. And we're requiring mandatory training for all of our people to be able to identify and speak up against racism. Again, it takes courage and it takes a voice. And I think it takes setting bold goals to make a change and these are changes we're committed to. >> Right, that's terrific. I mean, we started the conversation with Grace Hopper, they put out an index for companies that don't have their own kind of internal measure to do surveys again so you can get kind of longitudinal studies over time and see how you're improving Inamarie, I want to go to you on the social justice thing. I mean, you've talked a lot about values and culture. It's a huge part of what you say. And I think that the quote that you use, if I can steal it is " no culture eats strategy for breakfast" and with the social injustice. I mean, you came out with special values just about what Zendesk is doing on social injustice. And I thought I was actually looking up just your regular core mission and value statement. And this is what came up on my Google search. So I wanted to A, you published this in a blog in June, taking a really proactive stand. And I think you mentioned something before that, but then you're kind of stuck in this role as a mind reader. I wonder if you can share a little bit of your thoughts of taking a proactive stand and what Zendesk is doing both you personally, as well as a company in supporting this. And then what did you say as a binder Cause I think these are difficult kind of uncharted waters on one hand, on the other hand, a lot of people say, hello, this has been going on forever. You guys are just now seeing cellphone footage of madness. >> Yeah Wow, there's a lot in there. Let me go to the mind reader comments, cause people are probably like, what is that about? My point was last December, November timing. I've been the Chief People Officer for about two years And I decided that it really was time with support from my CEO that Zendesk have a Chief Diversity Officer sitting in at the top of the company, really putting a face to a lot of the efforts we were doing. And so the mind reader part comes in little did I know how important that stance would become, in the may June Timing? So I joked that, it almost felt like I could have been a mind reader, but as to what have we done, a couple of things I would call out that I think are really aligned with who we are as a company because our culture is highly threaded with the concept of empathy it's been there from our beginning. We have always tried to be a company that walks in the shoes of our customers. So in may with the death of George Floyd and the world kind of snapping and all of the racial injustice, what we said is we wanted to not stay silent. And so most of my postings and points of view were that as a company, we would take a stand both internally and externally and we would also partner with other companies and organizations that are doing the big work. And I think that is the humble part of it, we can't do it all at Zendesk, we can't write all the wrongs, but we can be in partnership and service with other organizations. So we used funding and we supported those organizations and partnerships. The other thing that I would say we did that was super important along that empathy is that we posted space for our employees to come together and talk about the hurt and the pain and the experiences that were going on during those times and we called those empathy circles. And what I loved is initially, it was through our mosaic community, which is what we call our Brown and black and persons of color employee resource group. But it grew into something bigger. We ended up doing five of these empathy circles around the globe and as leadership, what we were there to do is to listen and stand as an ally and support. And the stories were life changing. And the stories really talked about a number of injustice and racism aspects that are happening around the world. And so we are committed to that journey, we will continue to support our employees, we will continue to partner and we're doing a number of the things that have been mentioned. But those empathy circles, I think were definitely a turning point for us as an organization. >> That's great, and people need it right? They need a place to talk and they also need a place to listen if it's not their experience and to be empathetic, if you just have no data or no knowledge of something, you need to be educated So that is phenomenal. I want to go to you Jennifer. Cause obviously the NBA has been very, very progressive on this topic both as a league, and then of course the Warriors. We were joking before. I mean, I don't think Steph Curry has ever had a verbal misstep in the history of his time in the NBA, the guy so eloquent and so well-spoken, but I wonder if you can share kind of inside the inner circle in terms of the conversations, that the NBA enabled right. For everything from the jerseys and going out on marches and then also from the team level, how did that kind of come down and what's of the perception inside the building? >> Sure, obviously I'm so proud to be part of a league that is as progressive and has given voice and loud, all the teams, all the athletes to express how they feel, The Warriors have always been committed to creating a diverse and equitable workplace and being part of a diverse and equitable community. I mean that's something that we've always said, but I think the situation really allowed us, over the summer to come up with a real formal response, aligning ourselves with the Black Lives Matter movement in a really meaningful way, but also in a way that allows us to iterate because as you say, it's evolving and we're learning. So we created or discussed four pillars that we wanted to work around. And that was really around wallet, heart, beat, and then tongue or voice. And Wallet is really around putting our money where our mouth is, right? And supporting organizations and groups that aligned with the values that we were trying to move forward. Heart is around engaging our employees and our fan base really, right? And so during this time we actually launched our employee resource groups for the first time and really excited and energized about what that's doing for our workforce. This is about promoting real action, civic engagement, advocacy work in the community and what we've always been really focused in a community, but this really hones it around areas that we can all rally around, right? So registration and we're really focused on supporting the election day results in terms of like having our facilities open to all the electorate. So we're going to have our San Francisco arena be a ballot drop off, our Oakland facilities is a polling site, Santa Cruz site is also a polling location, So really promoting sort of that civic engagement and causing people to really take action. heart is all around being inclusive and developing that culture that we think is really reflective of the community. And voice is really amplifying and celebrating one, the ideas, the (indistinct) want to put forth in the community, but really understanding everybody's culture and really just providing and using the platform really to provide a basis in which as our players, like Steph Curry and the rest want to share their own experiences. we have a platform that can't be matched by any pedigree, right? I mean, it's the Warriors. So I think really getting focused and rallying around these pillars, and then we can iterate and continue to grow as we define the things that we want to get involved in. >> That's terrific. So I have like pages and pages and pages of notes and could probably do this for hours and hours, but unfortunately we don't have that much time we have to wrap. So what I want to do is give you each of you the last word again as we know from this problem, right? It's not necessarily a pipeline problem, it's really a retention problem. We hear that all the time from Girls in Code and Girls in Tech. So what I'd like you to do just to wrap is just a couple of two or three sentences to a 25 year old, a young woman sitting across from you having coffee socially distanced about what you would tell her early in the career, not in college but kind of early on, what would the be the two or three sentences that you would share with that person across the table and Annabel, we'll start with you. >> Yeah, I will have to make a pitch for transportation. So in transportation only 15% of the workforce is made up of women. And so my advice would be that there are these fields, there are these opportunities where you can make a massive impact on the future of how people move or how they consume things or how they interact with the world around them. And my hope is that being at Waymo, with our self driving car technology, that we are going to change the world. And I am one of the initial people in this group to help make that happen. And one thing that I would add is women spend almost an hour a day, shuttling their kids around, and we will give you back that time one day with our self driving cars so that I'm a mom. And I know that that is going to be incredibly powerful on our daily lives. >> Jeff: That's great. Kate, I think I might know what you're already going to say, but well maybe you have something else you wanted to say too. >> I don't know, It'll be interesting. Like if I was sitting across the table from a 25 year old right now I would say a couple of things first I'd say look intentionally for a company that has an inclusive culture. Intentionally seek out the company that has an inclusive culture, because we know that companies that have inclusive cultures retain women in tech longer. And the companies that can build inclusive cultures will retain women in tech, double, double the amount that they are today in the next 10 years. That means we could put another 1.4 million women in tech and keep them in tech by 2030. So I'd really encourage them to look for that. I'd encouraged them to look for companies that have support network and reinforcements for their success, and to obviously find a Waymo car so that they can not have to worry where kids are on for an hour when you're parenting in a few years. >> Jeff: I love the intentional, it's such a great word. Inamarie, >> I'd like to imagine that I'm sitting across from a 25 year old woman of color. And what I would say is be authentically you and know that you belong in the organization that you are seeking and you were there because you have a unique perspective and a voice that needs to be heard. And don't try to be anything that you're not, be who you are and bring that voice and that perspective, because the company will be a better company, the management team will be a better management team, the workforce will be a better workforce when you belong, thrive and share that voice. >> I love that, I love that. That's why you're the Chief People Officer and not Human Resources Officer, cause people are not resources like steel and cars and this and that. All right, Jennifer, will go to you for the wrap. >> Oh my gosh, I can't follow that. But yes, I would say advocate for yourself and know your value. I think really understanding what you're worth and being willing to fight for that is critical. And I think it's something that women need to do more. >> Awesome, well again, I wish we could go all day, but I will let you get back to your very, very busy day jobs. Thank you for participating and sharing your insight. I think it's super helpful. And there and as we said at the beginning, there's no better example for young girls and young women than to see people like you in leadership roles and to hear your voices. So thank you for sharing. >> Thank you. >> All right. >> Thank you. >> Okay thank you. >> Thank you >> All right, so that was our diversity panel. I hope you enjoyed it, I sure did. I'm looking forward to chapter two. We'll get it scheduled as soon as we can. Thanks for watching. We'll see you next time. (upbeat music)

(upbeat music) >> Hi, and welcome to theCUBE, the leading source for insights into the world of technology and innovation. I'm your host Donald Klein, and today's topic is the market for autonomous vehicles and the ecosystem suppliers looking to tap into this brave new world of autonomous capabilities in our daily commute. To have this conversation I'm joined by Rudy Burger, managing partner at Woodside Capital. Rudy, welcome to the show. >> Thanks Don, it's great to be here. >> Great, so look, why don't we start off Rudy, why don't you tell us a little bit about Woodside Capital and your role there? >> Great, so I founded Woodside Capital about 20 years ago having started five different companies of my own, one of which I took public. We are a specialist M&A advisor. We work with so-called growth stage often venture-backed companies and help them find buyers that are usually much larger public companies. Our clients are usually US or European companies and we find buyers in the US, Europe, or Asia. >> Excellent, excellent, okay. And why don't you talk a little bit about your kind of specialty areas? >> So I focused my career, and certainly the work at Woodside Capital, on imaging technologies and as an enabling technology, and the products and markets that are enabled by imaging and increasingly computer vision. So nowadays that is autonomous vehicles, consumer technology, security surveillance, and digital health. So enabling technologies, the computer vision is the theme that binds those together. >> Okay, well, the thing that's on everybody's mind these days is autonomous vehicles, when are we going to get them? Very high profile for sure. Before the show we talking about the kind of two key ingredients to making this happen, the AI software which is kind of the brains of the operation and then also the sensors which enable all of the AI. So why don't we talk about the sensor world first, okay? Lot of discussion about there, so sort of does the brave new world of vehicles need lidar? Does it not need lidar? Are there other types of sensors coming along? What's your sense of that market and how it's looking for all of the different players in it? >> So, Don, I look at it from a sort of fairly basic standpoint. Humans have two very capable image sensors and a very powerful processor, and the degree to which the automotive manufacturers and so-called Robo-Taxi developers have decided it's necessary to sprinkle every sensor known to man, and I'm talking lidar, radar, ultrasound, thermal, and of course cameras, is to some extent a degree to which, you know, image sensors are not as good as our eyes today. Now, there are some areas in which we will probably always have technology as a help. For example, humans are not very good at seeing in the dark whereas a thermal technology can do that very well. But my overall belief is that it's never a good idea to bet against an incumbent technology, and in this case I'm talking about so-called CMOS image sensors which are the sensor that goes into pretty much every camera in the world now. It's never a good idea to bet against the incumbent technology being able to scale into a new market. Every time people have done that, they've been wrong. Back in the early days the debate was whether CMOS image sensors would ever be good enough to replace CCDs as the sensor technology, and of course now, you know, everything uses CMOS image sensors. In other markets there was a long period of time in which people were thinking that LCD panels would never be large enough to replace, you know, for television, for example, 50 inch and so forth. It was never going to happen, so we needed plasma TVs, we needed rear-projection TVs. But slowly but surely the incumbent technology, LCDs, expanded to that market. So my belief is that CMOS image sensors will evolve to a point at which they will replace the need for lidar in most applications. >> Interesting, so that's a very controversial statement, right? Because you've certainly seen a lot of emphasis on the development of new generation lidar capability. >> Over 100 lidar companies started over the last three, four years, and of course many of them will not be happy to hear me say that. There are two distinct markets and one is the so-called Robo-Taxi market, and the other is more of the consumer vehicle ADAS market, and I think we need to think about those separately because the economics behind both are very different. If you look at the Robo-Taxi market, those vehicles tend to be much more expensive and are relatively price-insensitive. So if they can improve safety a little bit by putting a lidar on there, you know, great, let's do it, multiple lidars because these vehicles will be in operation 24 by seven, and if each vehicle costs 200,000, $250,000, fine. When we talk about the mass market for automobiles, type of car that you and I might go down and buy, very different thing. And, you know, auto makers sweat the pennies, and so putting a one or $200 lidar in a vehicle, big decision. And to the extent that they can replace the need for that lidar with a much less expensive camera system, that's what they'll do. Bear in mind that Mobileye, which has been the biggest success story, acquired by Intel for $13.5 billion, second largest acquisition Intel ever made, they for the most part still run on one camera, forward-looking camera. That's it, no radar, no lidar, no thermal, one camera. So the clever use of image processing, computer vision, and one image sensor can do a great deal. >> Interesting, okay. Well, so I want to talk about the software in just a second, but just to kind of finish this point, so if you were advising a sensor company that's developing some next gen capabilities, whether lidar or other related technologies, is the point you're making here that there are certain segments of this industry which are going to be more attractive to your technology than others? >> Absolutely, yes. I mean, the first thing to recognize is that the automotive industry has never really been a particularly comfortable fit with the economics and timeline of venture capital. VCs need to invest and recoup and redeploy back to their LPs on an eight-year cycle. But the automotive industry moves quite slowly, perhaps Tesla are excepted, and what the first piece of advice I would give these companies is it's probably going to be three, four, five years before, even if you have the right technology, before that technology really starts generating any significant volume and revenue. So for many venture-backed companies, that's too long. So the first piece of advice is find pockets of revenue, right, beachheads if you will, where you can land your technology and start generating revenue before you get to the automotive market. And many of these lidar companies we just talked about are not going to last long enough to get to the automotive market because not only does the automotive market move slowly but the autonomous vehicle market keeps on getting pushed out to the right as the industry realizes that this is a big, hairy problem. And so I would say, what is it that your technology can do an order of magnitude better than any other technology? Focus on that and find some opportunities for revenue outside the automotive industry that will sustain the company on its way to the holy grail. >> Interesting, yeah, so find that alternative revenue source to get you to base camp, and then when the market's ready, climb that Everest to-- >> I've seen so many companies basically go out of business because they've set their sights on either the automotive market, and it's go for broke. We're not interested in, all these other things are distractions. You know, entrepreneurs don't have a plan B. Or this. We're going to get our technology into a smartphone, that's it. And there are possibly some other opportunities but it takes so long and it's so difficult to get your technology into a smartphone that they go out of business before they ever get to that point. >> Interesting, okay. So good advice for people looking to kind of apply their technology in this kind of a very difficult market, right, very complicated market. All right, well, then let's switch to the other side of it. So we were kind of talking about the key ingredients, right? Sensors but also AI and the software around that, okay, and there are some very big players developing the software. Tesla's had their Autonomy Day where they've showcased their technology. You've obviously got Google with their capabilities developing software. How do you make sense of this overall landscape because we do see a lot of smaller providers also trying to develop software here. >> So the first thing that I find fascinating about the automotive industry is that for the most part there is no software market. There's perhaps one exception of any scale, that's BlackBerry that sells the QNX software. They found a point within the entertainment console where they can license their software. But for all of the development and capital invested into automotive software, nobody is actually generating revenue, making a living, by licensing software. And one of the main reasons for that is that, you know, the automotive market, really since inception, has been a hardware business. This is a business of bending sheet metal, internal combustion engines, and software has really not played that big a role up until relatively recently. So even those companies that do have software technology have ended up selling it into the automotive supply chain as a piece of silicon, embedded on a piece of silicon, not as, you know, here's my software on a USB stick, right? I think that the whole software licensing model hasn't so far fit well, fit comfortably, with the automotive industry. And the other reason is that there's no standard platform. If I were to develop a piece of software, I can, in the PC industry, I can develop for Windows, I can develop for Mac, I can develop for an iPhone. There's no such thing in the automotive industry, and particularly in this new world of autonomous vehicles there is no standard platform. There are many different processors, Nvidia has staked an early claim there. And the reason that most of the companies developing autonomous vehicle technology have developed the so-called full-stack solution, everything from code running on the processor, integrated through the sensors and so forth, is for that reason, there is no standard platform. So each company has developed the whole solution for themselves, and there are many of them around here that have raised hundreds of millions of dollars, some cases billions of dollars, for that purpose. So there is, today, no software market for automotive in the same way that we think about it in other industries. >> Understood, understood. But in terms of the companies that are actually pushing the envelope on these kind of capabilities, right, so we're taking the best of AI, we're applying it to big data sets, and then hopefully being able to extract that to create capabilities for these vehicles, right? What's your sense of how far that's come along in-- >> Well, it's come a long way but, here I'm going to push the boat out a little bit. I don't believe that the so-called deep learning technology, which is the current state of the art for AI, it's the technology that has allowed computers to beat humans at chess, at Go, I don't think that that flavor of AI, that approach to AI, is ever going to get us to safe enough autonomous vehicles. And that's because it works extremely well in fairly well-bounded rules, rule-bounded games or any scenario like that, but can you imagine trying to teach your 16-year-old how to drive by showing them images of every situation that they might encounter, right? Impossible. It's an infinite, it's not a well-bounded set. And that's so difficult because we really haven't developed the technology to allow computers to learn, to have things like common sense, to infer, you know, well, this happened, so this is likely to happen. So I think we are going to need a whole new breakthrough in AI before we get to what is generally considered safe enough vehicles. >> Interesting, well then, maybe if we kind of apply your previous thought about sort of Robo-Taxis as maybe being the segment where you're going to see the most use of these newer sensor technologies. >> Rudy: Near term, yes. >> Exactly, what about maybe, is that sort of the same rules apply there for maybe the AI providers, that they're-- >> I think so and that's why they're all focused on that. I mean, from Uber to Waymo, they've all made the same calculation which is if you're running a fleet of vehicles, and so for example in Uber's case, the driver takes 80% of the fare and only 20% goes back to Uber, but if you can replace the driver with a computer, you can keep that vehicle on the road 24 by seven and you can keep 100% of the revenue. You don't need to pay the computer. So that's the calculus that they're all going through. But I think that many of them are making a fundamental mistake and I predicted recently that I think Uber, my prediction for 2020 is that Uber is going to divest its autonomous vehicle business and get back to the business that it should be focused on. Uber generates about $14 billion a year in gross revenue, so 20% of that, which is the piece that Uber keeps after the drivers take their 80, is what, 2.8 billion. Uber should be able to be an extremely profitable business on 2.8 billion of net revenue, but they're spending a huge chunk of money every year on R&D. Now, I would argue that Hertz and Avis have successful businesses. They're in the service, they're in the transportation business, but they didn't decide that they had to build their own cars in order to be in that business. My view, personal view, is that what Uber should be doing is saying, that's not our business, right? We are the world's best at managing this sort of peer-to-peer network crowdsourced transportation, if you will. And when some company, some Silicon Valley startup, comes out with safe enough technology, great, we'll use it, but we don't have to develop that ourselves. >> Well then, maybe just to play devil's advocate here for a second, what about it's a Robo-Taxi-type technologies being applied in bounded areas within metropolitan areas where the rules-- >> That's where it will start. >> Could be more-- >> I think that's where it will start, but I think part of the problem is that we have, perhaps in part due to all of the media hype around autonomous vehicles, we've been misdirected to thinking about autonomous vehicles as a replacement for the car we drive to work every day and I think that's the wrong way to think about it. I think that autonomous vehicles are going to show up in the market as an extension of public transportation. Right, you know, I get off the train and there's an autonomous vehicle waiting to take me for the last couple of miles to my office. >> And those last couple of miles would be sort of a regulated space. >> Rudy: May well be. >> Where the AI is more than capable of functioning. >> Right, and that, you know, yes. And so it's better to think about autonomous vehicles as not being a revolutionary technology but much more of an evolutionary technology. And in fact, most of these technologies are showing up in so-called ADAS technologies which are designed to make driving your regular car safer, lane assist, keeping you a safe distance. >> Donald: Maybe just explain that word, ADAS, and what that means. >> So ADAS stands for automated driver-assistance systems. So one of the first was cruise control, right, everybody's familiar with cruise control. And so to some extent ADAS is just building on cruise control. In addition to maintaining a constant speed, you can now stay in the lane. In addition to maintaining a constant speed, it will now automatically slow down if you get too close to the car in front. And so you can see ADAS as, you know, collision avoidance and so forth, not full autonomy, still have to have a driver in the driver's seat, but evolving year by year until one year we wake up and, yep, my car will actually drive me all the way from home to work without me intervening. Right, it's going to happen in that way. >> So incremental improvements. >> Incremental improvement. >> To ADAS as opposed to kind of revolution of autonomy. >> An overnight sensation. >> Yeah, right, coming from nowhere. Okay, understood. Well then, let's pivot from that then, okay. So let's talk about the automotive industry as a whole and sort of your thoughts on how this is all going to play out. >> Yeah, so there are some very interesting dynamics playing out in the automotive industry. Firstly, as good news, as a result of all of this money and innovation in the automotive industry, Detroit's actually coming back. I go there once or twice a year and you can feel the economy coming back in Detroit, but it's not going to come back around, you know, bending sheet metal. And the challenge that the automotive companies have is so much of their infrastructure and expertise has been built on construction, building a car, production lines to bend the metal, install the engine, and the internal combustion engine itself. And by complete coincidence, to some extent, we've got this confluence of all of these autonomous technologies and electric vehicles happening at the same time. Electric vehicles are much easier to make than internal combustion engines. Far fewer parts. It's one of the reasons that China has spun up about 20 different electric vehicle companies recently. So I think that long term, my prediction is that the automobile industry will go the same way that the personal computer industry went. When the PC first, you know, it was born by IBM, or Apple in some sense before that. There were dozens of companies producing different PCs and it was very much, they were expensive products, and, you know, relatively unusual. As the industry matured, the supply chains matured, and it became apparent there were really only two companies that were making a lot of money out of the PC industry. The companies that developed the software, operating system, and the companies that developed the processor, and all of the manufacturing went over to, in the PC's case, in Taiwan, right? And I think that exactly the same thing is going to happen with the automotive industry. Tesla today still actually makes cars, but I don't see them long term being in the car business because they're really a technology company. It's the reason I don't think Apple is ever going to get into the car industry. They make fantastic margins selling computer products. The gross margin selling a car, it's miserable. It can be single digits or teens. That would completely tank Apple's blended gross margin. So my prediction for the industry is there will be a few small pockets of very profitable businesses, particularly around the operating system, by which I mean the intelligence or the AI intelligence, and then the processor, whether it's a Qualcomm processor or a Nvidia processor or an Intel processor. And as with the PC industry, most of the profit will go there and most of the manufacturing will end up getting outsourced because that's not the value-add, you know, bending metal and so forth. >> Interesting, well, so in the kind of compute market today, right, we have this notion of sort of cloud-native, right, okay, and that many of the companies that are developing apps as relying on cloud-native infrastructure have a kind of technology lead that's going to be hard for some of the legacy providers to actually catch up on. Now, other people say that that's not necessarily the case and et cetera, right? Can you make the same argument for the electric car market, that some of the electric-natives might have a kind of sustainable advantage here? >> I should've added, today the cloud infrastructure companies, cloud services, SaaS companies, in the PC world, you know, very profitable, and I can see a similar cloud services model developing for the automotive industry. However, other than Tesla, it's very difficult to change the automotive channel to support that. I'll give you one example. Everyone that owns a Tesla is very used to the idea that, sometimes on a daily basis, a new bunch of software, operating system software, is downloaded overnight to your vehicle. You wake up in the morning and some new feature's been turned on, right? Tesla can do that because they bypass the entire dealership channel that has a complete lock on the rest of the industry. So for example, if GM wants to do the same thing as Tesla and do sort of what's called over-the-air, OTA, updates, software updates, they can't do that because their contract with the dealership network states that if there is service to be done on the vehicle, the vehicle has to be brought back to the dealership, and the dealerships consider updating the software on the vehicle as service. So their contract with the dealers actually prevent them from doing something that basic. So it's not just a technology issue. The whole channel and way vehicles get sold is going to have to change. >> Interesting, so that's the advantage that some of the new generation of vehicle manufacturers-- >> I would say that Tesla has a five year lead, technology lead, because they, like Apple, are vertically integrated. They're doing everything from user interface, fit and function, all the way down to the semiconductor. They're developing their own semiconductors now. So they have become a fearsome competitor in the electronic vehicle space because they've been doing it for longer than the other major auto companies. They've figured out a lot of the, you know, tricks and techniques of how to extend mileage and so forth. And so they have a substantial lead in the industry at this point, despite the fact that over the next 12, 18 months, every automotive company is going to be coming out with their own flavor of electronic vehicle. >> So then it's more than just about having electric drivetrains, et cetera, right? It's about the whole suite of capabilities. >> It's a systems engineering challenge. >> Interesting, okay. All right, well Rudy, we're going to have to leave it there, okay, but I think everything you've told us is, it sounds like some good news for some of the Tesla stock holders at the moment. >> I think so. >> Okay, well. (laughs) We'll pass on making an opinion about that, but great conversation, thank you for your insights. Okay, this is Donald Klein, host of theCUBE, here with Rudy Burger, managing partner at Woodside Capital. >> Rudy: Great, thank you, Don. (upbeat music)

(upbeat music) >> Hi, and welcome to theCUBE, the leading source for insights into the world of technology and innovation. I'm your host Donald Klein, and today's topic is the market for autonomous vehicles and the ecosystem suppliers looking to tap into this brave new world of autonomous capabilities in our daily commute. To have this conversation I'm joined by Rudy Burger, managing partner at Woodside Capital. Rudy, welcome to the show. >> Thanks Don, it's great to be here. >> Great, so look, why don't we start off Rudy, why don't you tell us a little bit about Woodside Capital and your role there? >> Great, so I founded Woodside Capital about 20 years ago having started five different companies of my own, one of which I took public. We are a specialist M&A advisor. We work with so-called growth stage often venture-backed companies and help them find buyers that are usually much larger public companies. Our clients are usually US or European companies and we find buyers in the US, Europe, or Asia. >> Excellent, excellent, okay. And why don't you talk a little bit about your kind of specialty areas? >> So I focused my career, and certainly the work at Woodside Capital, on imaging technologies and as an enabling technology, and the products and markets that are enabled by imaging and increasingly computer vision. So nowadays that is autonomous vehicles, consumer technology, security surveillance, and digital health. So enabling technologies, the computer vision is the theme that binds those together. >> Okay, well, the thing that's on everybody's mind these days is autonomous vehicles, when are we going to get them? Very high profile for sure. Before the show we talking about the kind of two key ingredients to making this happen, the AI software which is kind of the brains of the operation and then also the sensors which enable all of the AI. So why don't we talk about the sensor world first, okay? Lot of discussion about there, so sort of does the brave new world of vehicles need lidar? Does it not need lidar? Are there other types of sensors coming along? What's your sense of that market and how it's looking for all of the different players in it? >> So, Don, I look at it from a sort of fairly basic standpoint. Humans have two very capable image sensors and a very powerful processor, and the degree to which the automotive manufacturers and so-called Robo-Taxi developers have decided it's necessary to sprinkle every sensor known to man, and I'm talking lidar, radar, ultrasound, thermal, and of course cameras, is to some extent a degree to which, you know, image sensors are not as good as our eyes today. Now, there are some areas in which we will probably always have technology as a help. For example, humans are not very good at seeing in the dark whereas a thermal technology can do that very well. But my overall belief is that it's never a good idea to bet against an incumbent technology, and in this case I'm talking about so-called CMOS image sensors which are the sensor that goes into pretty much every camera in the world now. It's never a good idea to bet against the incumbent technology being able to scale into a new market. Every time people have done that, they've been wrong. Back in the early days the debate was whether CMOS image sensors would ever be good enough to replace CCDs as the sensor technology, and of course now, you know, everything uses CMOS image sensors. In other markets there was a long period of time in which people were thinking that LCD panels would never be large enough to replace, you know, for television, for example, 50 inch and so forth. It was never going to happen, so we needed plasma TVs, we needed rear-projection TVs. But slowly but surely the incumbent technology, LCDs, expanded to that market. So my belief is that CMOS image sensors will evolve to a point at which they will replace the need for lidar in most applications. >> Interesting, so that's a very controversial statement, right? Because you've certainly seen a lot of emphasis on the development of new generation lidar capability. >> Over 100 lidar companies started over the last three, four years, and of course many of them will not be happy to hear me say that. There are two distinct markets and one is the so-called Robo-Taxi market, and the other is more of the consumer vehicle ADAS market, and I think we need to think about those separately because the economics behind both are very different. If you look at the Robo-Taxi market, those vehicles tend to be much more expensive and are relatively price-insensitive. So if they can improve safety a little bit by putting a lidar on there, you know, great, let's do it, multiple lidars because these vehicles will be in operation 24 by seven, and if each vehicle costs 200,000, $250,000, fine. When we talk about the mass market for automobiles, type of car that you and I might go down and buy, very different thing. And, you know, auto makers sweat the pennies, and so putting a one or $200 lidar in a vehicle, big decision. And to the extent that they can replace the need for that lidar with a much less expensive camera system, that's what they'll do. Bear in mind that Mobileye, which has been the biggest success story, acquired by Intel for $13.5 billion, second largest acquisition Intel ever made, they for the most part still run on one camera, forward-looking camera. That's it, no radar, no lidar, no thermal, one camera. So the clever use of image processing, computer vision, and one image sensor can do a great deal. >> Interesting, okay. Well, so I want to talk about the software in just a second, but just to kind of finish this point, so if you were advising a sensor company that's developing some next gen capabilities, whether lidar or other related technologies, is the point you're making here that there are certain segments of this industry which are going to be more attractive to your technology than others? >> Absolutely, yes. I mean, the first thing to recognize is that the automotive industry has never really been a particularly comfortable fit with the economics and timeline of venture capital. VCs need to invest and recoup and redeploy back to their LPs on an eight-year cycle. But the automotive industry moves quite slowly, perhaps Tesla are excepted, and what the first piece of advice I would give these companies is it's probably going to be three, four, five years before, even if you have the right technology, before that technology really starts generating any significant volume and revenue. So for many venture-backed companies, that's too long. So the first piece of advice is find pockets of revenue, right, beachheads if you will, where you can land your technology and start generating revenue before you get to the automotive market. And many of these lidar companies we just talked about are not going to last long enough to get to the automotive market because not only does the automotive market move slowly but the autonomous vehicle market keeps on getting pushed out to the right as the industry realizes that this is a big, hairy problem. And so I would say, what is it that your technology can do an order of magnitude better than any other technology? Focus on that and find some opportunities for revenue outside the automotive industry that will sustain the company on its way to the holy grail. >> Interesting, yeah, so find that alternative revenue source to get you to base camp, and then when the market's ready, climb that Everest to-- >> I've seen so many companies basically go out of business because they've set their sights on either the automotive market, and it's go for broke. We're not interested in, all these other things are distractions. You know, entrepreneurs don't have a plan B. Or this. We're going to get our technology into a smartphone, that's it. And there are possibly some other opportunities but it takes so long and it's so difficult to get your technology into a smartphone that they go out of business before they ever get to that point. >> Interesting, okay. So good advice for people looking to kind of apply their technology in this kind of a very difficult market, right, very complicated market. All right, well, then let's switch to the other side of it. So we were kind of talking about the key ingredients, right? Sensors but also AI and the software around that, okay, and there are some very big players developing the software. Tesla's had their Autonomy Day where they've showcased their technology. You've obviously got Google with their capabilities developing software. How do you make sense of this overall landscape because we do see a lot of smaller providers also trying to develop software here. >> So the first thing that I find fascinating about the automotive industry is that for the most part there is no software market. There's perhaps one exception of any scale, that's BlackBerry that sells the QNX software. They found a point within the entertainment console where they can license their software. But for all of the development and capital invested into automotive software, nobody is actually generating revenue, making a living, by licensing software. And one of the main reasons for that is that, you know, the automotive market, really since inception, has been a hardware business. This is a business of bending sheet metal, internal combustion engines, and software has really not played that big a role up until relatively recently. So even those companies that do have software technology have ended up selling it into the automotive supply chain as a piece of silicon, embedded on a piece of silicon, not as, you know, here's my software on a USB stick, right? I think that the whole software licensing model hasn't so far fit well, fit comfortably, with the automotive industry. And the other reason is that there's no standard platform. If I were to develop a piece of software, I can, in the PC industry, I can develop for Windows, I can develop for Mac, I can develop for an iPhone. There's no such thing in the automotive industry, and particularly in this new world of autonomous vehicles there is no standard platform. There are many different processors, Nvidia has staked an early claim there. And the reason that most of the companies developing autonomous vehicle technology have developed the so-called full-stack solution, everything from code running on the processor, integrated through the sensors and so forth, is for that reason, there is no standard platform. So each company has developed the whole solution for themselves, and there are many of them around here that have raised hundreds of millions of dollars, some cases billions of dollars, for that purpose. So there is, today, no software market for automotive in the same way that we think about it in other industries. >> Understood, understood. But in terms of the companies that are actually pushing the envelope on these kind of capabilities, right, so we're taking the best of AI, we're applying it to big data sets, and then hopefully being able to extract that to create capabilities for these vehicles, right? What's your sense of how far that's come along in-- >> Well, it's come a long way but, here I'm going to push the boat out a little bit. I don't believe that the so-called deep learning technology, which is the current state of the art for AI, it's the technology that has allowed computers to beat humans at chess, at Go, I don't think that that flavor of AI, that approach to AI, is ever going to get us to safe enough autonomous vehicles. And that's because it works extremely well in fairly well-bounded rules, rule-bounded games or any scenario like that, but can you imagine trying to teach your 16-year-old how to drive by showing them images of every situation that they might encounter, right? Impossible. It's an infinite, it's not a well-bounded set. And that's so difficult because we really haven't developed the technology to allow computers to learn, to have things like common sense, to infer, you know, well, this happened, so this is likely to happen. So I think we are going to need a whole new breakthrough in AI before we get to what is generally considered safe enough vehicles. >> Interesting, well then, maybe if we kind of apply your previous thought about sort of Robo-Taxis as maybe being the segment where you're going to see the most use of these newer sensor technologies. >> Rudy: Near term, yes. >> Exactly, what about maybe, is that sort of the same rules apply there for maybe the AI providers, that they're-- >> I think so and that's why they're all focused on that. I mean, from Uber to Waymo, they've all made the same calculation which is if you're running a fleet of vehicles, and so for example in Uber's case, the driver takes 80% of the fare and only 20% goes back to Uber, but if you can replace the driver with a computer, you can keep that vehicle on the road 24 by seven and you can keep 100% of the revenue. You don't need to pay the computer. So that's the calculus that they're all going through. But I think that many of them are making a fundamental mistake and I predicted recently that I think Uber, my prediction for 2020 is that Uber is going to divest its autonomous vehicle business and get back to the business that it should be focused on. Uber generates about $14 billion a year in gross revenue, so 20% of that, which is the piece that Uber keeps after the drivers take their 80, is what, 2.8 billion. Uber should be able to be an extremely profitable business on 2.8 billion of net revenue, but they're spending a huge chunk of money every year on R&D. Now, I would argue that Hertz and Avis have successful businesses. They're in the service, they're in the transportation business, but they didn't decide that they had to build their own cars in order to be in that business. My view, personal view, is that what Uber should be doing is saying, that's not our business, right? We are the world's best at managing this sort of peer-to-peer network crowdsourced transportation, if you will. And when some company, some Silicon Valley startup, comes out with safe enough technology, great, we'll use it, but we don't have to develop that ourselves. >> Well then, maybe just to play devil's advocate here for a second, what about it's a Robo-Taxi-type technologies being applied in bounded areas within metropolitan areas where the rules-- >> That's where it will start. >> Could be more-- >> I think that's where it will start, but I think part of the problem is that we have, perhaps in part due to all of the media hype around autonomous vehicles, we've been misdirected to thinking about autonomous vehicles as a replacement for the car we drive to work every day and I think that's the wrong way to think about it. I think that autonomous vehicles are going to show up in the market as an extension of public transportation. Right, you know, I get off the train and there's an autonomous vehicle waiting to take me for the last couple of miles to my office. >> And those last couple of miles would be sort of a regulated space. >> Rudy: May well be. >> Where the AI is more than capable of functioning. >> Right, and that, you know, yes. And so it's better to think about autonomous vehicles as not being a revolutionary technology but much more of an evolutionary technology. And in fact, most of these technologies are showing up in so-called ADAS technologies which are designed to make driving your regular car safer, lane assist, keeping you a safe distance. >> Donald: Maybe just explain that word, ADAS, and what that means. >> So ADAS stands for automated driver-assistance systems. So one of the first was cruise control, right, everybody's familiar with cruise control. And so to some extent ADAS is just building on cruise control. In addition to maintaining a constant speed, you can now stay in the lane. In addition to maintaining a constant speed, it will now automatically slow down if you get too close to the car in front. And so you can see ADAS as, you know, collision avoidance and so forth, not full autonomy, still have to have a driver in the driver's seat, but evolving year by year until one year we wake up and, yep, my car will actually drive me all the way from home to work without me intervening. Right, it's going to happen in that way. >> So incremental improvements. >> Incremental improvement. >> To ADAS as opposed to kind of revolution of autonomy. >> An overnight sensation. >> Yeah, right, coming from nowhere. Okay, understood. Well then, let's pivot from that then, okay. So let's talk about the automotive industry as a whole and sort of your thoughts on how this is all going to play out. >> Yeah, so there are some very interesting dynamics playing out in the automotive industry. Firstly, as good news, as a result of all of this money and innovation in the automotive industry, Detroit's actually coming back. I go there once or twice a year and you can feel the economy coming back in Detroit, but it's not going to come back around, you know, bending sheet metal. And the challenge that the automotive companies have is so much of their infrastructure and expertise has been built on construction, building a car, production lines to bend the metal, install the engine, and the internal combustion engine itself. And by complete coincidence, to some extent, we've got this confluence of all of these autonomous technologies and electric vehicles happening at the same time. Electric vehicles are much easier to make than internal combustion engines. Far fewer parts. It's one of the reasons that China has spun up about 20 different electric vehicle companies recently. So I think that long term, my prediction is that the automobile industry will go the same way that the personal computer industry went. When the PC first, you know, it was born by IBM, or Apple in some sense before that. There were dozens of companies producing different PCs and it was very much, they were expensive products, and, you know, relatively unusual. As the industry matured, the supply chains matured, and it became apparent there were really only two companies that were making a lot of money out of the PC industry. The companies that developed the software, operating system, and the companies that developed the processor, and all of the manufacturing went over to, in the PC's case, in Taiwan, right? And I think that exactly the same thing is going to happen with the automotive industry. Tesla today still actually makes cars, but I don't see them long term being in the car business because they're really a technology company. It's the reason I don't think Apple is ever going to get into the car industry. They make fantastic margins selling computer products. The gross margin selling a car, it's miserable. It can be single digits or teens. That would completely tank Apple's blended gross margin. So my prediction for the industry is there will be a few small pockets of very profitable businesses, particularly around the operating system, by which I mean the intelligence or the AI intelligence, and then the processor, whether it's a Qualcomm processor or a Nvidia processor or an Intel processor. And as with the PC industry, most of the profit will go there and most of the manufacturing will end up getting outsourced because that's not the value-add, you know, bending metal and so forth. >> Interesting, well, so in the kind of compute market today, right, we have this notion of sort of cloud-native, right, okay, and that many of the companies that are developing apps as relying on cloud-native infrastructure have a kind of technology lead that's going to be hard for some of the legacy providers to actually catch up on. Now, other people say that that's not necessarily the case and et cetera, right? Can you make the same argument for the electric car market, that some of the electric-natives might have a kind of sustainable advantage here? >> I should've added, today the cloud infrastructure companies, cloud services, SaaS companies, in the PC world, you know, very profitable, and I can see a similar cloud services model developing for the automotive industry. However, other than Tesla, it's very difficult to change the automotive channel to support that. I'll give you one example. Everyone that owns a Tesla is very used to the idea that, sometimes on a daily basis, a new bunch of software, operating system software, is downloaded overnight to your vehicle. You wake up in the morning and some new feature's been turned on, right? Tesla can do that because they bypass the entire dealership channel that has a complete lock on the rest of the industry. So for example, if GM wants to do the same thing as Tesla and do sort of what's called over-the-air, OTA, updates, software updates, they can't do that because their contract with the dealership network states that if there is service to be done on the vehicle, the vehicle has to be brought back to the dealership, and the dealerships consider updating the software on the vehicle as service. So their contract with the dealers actually prevent them from doing something that basic. So it's not just a technology issue. The whole channel and way vehicles get sold is going to have to change. >> Interesting, so that's the advantage that some of the new generation of vehicle manufacturers-- >> I would say that Tesla has a five year lead, technology lead, because they, like Apple, are vertically integrated. They're doing everything from user interface, fit and function, all the way down to the semiconductor. They're developing their own semiconductors now. So they have become a fearsome competitor in the electronic vehicle space because they've been doing it for longer than the other major auto companies. They've figured out a lot of the, you know, tricks and techniques of how to extend mileage and so forth. And so they have a substantial lead in the industry at this point, despite the fact that over the next 12, 18 months, every automotive company is going to be coming out with their own flavor of electronic vehicle. >> So then it's more than just about having electric drivetrains, et cetera, right? It's about the whole suite of capabilities. >> It's a systems engineering challenge. >> Interesting, okay. All right, well Rudy, we're going to have to leave it there, okay, but I think everything you've told us is, it sounds like some good news for some of the Tesla stock holders at the moment. >> I think so. >> Okay, well. (laughs) We'll pass on making an opinion about that, but great conversation, thank you for your insights. Okay, this is Donald Klein, host of theCUBE, here with Rudy Burger, managing partner at Woodside Capital. >> Rudy: Great, thank you, Don. (upbeat music)

>> Narrator: From Las Vegas, it's theCUBE, covering Qualys Security Conference 2019, brought to you by Qualys. >> Hey, welcome back, everybody. Jeff Rick here with theCUBE. We're in Las Vegas at the Bellagio at the Qualys Security Conference. They've been doing this for 19 years. They've been in this business for a long time, seen a lot of changes, so we're happy to be here. Our next guest works for Caterpillar. He is Brian Rossi, the senior security manager vulnerability management. Brian, great to see you. >> Thanks for having me. >> So I was so psyched, they had an interview, a gentleman from Caterpillar a few years ago, and it was fascinating to me how far along the autonomous vehicle route Caterpillar is. And I don't think most people understand, right? They see the Waymo cars driving around, and they read about all this stuff. But Caterpillar's been doing autonomous vehicles for a super long time. >> A really long time, a really long time, 25-plus years, pioneering a lot of the autonomous vehicle stuff that's out there. And we've actually, it's been cool, had an opportunity to do some security testing on some of the stuff that we're doing. So, even making it safer for the mines and the places that are using it today. >> Yeah, you don't want one of those big-giant dump-truck things to go rogue. (laughing) >> Off a cliff. Yeah, no, bad idea. >> Huge. Or into a bunch of people. All right, so let's jump into it. So, vulnerability management. What do you focus on, what does that mean exactly? >> So, for me, more on the traditional vulnerability management side. So I stay out of the application space, but my group is focused on identifying vulnerabilities for servers, workstations, endpoints that are out there, working with those IT operational teams to make sure they get those patched and reduce as many vulnerabilities as we can over the course of a year. >> So we've done some stuff with Forescout, and they're the kings of vulnerability sniffing-out. In fact, I think they have an integration with Qualys as well. So, is it always amazing as to how much stuff that gets attached to the network that you weren't really sure was there in the first place? >> Yes, absolutely. (laughs) And it's fun to be on the side that gets to see it all, and then tell people that it's there. I think with Qualys and with some of the other tools that we use, right? We're seeing these things before anybody else is seeing them and we're seeing the vulnerabilities that are associated with them, before anyone else sees them. So it's an interesting job, to tell people what's out there when they didn't even know. >> Right, so another really important integration is with ServiceNow, and you're giving a talk I believe tomorrow on how you use both Qualys and ServiceNow together. Give us kind of the overview of what you're going to be talking about. >> Absolutely, so the overview is really what our motto has been all year, right? Is put work where people work. So what we found was that with our vulnerability management program, we're doing scanning, we're running reports, we're trying to communicate with these IT operational teams to fix what's out there. But that's difficult when you're just sending spreadsheets around and you're trying to email people. There's organizational changes, people are moving around. They might not be responsible for those platforms anymore. And keeping track of all that is incredibly difficult in a global scale, with hundreds of thousands of assets that people are managing. And so we turned to ServiceNow and Qualys to really find a way to easily communicate, not just easily, but also timely, communicate those vulnerabilities to the teams that are responsible for doing it. >> Right, so you guys already had the ServiceNow implementation obviously, it was something that was heavily used. You're kind of implying that that was the screen that a lot of people had open on their desktop all the time. >> We lucked out that we were early in the implementation with ServiceNow. So, Caterpillar was moving from a previous IT service management solution to ServiceNow so we got in on the ground floor with the teams that were building out the configuration management database. We got in with the ground floor with the teams who were operationalizing, using ServiceNow to drive their work. We had the opportunities to just build relationships with them, take those relationships, ask them how they want that to work, and then go build it for them. >> Right, it's so funny because everyone likes to talk about single pane of glass, and to own that real estate that's on our screens that we sit and look at all day long, and it used to be emails. It's not so much email anymore, and ServiceNow is one of those types of apps that when you're in it, you're working it, that is your thing. And it's one thing to sniff out the vulnerabilities and find vulnerabilities, but you got to close the loop. >> Brian: You got to, absolutely. >> And that's really where the ServiceNow piece fits. >> And it's been great. We've seen a dramatic reduction in the number of vulnerabilities that are getting fixed over the course of a 30-day period. And I think it simply is because the visibility is finally there, and it's real-time visibility for these groups. They're not receiving data 50 days after we found it. We're getting them that data as soon as we find it, and they're able to operationalize it immediately. >> Right, and what are some of the actions that are the higher frequency that you've found, that you're triggering, that this process is helping you mitigate? >> I would say, actually, what it's really finding is some of our oldest vulnerabilities, a lot of stuff that people have just let fall off the plate. And they're isolated, right? They may have run patching for a specific vulnerability six months ago, but there was no view to tell them whether or not they got everything. Or maybe it was an asset that was off the network when they were patching, and now it's back on the network. So we're getting them the real-time visibility. Stuff that they may have missed, that they would have never seen before, without this integration. >> So I'd love to get your take on one of the top topics that came in the keynote this morning, both with Dick Clark as well as Philippe, was IoT-5G and the increasing surface-area, attack surface area, vulnerability surface area. You guys, Caterpillar's obviously well into internet of things. You've got a lot of connected devices. I'm sure you're excited about 5G, and I'm sure in a mining environment, or those types of environments are just prime 5G opportunities. Bad news is, your attack surface just grew exponentially. >> Yeah. >> So you're in charge of keeping track of vulnerabilities. How do you balance the opportunity, and what you see that's coming with 5G and connected devices and even a whole other rash of sensors, compared to the threat that you have to manage? >> Certainly in the IoT space it's unique. We can't do the things to those devices that we would do with normal laptops' assets, right? So I think figuring out unique ways to actually deal with them is going to be the hardest part. Finding vulnerabilities is always the easiest thing to do, but dealing with them is going to be the hard part. 5G is going to bring a whole new ballgame to a lot of the technology that we use. Our engineering groups are looking at those, and we're going to be partnering with them all the way through their journey on how to use 5G, how to use IoT to drive better services for our customers, and hopefully security will be with them the whole way. >> Right, the other piece that didn't get as much talk today, but it's a hot topic everywhere else we go is Edge, right? And this whole concept of, do you move the data, do you move the data to the computer or the computer to the data? I'm sure you guys are going to be leveraging Edge in a big way, when you're getting more of that horsepower closer to the sites. There's a lot of challenges with Edge. It's not a pristine data center. There are some nasty environmental conditions and you're limited in power, connectivity, and some of these other things. So when you think about Edge in your world, and maybe you're not thinking of it, but I bet you are, how are you seeing that, again, as an opportunity to bring more compute power closer to where you need it, closer to these vehicles? >> So I think, I wish I had our other security division here with me to talk about it. We're piloting a lot of those things, but that's been a big piece of our digital transformation at Caterpillar, is really leveraging data from those connected devices that are out in the field. And we actually, our Edge has to be brought closer to home. Our engineers pack so much into the little space they have on the devices that are out there, that they don't have room to actually calculate on that data that's out in the field, right? So we are actually bringing the Edge a little closer to home, in order for us to provide the best service for our customers. >> Right, so another take on digital transformation. You talked about Caterpillar's digital transformation. You've been there for five years now. Before that you were at State Farm. Checking on your LinkedIn, right? State Farm is the business of actuarial numbers, right? Caterpillar has got big heavy metal things, and yet you talk about digital transformation. How did you guys, how are you thinking about digital transformation in this heavy-equipment industry that's in construction? Probably not what most people think of as a digital enterprise, but in fact you guys are super aggressively moving in that direction. >> Yeah, and for us, from a securities perspective, it's been all about shift-left, right? We have to get embedded with these groups when they're designing these things. We have to be doing threat models. We have to be doing pen testing. We have to be doing that secure life cycle the entire way through the product. Because with our product line, unlike State Farm where we could easily just make a change to an application so that it was more secure, once we produce these vehicles, and once we roll them out and start selling them, they're out there. And we build our equipment to last, right? So there's not an expectation that a customer is going to come back and say, "I'm ready to buy a new truck two years from now," because of security vulnerability. >> Jeff: Right, right. >> So, yeah, it's a big thing for us to get as early in the development life cycle as possible and partner with those groups. >> I'm curious in terms of the role of the embedded software systems in these things now, compared to what it was five years ago, 10 years ago 'cause you do need to upgrade it. And we've seen with Teslas, right? You get patches and upgrades and all types of things. So I would imagine you're probably a lot more Tesla-like than the Caterpillar of 20 years ago. >> Moving in that direction, and that is the goal, right? We want to be able to get the best services and the most quality services to our customers as soon as possible. >> Right, very cool. Well, Brian, next time we talk, I want to do it on a big truck. >> Okay. >> A big, yellow truck. >> Let's do it. >> I don't want to do it here at the Bellagio. >> Let's do it, all right. >> Okay, excellent. Well, thanks for-- >> Thank you. >> For taking a few minutes, really appreciate it. >> Absolutely. >> All right, he's Brian, I'm Jeff, you're watching theCUBE. We're at the Bellagio in Las Vegas, not on a big yellow truck, out in the middle of nowhere digging up holes and moving big dirt around. Thanks for watching. We'll see you next time. (upbeat techno music)

>> From the Salesforce Tower in downtown San Francisco, it's theCube, covering Accenture Tech Vision 2019, brought to you by SiliconANGLE Media. >> Hey welcome back everybody, Jeff Frick here with theCube. We're in downtown San Francisco with the Salesforce Tower. We're in the 33rd floor with the grand opening of the Accenture Innovation hub. It's five stories inside of the Salesforce Tower. It's pretty amazing, couple of work floors and then all kinds of labs and cool things. Tonight they introduce the technology vision. We've been coming for a couple of years. Paul Daugherty and team. Introduce that later, but we're excited to have a couple of the core team from the innovation hub. And we're joined by Mary Hamilton She's a managing director of Accenture Labs. Great to see you Mary. >> Nice to see you too. >> And Teresa Tung also managing director of Accenture Labs. Welcome. >> Thank you. >> So it's been quite a day. Starting with the ribbon cutting and the tours. This is quite a facility. So, what does it mean having this type of an asset at your disposal in your client engagements, training your own people, it's a pretty cool spot. >> Yeah, I think it's actually something that's, these innovation hubs are something that we're growing in the U.S. and around the world, but I think here in San Francisco, we have a really unique space and really unique team and opportunity where we're actually bringing together all of our innovation capabilities. We have all of them centered here and with the staircase that connects everyone, we can now serve clients by bringing the best of the best to put together the best solutions that have open innovation and research and co-creation and innovation all in one. >> Right and you had a soft opening how many months ago? So you've actually been running clients through here for a number of months, right? >> We have. So, we've been working here probably about six months in the workspaces. We've been bringing clients through, kind of breaking in the space, but just over the holidays we opened sort of all of the specialty spaces. So, the Igloo, the Immersive Experience, we've got a Makeshop, and those all started to open up so our employees can take advantage and our clients can come in. >> Right, right. >> Yeah. >> So one of the things that comes up over and over I think in every other interview that we've had today is the rock stars that are available here to help your clients. And Teresa I got to brag on you. >> Got one here. >> You're one of the rock stars, all you hear about is most patents of any services for most patents from this office of all the other offices in Accenture. >> All of Accenture >> You're probably the person. (laughs) So congratulations. Talk about your work. It's funny, doing some research, you have an interview from a long time ago, you didn't even think you wanted to get in tech. >> Yeah. >> Now you're kicking out more patents than anybody in Accenture which has like 600,000 people. Pretty great accomplishment. >> I think it's a great story how a lot about people think about technology as a geek sort of thing and they don't actually picture themselves in that role but really, technology is about imagining the future and then being able to make it happen. You can imagine an idea, and you think Cloud, and AI, VR, it's all so accessible today. You could buy a 3D printer and just print your own idea. >> Right. >> And that's so much different than I think it was even ten, twenty years ago. And so when you think about tech, it's much more about making something happen instead of, just again, coding and math. Those are enablers but that's not the outcome. >> Right, right. So what type is your specialty in terms of the type of patent work that you've done? >> I've done them all. So I start with cloud computing, doing a lot of APIs and AI. Most recently doing a lot of work on robotics and that's the next generation. >> Right. so one of the cool things here is, software is obvious, right? You get to do software development, but there's a lot of stuff. There's a lot of tangible stuff. You talked about robotics, there's a robotics lab. Fancy 3D printing lab. >> There's like this, >> Yep. >> I don't know, the maker lab, I guess you call it? >> That's right. >> So, I don't know that most people would think of Accenture maybe as being so engaged in co-creation of physical things beyond software innovation. So, has that been going on for a long time? Is that relatively new? And how is it playing in the marketplace? >> Yeah, so, there's a few things we've been doing. Some of it is the acquisitions we've made, so Mindtribe, Pillar, Matter, that really have that expertise in industrial design and physical products. So we're getting to that space. And then, I'm also, as a researcher's standpoint, I'm really excited about some of the area that you'd never think Accenture would play in around material science. So if you start to combine material science plus artificial intelligence, you start to have smart materials for smart products and that's where we see the future going is what are all the kinds of products and services that we might provide with new material? And new ways to use those materials And, >> Right. >> My original background, my degree is in material science so I feel like I've kind of come full circle and exactly what Teresa was saying is how can you design things and come up with new things? But now we're bringing it from a technology perspective. >> Right, got to get that graphene water filtration system so we can solve the water problem in California. That's another topic for another day. But I think one of the cool things is really the integration of the physical and the software. I think a really kind of underreported impact of what we're seeing today are connected devices. Not that they're just connected to do things, but they phone home at the end of the day and really enable the people that developed the products, to actually know how they're being used. And then the other thing I think is so powerful is you can get shared learning. I think that's one of the cool thing about autonomous cars and Waymo, right? If there's an accident, it's not just the people involved in the accident and the insurance adjuster that learn what not to do but you can actually integrate that learning now into the broader system. Everyone learns from one incident and that is so, so-- >> Right. >> different than what it was before. >> Yeah I mean, it really points to type of shared pursuits of larger business outcomes. By yourself, a company might see their customer and impact their business and their product, but if you think about the outcome for the customer, it's around taking an ecosystem approach. It might be your car, your insurance company, you as an individual, and maybe you might be a hobbyist with the car, you're mechanic. Like this ecosystem that I just described here. It's the same across all of the different types of verticals. People need to come together to share data to pursue these bigger outcomes. >> Right, you need to say? >> I was just going to say, and along those lines, if you're sharing data, those insights go across the legal system. But then they can get plugged back in to thinking about the design, and we're looking at something called generative design where if you have that data, you can start to actually give the designer new creative solutions that they may not have thought about. >> Right. >> So you can kind of say, hey based on these parameters of the data we've received back about this product, here are all the permutations of design that you might want to consider, and here's all the levers you can pull and then the designer can go in and then say, okay, this makes sense, this doesn't. But it gives them the set of here are all of the options based on the data. >> Right. >> And I think that's incredibly brilliant. It's kind of the human plus machine coming together to be more intelligent. >> So, human plus machine, great Segway, right? What we just got out of the presentation and one of the guys said there's three shortages coming up. There's food, water and people. And that the whole kind of automation and machines taking jobs is not the right conversation at all, that we desperately need machines and technology to take many of the tasks away because there aren't enough people to do all the tasks that are required. >> I mean think about it as a good thing. As a human, the human plus workers really enabling your job to be easier, more efficient, more effective, safer. So any task that's dull dirty, dangerous, those are things that we don't want to do as humans. We shouldn't be doing those as humans. That's a great place for the robotics and the machines to really pair with us. Or AI, AI can do a lot of those jobs at scale that again, as a human we shouldn't be doing. It's boring. Now you could have human plus machine whether it's robotics or AI to actually make the human a higher level worker. >> Right, I love the three Ds there. You got to add the fourth D, drudgery. Talking about automation, right, it's like drudgery. Nobody wants to do drudgery work. But unfortunately we still do. I mean, I'm ready for some more automation in my daily tasks for sure. Okay, so before we wrap up. What are you looking forward to? We got through the ribbon cutting. Are there some things coming in the short term that people should know about, that you're excited that you're either doing here, or some of your, kind of research directives now that we got the big five from Paul and team. What are you doing in the next little while that you can share? >> Well, I'm excited to have clients coming in, so >> Yeah. >> Al lot of the innovations that we have like Quantum Computing. This is a big bet for Accenture. At the moment, at the time we started Quantum Computing, our clients weren't begging for it yet. We made that market. We went out and took a bet. We saw how the technology was changing. We saw the investments in Quantum. We made the relationships with 1QBit, with IBM and through that, now we're able to find this client opportunity with Biogen and that's the story that we published a drug discovery method that is actually much better than what would happen before. >> Right. >> Yeah. >> Mary? >> For me it's about, it's also the clients and it's thinking about it from a co-research and co-innovation standpoint. So, how do we establish strategic, multiyear, long-term relationships with our clients where we're doing joint research together and we're leveraging everything that's in this amazing center, to bring the best and to kind of have this ongoing cycle of what's the next thing. How are we going to innovate together, and how are we going to transform them, talk about approximately from building physical products to building a set of services. >> Right, right. >> And I think that's just taking advantage of this to make that transformation with our clients is so exciting to me. >> Well, what a great space with great energy and clearly you guys look like you're ready to go. >> Hey, we are. >> So congrats again on the event, and thanks for taking a few minutes and sharing this terrific space with us. >> Thank you. >> Thank you. >> All right. She's Teresa, she's Mary, I'm Jeff. You're watching theCube, from San Francisco the Accenture Innovation Hub. Thanks for watching, we'll see you next time. (upbeat music)

(upbeat music) >> Announcer: Live, from Las Vegas, It's theCube! Covering Informatica World 2018, brought to you by Informatica. >> Welcome back everyone. It's theCube's live coverage of Informatica World 2018, here in Las Vegas. I'm John Furrier, your host and analyst, with Peter Burris, co-host and analyst at Wikibon and still going on theCube. Our next two guests is Gitesh Ghai, C Vice President, General Manager of Data Quality Security and Governance for Informatica, and Smail Haddad who is the Senior IT Director of Data Governance and Data Delivery Architecture at Toyota, company wide, Great to have you on Gitesh. Great to have you on Smail. So we were just talking before coming on camera, before we went on live about the massive role that you have at Toyota with data. You are looking at everything now. You're touching all the data. But it wasn't always like that. >> Smail: Yeah it wasn't always like that... >> Tell us about your journey and your role at Toyota. >> Yeah thank you. So Toyota, again, started business in North America. People know, maybe not, 65 years ago. And we started as a little dealership in North Hollywood. Bringing these Japanese cars. So we grew from that single dealership in North Hollywood to this big company we are today, with almost 25 plants around North America, Canada, US, and Mexico. And almost 2,600 dealerships across nationwide. So what that came with, it came with a big responsibility, in terms of understanding our customer base and trying to be more closer to what the customer needs. So our supply chains, where we produce the vehicles, it really was mostly a push supply chain, where we build a car and we push it to the customer to buy it. The model works very well, all the way to 2008. Where things change and we all understand what happened back in the financial meltdown and the crisis, that was a worldwide crisis. And that was a turning point for Toyota because we start seeing a shift in the demand. The customers becoming more savvy. Demanding for example, more electrical cars, less gas guzzlers vehicles and so on. The marketing department, which was a different company back then, understood that but the production companies, which was producing the vehicles, they didn't have that knowledge. So the journey to bring these two together became really critical after that 2008 crisis. Because what it forced us to do was the vehicles were being produced everyday, the dealers were not able to sell, and we were just stuck in vehicles around the lot. So why the digital disruption was so key for us, is the data was always there. Data always told us the truth. And that's what the facts are. Where we started looking at, back after that, is hey, if we look at the data and the data always predicted that the shift in the market will happen that way. And we should've have throttled down maybe, our production system better. Why we didn't do it that way? We were not looking at the data. Data was available. So what we undertook, under Toyota IS, we said, "Can we bring all this data across all these silos, "into one place?" So we build our big data solution, where the data is coming from various departments and various business lines. And it's being blended together and correlated. What that gives us is really that 360 view of our business, which we were missing. 'Cause we were looking at the business in silo, in pieces. And with that explosion of data, that we were gathering, obviously that brings a lot of questions about where this data, how good it is, if I'm going to make decisions on it, can I trust it? All that was a good takeaway into the business I'm in, which is the Data Governance. It's basically how can we govern this data that we are collecting on a daily basis today? And so my department is leading basically, the North American Governance and Quality across all the business line in North America. So as we are gathering these data points everyday, on a daily basis, even today we are gathering. What made it even, made it go even further in terms of volume, is we started capturing data coming from the cost, on a real time basis. So this is not just sales data where we capture the experience, the sales, and configuration of the vehicles on a daily basis... >> John: That's a lot of data coming in. >> A lot of it, a lot of it. So the volume exploded. With that, the responsibility to put a solution, where people can go quickly, find the right data. So basically, the time to data became so critical. How can we shorten that time to find the right data you want? And understand it, and trust it, and use it? >> John: So last... >> Sorry John, the Toyota story that you're telling us is especially interesting 'cause Toyota is legendary for empirical based management, lean manufacturing, so you have plants and marketing organizations, and sales organizations who, because of the Toyota way, have grown up on the role that data needs to play in their function. And what you're doing is you're saying, "That was great. "But we had to take it to a next level "and organize our data differently so we could look at it "across the entire company." >> Across the entire company. So absolutely, there are four, basically, goals that Toyota is trying to achieve today. One is understanding our customer in a more personalized way. Understand today's demand and hopefully predict tomorrow's demand. The second important pillar, empower our employees and our team members. By the way, Toyota, we call employees team members. And the third one is optimize our operations. And the fourth is transform our product. In order to achieve all these four goals, data is at the middle of all this. Why it's so important, we understand that today, in this day and age of digital disruption. And by the way, the automotive industry is being disrupted. Not our competition right now, Toyota, is no more the GM, and the Ford, the traditional automotive companies. But our new competition is all the technology companies, Google, Apple, Amazon. And you might have heard the news. Everyday, how they are disrupting these segments where you hear about autonomous driving cars and everybody's jumping on it. And behind all that, taking just the autonomous driving cars. The amount of data behind these so you can make the vehicle drive itself and take you from point a to point b in a safe manner and avoid all the road hazards. That needs a huge amount of data that's behind it, and fuels that. We're able to make huge stride. The new story of Data Governance at Toyota, is really, how we can enable that and not being just about compliance and risk management, which is kind of understood, that's part of the job. But we make that seamless. We wanted our business unit to focus more on the core business and goals, versus worrying about, "Am I in compliance, do I need to do this or that?" Try to seize the opportunities and put Toyota in a competitive way so they can compete with all these new disrupters like I said, Google, and the, the Apple of the world. Because what they have in common, those companies, >> John: They're data companies. >> Exactly. Data companies, technology. They understand how to use data. They understand how to analyze data. This is where traditional automotive companies like Toyota, and GM, and Ford, are basically bound to learn about that. >> But Waymo is not a car manufacturer, Uber is not a car manufacturer, they're companies that are providing a transportation service. And the only way that Toyota could provide a transportation service, is if you started organizing your data differently, in service to the idea of providing consumers a better, and businesses, with better transportation services. Whether you call it personal. I don't want to be the typical analyst that kind of goes off and starts renaming things. But that's fundamentally what you're trying to do. Is you're saying, "Our customers are mainly focused "on getting from point a to point b safely. "Let's make sure that we have products and services "that help them get there. "Perhaps through a lot of intermediaries along the way." But is that kind of how you're organizing things? >> Absolutely, so in order to achieve that goal. We wanted to bring the silos. Like I said, the data was always there but it was always built in silos, stored in silos. What we did in the next, last few years, we started breaking all the silos because we started looking at the data as an enterprise assets and no more as just a departmental assets or as a tool to get to a goal. It became the strategic assets for the company. And in order to achieve that, was to really break the silos. Bring it together so we can see across and understand how are business is operating. And hopefully, put the company in a competitive advantage to see the future coming to. >> It must be really frustrating to know that the data was there the whole time. And you're kind of kicking yourself. What did you do? I mean, you brought Informatica in. What's the Informatica connection, Gitesh? Get a word in, come on. With the Informatica connection, these guys. Are you the core supplier? Do you guys, the connective tissue between Toyota's groups? >> It's all about the data, right? It's all about the data. Informatica's role in all of this, it's a great story. Toyota's, Smail's story, is a great story. What Informatica brought to bear for Toyota, it's actually the promise of big data. The promise of big data is bringing together data that hasn't been analyzed together in a new context before. So breaking down these silos and bringing together the data. What's interesting is when you bring it together, you create a data lake. But there's a very big difference between a data lake and a data swamp. Which is why naturally, governance, quality, trustworthiness became a focus area of bringing all of this data together. >> Well last year, talking about data swamp and data lake as our core theme. This year governance and enterprise catalog is a bigger story because you guys easily could've been swamped out because of all this new data coming in, whether it's car telemetry or new data. 'Cause if you had set the table for your intercompany connective tissue, if you will, then you're like, "Oh, hey we're done, wait a minute." >> But Toyota was applying data to the work of manufacturing, to the work of marketing cars. And now you're trying to apply data to the work of providing better transportation. And the only way to think that through is to see how all this data can be reorganized and brought together. And at the same time, you can still, then turn that data around and still apply it for the work of manufacturing, the work of marketing, and the work of selling. >> Gitesh: Absolutely. >> Also I'd add, to be competitive in a new market, they are going to use their, leverage their assets. Not only data but their physical assets. To compete at a new level, a new playing field. >> Smail: Absolutely. >> With data at the center. >> And I think you said it earlier, you have to bring this data together in the lake. But you need an organized view of all the data that's out there, which starts with our data catalog. So the data catalog gives you a sense of what data do you want to bring in the lake and what data, frankly, is noise, doesn't matter? >> Whole 'nother level of operations, whole 'nother level of intelligence. Competitive advantage, competitive strategy. >> Peter: What a job. >> We're data geeks, geeking out here. Great story, I'd like to do a follow up. I think that this is a real big story of not only of digital transformation, digital evolution, digital disruption, digital business, great story... >> You used to be able to do this job in Southern California. >> Yes, absolutely. >> Thanks for bringing Toyota to the table. Thanks for coming on. >> My pleasure. Thank you for having me on. >> The beginning of a journey that's going to continue it's not ending anytime soon. Toyota company, really bringing data into the center of the action. Of course, we're in the center of the action as theCube, bringing you the data from Informatica World, right here, on theCube. More coverage after this short break. I'm John Furrier, Peter Burris. Stay with us, we'll be right back. (upbeat music)

>> Narrator: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles. Brought to you by Western Digital. >> Hey. Welcome back, everybody. Jeff Frick here with theCUBE. We're in Milpitas, California at the Autotech Council Autonomous Vehicle Event. Autotech Council is an interesting organization really trying to bring a lot of new Silicon Valley technology companies, and get them involved with what's going on in industries. They've done a Teleco Council. This is the auto one. We were here last year. It was all about mapping. This is really kind of looking at the state of autonomous vehicles. We're excited to be here. It's a small intimate event, about 300 people. A couple of cool, dem hook cars out side. And our first guest is here. He's Suneil Mishra. He is the strategic marketing for Tensyr. Nice to be here. >> Thanks, Jeff. Appreciate you having us. >> Yeah. So, give us the overview on Tensyr. >> Sure. So we're a Silicon Valley startup, venture-backed. We're actually just coming out of stealth. So you're one of the first folks to hear about-- >> Jeff: Congratulations. >> what we're up to. And we're basically doing software platforms to actually accelerate autonomous vehicles into production, doing all the things around safety and efficiency, and ROI that will be important when we actually want to make money on all of this stuff. >> Right. So what does that mean because obviously, you're in Palo Alto. I'm in Palo Alto. We see the Waymo cars driving around all the time. And it seems like every day I see a few more cars running around with LIDAR stacks on top. You know, those are all kind of R and D login miles, doing a lot of tests. What are some of the real challenges to get it from where it is today to actual production? And how are you guys helping that process? >> Sure. So yeah, I mean a lot of what people don't think about is these R and D kind of pilot cars. They actually are doing R and D. It's trial and error. That's the whole point of R and D. When you get to production, you can't have that error part anymore. And so safety suddenly becomes a critical element. And part of the things of getting safety is being much more efficient on the vehicle because you have to do a lot more software in order to be safe across multiple different kinds of examples of streets, and locations, of weather conditions, and so on. So, we basically provide essentially all of the glue, all of the grunt work, at the lower levels, to make things as efficient as possible, as safe as possible, as secure as possible. And also making things adaptable and flexible. There's lots of different hardware coming down the pipeline from all different vendors. And if you're a production vehicle, it's which ones you choose. There may be different configurations for different cost points of vehicles. And then of course when you're looking to the future as a production vehicle manufacturer, how do you know which pieces of hardware to use and whether your software will work or not? We kind of give you a lot of insight into all of those things that allow you to certify that your products are safe. And so we don't build the stacks themselves, but we actually take people self-driving models, and we accelerate them onto the vehicles. >> Jeff: With your software in the ecosystem of the self-driving car hardware. >> Exactly. So we have an actual runtime engine that will set on the end device, in this case a vehicle. And it will actually optimize the scheduling, the orchestration of all of your code. That makes it much more efficient. And we can monitor that so you can mitigate for safety. And if something does go wrong, we're essentially like a black box where you can actually see what actually happened to your software. >> So it's interesting. We talked a little bit before we turned the cameras on that a lot of the self-driving vehicles are Fords. We talked to the guys at Phantom and apparently, it's a really nice system to be able to get computer control into the control mechanisms of the car. But you said there's a whole layer of how do you define being able to interact with the control systems of the car, versus is it safe, is it ready for production, and kind of taking it beyond that R and D level. So what are some of the real challenges that people need to be aware of when we're going to make that big leap. >> Yeah, so I mean, a couple of the big things that happen is when you're seeing these pilot vehicles driving around, the amount of software that they actually have on there to control the vehicles is very tuned for the particular cases. That's why you see a lot of these vehicles out in places like Arizona where it's sunny weather. You're not having to deal with snow and all the rest of that stuff. >> Jeff: Right. >> If they actually take a car and move it to Michigan for the snow test, they'll actually deploy different software to do the snow case. But when you're actually in a production vehicle, and nobody can actually come back and change that software, you're going to have to load all of those types of solution, on at the same time. That requires more space, more compute power. And so for solutions like ours, we actually allow the production manufacturers to figure out what the optimal solutions are in those cases because you can't come back and change the software. You don't have an engineer that can go tweak that code. And you don't have a safety driver, of course, to go grab the wheel if something goes wrong. These things essentially have to be able to go out there in the wilderness for years and years, and actually work. So it's a whole different classification of problem that takes a lot more compute power. And people who are seeing those giant sets of sensor rigs don't probably realize there's also a giant trunk for clarisitive, where if there's compute power in the back, running 3,000 watts of power. When you actually get to deployment, you're going to have an embedded system with maybe 500 watts of power. So you have less compute power, and you're trying to do more with it. So it's quite a challenging problem, to actually jump to production. And we're kind of smoothing out a lot of those wrinkles. >> Right. So, I just want to get your kind of perspective on kind of the Apple approach, which everyone kind of sees Tesla as. Right? It's soup to nuts, it's the car's design, it's the software, versus kind of an industry approach where you have all these different players, obviously, 300 people here at this event. There's autonomous vehicle events going on all over the place where you got all these component manufacturers, and component parts, coming together to create the industry autonomous vehicles versus just the Tesla. So what's kind of the vibe in the industry? It feels like early days. Everybody's cooperating. How is this think kind of coalescing? >> Yeah. I think what we're seeing, we basically talk to people up and down the stack, because anyone who's doing this stuff is a potential customer for us, so automotive OEMs to tier one suppliers, to the AI startups are building these software stacks, they're all potential customers for us. What we're seeing from everyone is they're saying there's so many difficult problems to solve along this path that no company can really do it themselves. And of course, you're seeing big companies investing billions of dollars. But it's great because everybody's saying, let's find people that specialize, whether it's in sensors, or compute, all the rest of those things. And kind of get them, and partner with them, have everybody solve the right problem that they're specialized and focused on. And we essentially can kind of come in and we solve parts of those problems, but we're also kind of the glue that fills a lot of those things together. So we actually see ourselves as being quite advantageous in that anyone who's doing their specialized piece, contributes into the collective. And we kind of build that collective and make it easy for the actual end vendor that's trying to sell a car or run a service, to actually access all those mechanisms. >> And are kind of the old school primary manufacturers still the focal point of the coalescing around this organization or are they losing kind of that position? >> I wouldn't say their losing it. It's kind of an interesting play. So you've got a bunch of traditional automotive guys who actually don't really, not to diss them, but they don't really understand large-scale software because they haven't had that in their vehicles until now. And at the same time you've got kind of your startup mode software experts that don't really understand a lot about automotive. But eventually, it's got to go on a car. And so what we're finding is the automotive manufacturers are really saying to get to production, we need certain kinds of safety guarantees and ROI and so on. So they're really driving from that point of view. The software guys are kind of saying, well, we're just going to throw the software over to you and sort of, good luck. So, we're actually finding both sides care, but nobody's quite sure who should be taking the lead. So I think we're getting to the point where ultimately, automotive manufacturers will be the one shipping vehicles and that software's going to be on their car. So they're going to be the ones that care about it most. So we're actually seeing them being quite proactive about how do we solve these problems. How do we get from the R and D stage to the actual production stage? So that's where we're seeing a lot of the interest on our side. >> All right, Suneil. We could go on forever, but we have to leave it there. And congratulations on your launch and coming out of stealth. And we're excited to watch the story unfold. >> Great. Thanks, Jeff. I appreciate the time. >> All right. He's Suneil. I'm Jeff Frick. You're watching The Cube from the Autotech Council Autonomous Vehicle Event in Milpitas, California. Thanks for watching. (upbeat music)

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in Mountain View, California, at a really cool start-up, Phantom Auto. They're coming at this autonomous vehicle thing from a very different direction. They're not a car company, it's not BMW and Audi and Nissan and all the other people you hear about. It's a pure software play, but it really has a huge impact on the autonomous vehicle industry. We're excited with the guy who's putting all these development, business development deals together. He's Jordan Sanders, director of business development and operations. Jordan, great to see you. >> Yeah, thanks for having me. >> So, again, when I first heard about you guys I thought, "Okay, do I order "this to drive my grandfather to the store," because he shouldn't be driving even though he has his driver's license, but no, that's not it at all. You guys have a very specific target market and it's really more a biz dev than a direct-to-consumer market. >> Yeah, exactly, so we are a B2B business and our target customers are those who are closest to getting their autonomous vehicles on the road. And so, that's frankly where we're seeing the most traction for now, at this point, from customers. As you get closer to true deployment of level four robo-taxis you realize a need for remote assistance, and we think we have the best solution on the market. >> Jeff: Right. >> To actually remotely drive the car and have a human in the loop to promote safety and service. >> So, as you look at your kind of tam, your ecosystem that you're going to market with, obviously we all know Waymo. We see the cars driving around all the time, the Nest is right up the street, but how's that landscape evolving? You know, we obviously hear about Uber, we hear about Lyft, you hear little bits and pieces about BMW and different car companies. As you sit back from where you're sitting, how do you kind of segment the market, how do you figure out where you're going to go next? >> Yeah, it's an interesting question. I mean, right now, you know, there's obviously a lot of excitement around this market and where it will be in five years. Right now the number of actual autonomous vehicles deployed is relatively low, and so that is frankly what our business is tied to. Again, it's enabling every vehicle on the road to actually operate safely, and so in terms of total addressable market, how we see it evolving, right now it's a relatively small number of cars and a relatively small number of players, but we see huge opportunity and huge growth in the sector over the next five years and 10 years. >> Right, and obviously a big integration challenge for you guys because each platform that you partner with is, you know, we hear all the time, some of them are using some shared infrastructure, some of them are trying to use their own, some are RADAR, some are LIDAR, some are camera, some are combination, so from a business development point of view you guys have to integrate with all those different platforms. >> That's correct, and so that's from the very beginning, we're building our end-to-end service to be very flexible and the software piece especially can integrate with any vehicle, with any vehicle manufacturer, because frankly we want to be open to the market and we don't want to just cover, you know, one customer's vehicles. We are sort of a third party who can provide a safety solution for a number of AV operators. >> Right, now the other interesting thing that people probably don't think about is, you know, we hear all about the technology in the cars and the machines, right, and IOT and it's all about machines, but in bringing a human operator into the equation it's not just to operate the vehicle, it's actually a person and all that that means. I wonder if you can kind of explain how that impacts people's autonomous car vehicle when there's actually a person involved. >> Yeah, definitely, so I think, you know, I think about this from a personal standpoint, so part of me is very excited for autonomous vehicles and I've ridden in several autonomous vehicles, feel very comfortable in them very quickly, but I also live in Silicon Valley and not everyone does just get to zip around in autonomous vehicles and is working in this industry, and so we do view there's going to be a, you know, a big consumer adoption kind of hurdle to overcome, and a piece of that is having the passengers in the car comfortable and feeling that, you know, someone has their back, right? >> Jeff: Right. >> So that's a key part of what we believe that we deliver is a human touch to self-driving cars, which we think is very important just at a psychological level, knowing that you have somebody who is monitoring your ride and is ready to intervene and protect you, you know, in the event that something goes wrong with the ride. And the other thing is by having a human in the loop it also enables all sorts of interesting ways of providing better service, and that's going to be a very, a key piece of whenever everyone inside the car is a passenger, there are no longer drivers, we're passengers. There are going to be lots of opportunities for enhancing passenger experience, and we think part of that can be, you know, providing a human service, an actual human on the other end making you feel comfortable and also connecting you with almost like a concierge. >> Right, and like OnStar has been around forever, right, that's probably the first kind of two way- >> You said that, not me, yeah. >> Two way communication, right, into the vehicle, which at first was I think mainly a safety feature. You crash and it sends out a 911 and then I think they kind of evolved it into a little bit of a concierge service. >> Exactly, so again, there's certainly that piece that we think is going to be really important for consumer adoption. I mean, I think AAA did a survey recently that showed 75% of consumers are afraid of trusting a machine, an autonomous vehicle. Now, we're very confident that the AV tech, once you get inside an autonomous vehicle that you very quickly realize, "Wow, this is a great driver," and we're very bullish on, you know, autonomous vehicle technology and believe that it's very reliable. But again, in those edge case scenarios, having a human who's going to intervene on your behalf and be able to actually operate the vehicle will be really important. >> Right, so somebody's watching this and going, "Ha-ha-ha," you know, "I'm a hacker, I'm going to hack into the stream," and it's not going to be Ben, the nice, smooth driver taking over the car but some person that maybe we don't want taking over the car. So, in terms of security and network infrastructure, how much are you leveraging your partners' infrastructure, how much are you leveraging your own, where does kind of security fit in this whole puzzle? >> Yeah, it's a great question and certainly one that, you know, we're hearing from a lot of customers. So, we are working with a variety of cybersecurity firms for making sure that our solution is extremely secure across multiple vectors, so whether it's just on the software piece or really our end-to-end solution, from the hardware that we can offer in the car, to the software, to the actual control center, the operation center where the driver's driving you, making sure that we have end-to-end security to avoid any situation like that. >> Right, so Jordan, for the people that aren't in Silicon Valley, what should they know about autonomous vehicles, how close are we, how much is it just, you know, stuff in the newspaper and you know, kind of nirvana still or just, you know, specialize Waymo vehicles that we see all the time in this neighborhood. How close is this to Main Street, how close is this to being that vehicle that picks me up when I get off the Caltrain to San Francisco and I need to go to a meeting over the Embarcadero? >> Yeah, so I think what people should know about this technology is that it is incredible technology that will be life-saving and that needs to get on the road, but that needs to happen in a safe manner and at a time where you can have full confidence in the operation and all settings, right. The technology is incredible, and so what Phantom Auto is here to do is to get these life-saving vehicles on the road quicker, and so what I would say to the average person who's a little uncertain of this technology is that it is incredible and you're going to enjoy the experience and it will be life-saving, and again, I think Phantom Auto is working to actually bring that experience to consumers by getting these robo-taxi services deployed. >> Jeff: Right. >> Pull out the safety driver and have a remote safety driver, a Phantom Auto remote operator ready to take over control of the vehicle in the event that you need assistance. >> And in terms of where you guys are as a company, right, you're a relatively small company, got this cool Lincoln here, where are you in terms of your company? Do you have POCs in place, do you have customers in place, kind of where is it in terms of the deployment of the technology within your ecosystem? >> Yeah, well we realize that we're bringing a very critical solution to these operators, so again, if you're an autonomous vehicle developer and operator and really thinking seriously about deployment you realize that you need a solution like ours, and so on the business standpoint we have several deals already closed, some pilots planned over the next few months, so you'll be seeing a lot more, I think, of us very soon out in the market. >> All right, now you're going to see more of us on the street. So, Jordan, let's stop talking and let's go take a ride in the car. >> Let's get in the car. >> All right, he's Jordan, I'm Jeff. We're getting in the car, thanks for watching. (techy music playing)

(click) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. It's 2018. We just got out of the CES show and all the rage is autonomous vehicles. You can't get away from it. It's what everybody's talking about. Tesla just announced their autonomous truck, their autonomous Roadster. We're here in Palo Alto, right on San Antonio Road. Googleplex and Waymo's are right up the street. So everyone is all about autonomous vehicles, but we're excited to be here at Phantom Auto and they're taking a slightly different approach for a slightly different problem. We're excited to have Shai Magzimof. He's the co-founder and CEO of Phantom Auto. Shai, great to see you. >> Nice talking to you, yeah. Thanks for having me. >> So Phantom Auto, you guys just got back from CES. You were giving demos, but you weren't stuck in, like, the little lane that was protected. You were actually driving people all over the streets. >> We were driving on the Strip, yeah, yeah. We actually were picking people from the hotel lobby, so the valet guys would let us in with an empty vehicle. These videos are actually also online, and we drove them off the Strip and back to the hotel, or to another destination. >> So you're doing a whole different thing. You do not have an autonomous vehicle. >> It's not an autonomous vehicle. >> You were the ultimate chauffeur driven vehicle. >> Right. Right. So again, for the show, we did our job to show that the vehicle can drive without a driver in the driver's seat, but what we do is actually a safety solution for autonomous vehicles. And that safety is basically what happens if an autonomous vehicle artificial intelligence doesn't work. Let's say there's something that it cannot see, or something that, you know, an unidentified object, road construction areas, severe weather conditions, all this stuff happens all the time. And autonomous vehicles may struggle with the situation so Phantom Auto provides a solution that we work with these companies. We provide them that solution that allows remote operations, so someone will connect remotely. >> So let's back up a couple steps. Autonomous vehicles are meant for no driver. You guys have a driver but you're really assisted driving with a person from a remote location. So how do you describe that in a short category? I'm sure the analysts will want you to have a category. >> The category would be the same way you think about air traffic control, right, or any type of control center, like call control centers. Any type of support for customers, you would have a bunch of people sitting in front of computers, in our case they're sitting at computers with steering wheels, we'll see that later, and they can connect to a vehicle remotely, and when they move the steering wheel or press the gas or brake, it would actually happen in realtime. So we have this software that allows this realtime, critical communication for autonomous vehicles. >> Now what's weird is when we first heard about you guys, I'm thinking, okay what is the use case? Am I going to send the Phantom Auto to go pick up my hundred-year old grandfather who probably shouldn't be driving anymore, where you're escorting it. But really it's a very different application, and I don't think most people understand that, in autonomous vehicles, there's a whole lot of use cases still that they haven't quite figured out. My favorite one is when two of them pull up to a four-way stop, and neither of them wants to go first. They get stuck in a friendly lock, right, they get paper-logger, some poor kid has his foot in the intersection and is trying to wave the car through and it won't go through. So it's corner cases that you guys are all about, to really enable that next-stage of machinery. >> When I started a company, right, I'm a big believer in autonomous vehicle, I wanted to make them happen faster and sooner because it's life-saving technology. This is going to change the world. We all want it faster. Now, the reason why we're still not there yet is because there are many corner cases, edge cases, these situations where the machine didn't train enough for, and in this situation they provide a cover. So we have a person that would sit in an office, he doesn't have to be so close nearby. When we were in Vegas a couple weeks ago, the driver was in Mountain View, so Mountain View, California, Silicon Valley to Vegas, and he moves the steering wheel and he moves it real time. >> But he's driving the car. >> Yeah. >> So one of the great knocks on cloud, right, is latency, and clearly the use case that's always brought up is if you're in a self-driving car, you don't have time for the data to get it to the cloud and back to make a decision if a little ball rolls out into the street. So latency is a big issue. How do you guys deal with the latency issue? >> That's our secret sauce, obviously, but I'm happy to share as much as I can. The high level description would be, we connect multiple networks at the same time. We would usually have only AT&T in your cellphone, right, or in your car, and then we have AT&T, Verizon, T-Mobile, and a few networks, all of these together are bonded, and once they're bonded they get a much stronger connection. It sounds maybe easy, okay so let's plug a few phones and then get a really good connection, but it's much more complicated than that. We share and split the data across multiple networks at the same time, we prioritize the data. So, like a brake, it's very important, right, so if the remote operator is pressing the brake, you want it to be first in the vehicle, where the right side of the camera is not as critical, so lower latency for the brake, and then a little bit higher latency for something less important. >> So you've got dynamic, kind of, latent distribution. >> It's all dynamic, realtime, you know, so that's what we do, our real core. We provide this communication, real time, critical layer of communication for the video streaming and back of the data from the remote operator, back and forth all the time. >> So that's one big piece of it. Another big piece of it is the communications between the occupants in the vehicle and the driver. Another really important piece that obviously most people aren't thinking about for autonomous vehicles because they don't have that use case. But that's a pretty important piece of your solution. >> Yeah, that's a big one. I'd say that for this, you don't need to do a lot of innovation. It could be a simple call with the driver remotely. But, we're all about safety, right, and we're all about giving passengers this psychological trust, and it is true, you want to sit in a car that drives 100% of the time. If I tell you that your car today would go in and drives only 95% of the time, you would not buy this car. Same thing with autonomous vehicles. So we provide a safety and service layer. On the safety side, it's about assisting the vehicle when there's an emergency. It could be post-emergency or before it happens. Let's say you're just stuck in the middle of the lane and you don't know what happens. Even if the driver remotely wouldn't actually drive the car, you still want to be able to talk to somebody, right. So, I'd start with first the person, the driver, the human being would greet you when you enter the vehicle. It's an autonomous vehicle, he would say hello, how are you, nice to meet you, my name is let's say Ben- >> Ben is going to be your driver. >> Your driver soon, and Ben is going to tell you that whenever you have a problem, if you need any assistance, he would be there for you. That already gives you like a whole different type of experience, and when you leave the vehicle too, he's not going to be there all the time engaged with the car. The car is going to drive on an autonomous AV system, but at least he's there in case you need him. >> And again, the attention thing, which is an issue, you see with some of the test autonomous cars out there we were talking before we turned the cameras on, where the engineer's got his hands ready to grab the wheel if there's an emergency. That's not really Ben's role here. The car is going to take evasive action in terms of emergency. It's more to get out of like these weird corner cases as you said. >> Correct, it's not a test driver. Today, most autonomous vehicle companies still require and mandate it, it's actually illegal. By the regs, you have to have a person in the car. We also have a person in the car, and we do that same thing, although when Ben is driving, he's not replacing that person. He's just assisting when the autonomous vehicle system would have an issue. >> Right. So the next thing I think that's pretty interesting about your company, as you said, you're a software company. There is hardware components, you can see the back of the car, we'll take some film of the driving station, but you use a lot of off the shelf, really simple hardware to execute this. There's Logitech, little steering wheels are over there, it feels like a big video game, you've got the big, curved Samsung screens, basic cameras on the car, so talk about the opportunity to build a software company and you're leveraging somebody else's autonomous vehicle technology to really get in the middle of this with just software, a pretty cool opportunity. >> I'll tell you what. The best time of my life was earlier this year, when I was just putting this whole thing together because it was plugging in the hardware and the software, I did it together with a team that's also here in the office. Obviously, it was more challenging because from a software person to try and build this hardware, you know, is more challenging, but I'd say today, you can get anything on Amazon, you buy on eBay a part you need, you plug it in and it would just work. So, again, we did a lot of iteration, I'd say we spent a bit more money than we were supposed to. But, that works. >> Right. And then the last piece of the puzzle that I think is fascinating is the way you're going to integrate in with other people's autonomous vehicle, so again, we talked about Waymo up the street, the Google one, Uber is working on theirs, Volvo, every day you read about BMW, et cetera et cetera, so you really get to take advantage of those hardware systems, the sensor systems, the control systems, not only from those autonomous vehicles, but you're seeing now all this stuff that's coming in factory, right, avoidance collision and radar and all types of sensors, so you will have to be able to take advantage of those different platforms and integrate your system into those various platforms. >> Right. So we would work with a company, let's say if it's one of the big OEMs or ride-sharing companies, we would know how their vehicle is set up, all we need for our solution to work is a bunch of cameras and a few modems, right, so cameras everybody have, it's one of the most essential things in an autonomous vehicle- >> Right, right. >> We would just tag into these cameras, use the modems that we need for the software to run, and that's about it. So it's a pretty straightforward solution to allow remote control assistant for autonomous vehicles. >> I'm just curious, when you're talking to customers or potential partners, what is the piece that really resonates with them when you kind of explain your solution and how it fits with what they're trying to accomplish? >> Right, so our solution is really trying to help them reach market faster, so we're not replacing anybody's work. We're adding another layer of support and safety so when yous computer crashed, when your software crashed in the car, we're going to be there with another redundancy system to support with a driver remotely. So, that's what we do at the service level. >> Okay, so can I go take a drive? >> Yeah, sure. Let's do it. >> All right, we're going to check it out, we're going to take a drive. We'll see you in the car. Thanks for watching. (upbeat music)

>> Hey, welcome back everybody! Jeff Frick here with theCUBE. We're at Zuora Subscribe at downtown San Francisco, and every time we go out to conferences, there's a pretty high probability we're going to run into this Cube alumni. Sure enough, here he is, Ray Wang. He's the founder and principal of Constellation Research. Ray, always great to see you. >> Hey Jeff, this is awesome, thanks for having me. >> And close to your hometown, what a thrill! >> This is, it's a local conference! What else can I ask for? >> So what do you think? Subscription economy, these guys have been at it for a while, 1200 people here, I'm a big Spotify fan, Amazon Prime, go back to Costco if you want to go back that far. But it seems to really be taking off. >> It is. About three years ago, digital transformation became a hot topic. And because it became a hot topic, it's really about how do I get products to be more like services. How do I get services to get into insights, and how do I make insights more like experiences and outcomes? And that natural transition as companies make a shift in business models is what's driving and fueling the subscription economy. >> It's interesting. Do you think they had to put the two and two together, that once the products become services now you can tap into that service, you can pull all kinds of data after that thing, you can have analytics, as opposed to shipping that product out the door it goes and maybe you see it every 15,000 miles for a checkup? >> You know what it is? It's basically, about three years ago, people started to realize this. Tien's been talking about this for ages, right? He's been talking about everything's a subscription economy, everything is going to be SAS-ified. And in tech world, everybody got that. But it was when companies like GE, which we saw together, a Caterpillar or a Ford, started to realize, "Hey we can do remote monitoring and sensing "with IOT on our cars, "and I can now figure out what's going on "and monitor them or give an upgrade, "or give a company an upgrade on their appliance, "or give an upgrade on their vehicle, "or do safety and compliance." Then people started realizing, "Oh, wow. "We're not just selling products. "We're in the services business." >> Right. It's funny, if you read the Elon Musk book, how the model years of Teslas, there's no such thing as a model year. It's what firmware version are you on, and then they upgrade. >> Oh, no, that's what we do all the time. You click on a little T, and it's like, boom, firmware. Oh, I get a new upgrade. Only the other day, you touch your head seat, there's like a lumbar support thing, the software popped up for headrest! I never knew I could change the headrest! It literally showed up two months ago. It's unbelievable. >> So, the cool thing, I think, that doesn't get enough play is the difference in the relationship when now you have a subscription-based relationship. That's a monthly recurring or annual recurring, you got to keep delivering value. You got to keep surprising you every morning, when you come out and get in your car, as opposed to that one time purchase. "Adios, we'll see you in however many years "until you get your next vehicle." >> Oh, that's a great example. And the Tesla, we got the Easter eggs over Christmas, right? So the Christmas holiday thing with the Model X that actually did Trans-Siberian Express to the Bellagio fountains with the doors that popped up. You're like, "Hey, what is this thing?" It's just an upgrade that shows up. You're like, "Okay." But you do. You do have to delight customers, you're always capturing their attention, and the fact is, hey, I might buy a toaster. And in that toaster, I might get an upgrade two to three years out. Or maybe, I just buy toasters, and I subscribe to them. And every three years, I get a new toaster. And I can choose between a model L or I can go upsell, get a different color, or I can change out a different set of features, but we're starting to see that. Or maybe, I get a hotel room or a vacation. And that hotel room is at level X, and if I get a couple more members of my family, I get to level Z, and I get to another level, where I lose all the kids, I go back to level A. But the point being is I'm buying a subscription to having an awesome vacation. And that is the type of things that we're talking about here. It's that freedom that Tien was talking about. >> Because he talked about the freedom from obsolescence, freedom from maintenance. There's a whole bunch of benefits that aren't necessarily surfaced when you consume stuff as a service versus consuming it as a product. >> It does. And sometimes it may cost more, but you're trading the convenience, you're trading the velocity of innovation, right? For some people, they just want to own the same thing, they're not going to make the move, but for other people, it's about getting the newest thing, getting delighted, having a new feature. And in some cases, it's about safety, right? This is regulatory compliant or I'm actually doing rev rec correctly, as they were talking about, ASC606. >> Alright, so you're getting out on the road a lot, it's June 6, and I won't tell anyone on air how many miles you already have, because Tamara is probably watching, and she'll be jealous, but biggest surprise is you see here or recently as this digital transformation just continues to gain speed. I'm doing a little research now, and maybe you can help me out. Looking back at digital photography, because it's like, "No, no, no, no, no." for the film, and then it's like, boom. I think these really steep inflection points, or up if you're on the right side, are coming. >> Let's stick to digital photography, that was a great one. There was the point, remember, where we actually had all those disposable cameras at parties that'd get developed, one hour developing. Then we get to back to the point where you just showed up at Costco, dropped something off, you'd get the disk and the photo. Then we had O-Photo, and now we have nothing. Everything just went away because of the phones. These things changed everything, right? I mean, they changed the way we look at photography to the point where, do we even have an album? I was breaking out albums basically three weeks ago, showing my kids, like "Hey, this is what a photo album looks like." And they were completely mystified. "Oh, you print these, how do they get printed?" I mean, they're asking the basic questions. That transformation is what we're having right now. "You own a car?" "You actually buy a PC?" I'm buying compute power. Kilowatts per hour for artificial intelligence in the next year. It's not going to be, I bought the server, I loaded it up, I got it tuned, I got it ready. So yeah, we are in the middle of that shift. But it's the fact that companies are willing to change their business models, and they're willing to break free in the post ERP era. A lot of this is just, my old ERP does not do billing, it doesn't understand the smallest unit of something I sell, and I've got to fix that. And more importantly, my customers, they want to buy it today. The want to buy it in pieces. They want to buy it even smaller pieces. They might buy it every other week, they might buy it-- we have no idea. Yeah, I've got to make sure I can do that. >> It's just interesting too that this is happening now. We're talking about autonomous cars. We see the Waymo cars all the time. The guy from Caterpillar, he's got to a whole autonomous fleet of mining vehicles that are operating today. >> 500,000! He's got 500,000 little trucks. Well, they're not little trucks, they can't fit in this building. >> They're big trucks. Apparently, they tried. >> But they're trying to get these trucks in. We used to think about, like "Hey, these are agricultural vehicles that can be remotely controlled by GPS, they also work for tanks." These are things that are actually doing runs. Now, it's a great reason. Think Australia. Out in Perth, it's about $150,000 to hire a driver. Just to go back and forth. So they figured, "This is just getting ridiculous. "We don't have enough people out here. "We can't convince enough people "to come drive these trucks. "Let's go automate that." That's a lot of the story of where a lot of this came from. >> Or he had a bad night, or broke up with his girlfriend, or distracted about this or that. The whole autonomous vehicle versus regular people driver-- all you've got to do is ride around on your bicycle in your neighborhood, and watch how many people stop at stop signs. Should we answer that question real fast? >> Oh, I do that in California. That's kind of bad, actually. >> Alright Ray. Well, thanks for taking a few minutes. I'm glad you get a weekend at home. Where you off to next, I should ask? >> Oh, it's going to be a crazy next few weeks. I'm going to be in London and Paris and Boston all next week. >> Oh, you're going to eat well. >> I'll try. >> Alright, he's Ray Wang. I'm Jeff Frick. You're watching the Cube from Zuora Subscribe. Thanks for watching.

[Announcer] Live from San Francisco, its theCUBE, covering Informatica World 2017. Brought to you by Informatica. (upbeat music fades) Okay, welcome back everyone. We are live in San Fransisco for Informatica World 2017. This is theCUBE's exclusive coverage, two days. We're on day two, meeting all the top executives, customers, sentures, system integrators, all the best guests here at Informatica World. Part of Informatica's three year coverage with theCUBE. I'm John Furrier with Peter Burris. Our next guest is Bala Kumaresan, who's the senior vice president and general manager of data security for Informatica, formally in charge of engineering, been in R and D, super technical, knowledgeable. Thanks for spending the time to come on theCUBE, appreciate it. >> Thank you. We get to ask you all the tough questions under the hood. What's in the engine of innovation. >> Absolutely. >> Peter: First question, the innovation engine for Informatica, what is it? Describe it quickly. So, the innovation engine of Informatica is entirely metadata driven. It's a data centric metadata driven engine. We call this concept CLAIRE. (John chuckles) It's EI driven, and in a sense, in order for you to make better decisions, you really need to look at your metadata. You really need to-- One of the most important things in security, which actually, current traditional systems lag behind, is the lack of data centricity, resulting in lack of accuracy. If you really want highest time-to-value, and the ability to respond quickly, you really need to be smart enough. Not only out-of-the-box accuracy, but also a period of time, learn, and look into the inputs that are specific to your ecosystem. Specific to that particular environment, and be able to provide actionable insights. Actionability... Actionability without accuracy is basically disaster. >> One of the big drivers in today's market is some of the penalties around governance. Okay, so, there's um, what do you call, G... >> Peter: Oh, GDRP? >> GDRP >> Bala: GDPR! GDPR. >> GDPR, and then Europe is different than North America, but bottom line is you get penalized. There's a risk management piece around the governance, but that's if you've been hacked, so lets talk about the security is fundamental to governance. They play hand in hand. What bet did you guys make on security, and what should people watching know about what Informatica's doing with respect to security, data security? >> I think, great point. General data protection regulation at Europe, that's a regulation that's actually going to go effective May 2018. It's going to be, like, 4% of your annual revenues are going to be the fines in case for every non-compliance and so on. So, we believe that part of the problem that exists today, with or without GDPR, GDPR is today, tomorrow it could be something else, is that lack of versatility, lack of versatility. The entire traditional data security is all about perimeter. You secure the perimeter, and everybody inside the perimeter is trusted. I was just telling, where trust begins, vulnerability seeps in. (Peter chuckles) So you really need to trust and verify. And, what are you protecting? You're really protecting data, so insights into the data is super critical. Our investment on secular source is centered around the Informatica Metadata Company, and insights into the data, how to you translate that into a security prospective. That is precisely what we have done. So, what kind of data you have? Classify the data. How is it being used, where are all that is present, who the users are. Everything is changing. >> So data is the fundamental centroid for security, because perimeter's gone, right? I mean, you got the cloud. I mean, not gone, but its not the fundamental-- >> It becomes the primary citizen in a security regime. >> Yes, yes. So... >> Well said. >> Absolutely. It doesn't matter where your data is. It could be in your relational databases, it could be in the cloud, it could be in your big data systems. It does not matter. It's all about data. Let me give a couple of examples as to the problems that exist today. Once you are inside the perimeter, and you are an authorized user, you pretty much are a trusted person, and then nobody is monitoring your behavior. Are you still the same person, or has somebody hacked into your account? Or did the person turn into... Did his role shift? None of that is being-- So, basically, two main things we are delivering part of our innovation. Role-based access control. It's not user, user... Identity based access control, it is actually role-based access control. If your role is in a IT, versus if your role is a development organization, You, within a company, could move but your privileges actually should be based on the role. That's number one. The second thing is that, look, you... Let's say you have access to all the sales force, because you are a sales force, you're actually part of our sales team. You typical patterns are that you're look at 10 records, 20 records a day, even though you have access to the million records, right? But, the base line and the behavior changes. They're actually indicate something. So this is part of trust and verify. You trust a person, but you also need to verify. Keep up with the changes, and that's fundamental to the data centric security. >> I want to amplify a piece of that, and tell me if I'm so appropriately. Role based security, I would actually ask, are we going to move to something we might call context based security, where context is what do you do. The role is part of what you do. So it says, what do you do, and who are you, and how are you doing it. So that's number one, and number two is, how does this relate back to some of the metadata initiatives that you guys have, where increasingly, some of the most crucial metadata will be the metadata that's ultimately used to put bounds on how the data gets employed. >> Let me answer that question in three different dimensions. Number one, yes. Absolutely. Role is part of the context. It's not the entire the entire context, but role is part of the context. >> Peter: Correct. >> Any protection, and any access to the protected information needs to be role based. Number two, the data context that we have in our product, where we go and catalog and classify all the data, that is very much used in prioritizing. For example, an alarm that goes on in a school during the school hours versus an alarm that goes on in a junkyard. They're both alarms. Today most of the traditional security actually kind of categorizes them as similar. An alarm went off. But, are they the same? No, they are not. So that's where the second level of the context. The third level of the context is in terms of the real... Basically, third level of the context is actually, what do you need to be in compliance with. What kind of usage is allowed? It's actually nothing to do with that particular usage itself, its actually got to do with a whole bunch of other safeguards that you need to manage. That's where our central policy management comes in the picture. So with these three contexts, the business context, the user context, and the category or the classification of the data context, it is totally-- >> All that has to be part of the security regime. >> Absolutely. That's actually, the metadata that we have, which drives those accurate decisions, accurate decisions for prioritization as well as detection, and the right protection. >> So here's a question, then. Again, I'm going to test this on you. Historically people have separated data, data sec-- metadata, data security. In the future, how do we keep those separate? We have to start seeing how they come together, right? >> I think, fantastic, fantastic question. Our view is that data governments is about... The governance actually has a slice across many dimensions. One of them is the data stewardship, the provenance, and the quality of the data, and so on. The other part is actually about data security governance, in terms of what kind of safeguards the role based access control. Really, what kind of risks that you are entitled to and, how are you managing the risks. So, that's our views. So, when we look at metadata, the metadata is actually driving multiple decisions. One of them is quality. The other one is risk. The other one is protection. So, we see this as a unifier bringing things together. Informatica is uniquely positioned with our Axon, EAC, and Secure@Source products. In fact, one of the things that we are announcing in Informatica World is actually about our GDPR bundle, because GDPR is actually about, as much about data governance as about privacy, and also it is about policy driven data protection. >> Well, privacy, policy, inform. The governance regime. You can't separate. It's not just about compliance, and I'll give you-- I'm going to test one more thing on you. At some point in time, as we think about digital business and the idea that a digital business is defined more by its use of data assets. Otherwise its just a business, and we want to protect our data. We're also worried about how we share our data, and how others share data with us. We want to make sure that we are not inappropriately exploiting somebody else's data because we don't want to create a billion dollar business that fundamentally, upon inspection, was predicated on the misappropriation of somebody else's data. >> Absolutely. You are touching upon the consent, and the consent control, and what kind of validations we have in place to evaluate... This might not be popular. What I'm going to say is not necessarily popular, right? I think it goes back data ethics, as well. I think companies consider customer data, partner data as their asset. They, 20 years, 30 years of how the data's been used, I think the realization is going to sink in. The realization is already sinking in with respect to the ethics, with respect to the trust-- >> John: That it's not their data. >> It's not their asset. >> What's sinking in is it's not their data assets. >> Its not their data. They are, in fact... They are, in fact, obligated. They're, in fact, supposed to use that with care. They're, in fact, accountable for that data. So, while regulations are starting to put those things in place, with GDPR being one and then every other... Geography is going to come up with its own set of modifications similar to that. I think this is a fantastic opportunity for companies to go to that higher order, and really start to think this as, why they are ethical. What is the ethics that they want to put in place, above and beyond what the regulations talk about. I think Informatica is uniquely positioned with our metadata driven strategy, with our metadata cloud engine which is driving solutions across quality, governance, and security as well as constant control over-- Yeah. >> Well let me make one more point on that. It comes back to this fundamental notion of your brand is the promise you're making to the marketplace. What you just described will have more impact on company brands in 10 years, and probably even five years, than the characteristics of the products they sell very often. >> Absolutely. If I'm an investor, I'm thinking about reputation. What is the company's reputation? What kind of pull effect the reputation has towards expanding the business. That is where the ethics, actually, is in higher order of existence. Where, people want to partner with you. People what to do business with you, and I think that's actually where we can be very helpful. I mean, there're already intelligent solutions, use them intelligently. >> Its interesting you bring up data ethics, because I wanted to jump in on that, because if digital transformation, if we believe that its happening, and of course everyone's talking about business transformation, which is the outcome of digital transformation, ethics transforms too, digitally. >> Bala: Digitally, yes. >> So, where is, in your mind, the ethics with data? Is there, I mean there's articles that's thought leadership around it, but, is it actually in use. Do actually people have data ethics in your opinion? Is this something that's talked about but not walked? Your thoughts on that, reactions that-- >> I think it's an evolving concept. So far, companies have been taking advantage of the data. The evolving concept is going to catch on. It is actually catching on. Analysts are actually talking about it. I think we are thinking about it. We are thinking about what we are building is actually kind of going to help customers go there. But I want to also separate it. There is actually something that is at a higher level of existence versus what is really, absolutely necessary and need it today? Policy driven data protection while we are able to standardize the policies across the enterprise, across all your data silos. That is super critical, to get the immediate problem resolved while we can start to build on that's access towards the ethics. >> This economy's a scale. You can't just jump the data ethics and be ethical. You got to, you got to build your way up. Have a trajectory and tract record of foundational-- >> Here's what I say John, and Bala, you know, tell me if you think I'm wrong, but... >> Make sure you say if you think he's wrong. >> Yeah, please do, cause I have been wrong in the past. You said something very interesting. You said, "Yeah, everybody's talking about data, >> Data ethics. or additional business. And that's just it. They're doing it, but they're not doing plan-fully, because we often don't understand exactly what it is, and the process of thinking though the ethics is crutial to informing that planful approach to thinking about digital business. At least, that's my perspective. What do you think about that, Bala? >> I think the versability. The versability at the board level, the versability at the senior exec's level, as to where you stand. What is your risk? What is your compliance scorecard? Do you have a plan in place where there's an informed remediation plan? Did we actually allocate sufficient budget? Its not about budget justification, its actually about did you allocate budget for this risk. Also, do we have systems in place that are continuously assessing and reassessing to basically drive towards risk correction and towards maintaining the compliance. Those are key, and I think that addresses what you are saying, and I think I agree with you. >> So, lets take this very practically. If you look at the industry, you see companies like Apple and Microsoft being very clear about how they're going to use their customer's data. >> John: Facebook? (John laughs) >> You see Facebook and Google being less clear about how they're use your data. You see Amazon right in the middle, and people wondering which way they're going to go. This is a huge issue. Not to talk about it's security level, but just overall business model. This is going to have an enormous impact on a global basis of how we think about digital business and the role data's going to play in creating new shareholder customer value-- >> If data's the new gold, so here's my take on this. Love to get the reaction. If data's the new oil, if data's the new gold, the new heartbeat, whatever metaphor you use. If its the new gold, let's just say its the gold. That's valuable. So, the value will shift to whoever has the data. Someone's going to wake up and say, hey wait a minute. That's my data. And I think you're starting to see that a little bit with Facebook certainly. Less Google because the utility is pretty well intergraded, but at some point the utility value has to be greater than than the value of the data gold, if you will, cause otherwise, I will demand the data back. So I think there's end user, or the primary use of the data, the primary user of data-- >> This is a very coarse view, but I wonder if Uber right now is wondering how they could've used data security different, relative to the 200 million, what ever it is, lawsuit that Waymo's bought against them. So this issue of ethics and the role the data's going to play is going to have enormous implications-- >> John: Love that conversation on the eithics side. >> Yeah, I think actually if you look at the way companies use data, and then the way you lay out in terms of where different companies are, that is actually a spectrum of how you could question them. One is actually how they can help the consumer. That's something that we all love. And then there's an absolute exploitation. >> John And Peter: Yes. And then there is something in the middle, and ethics is actually not about exploitation. Ethics is actually about keeping people informed. Letting them know exactly-- >> John: Transparency. >> Transparency, and-- >> John] There's always an underbelly everywhere. >> Peter: Well, you can a bad ethics. (Bala laughs) >> All those bad actors out there. Okay, we got to wrap it up. I want to get one quick comment from Bala. Obviously, I can't help but jump to blockchain when I start thinking about security. Thoughts on blockchain. How's that going to be relevant, if any. Obviously, supply chain. You're seeing some indications there. Blockchain has a potential mechinism-- >> The blockchain technology is very compelling. It has the integrity. Its basically... One of the things that I've always talked about with my team and in general for prag development is that security has always been in the past, as an afterthought. As something that sits outside, and if you were to go back and design some of the systems that we built in the last 20, 25 years, with so much emphasis on privacy and compliance and security and protecting breach, wouldn't security be built in, part of the design? Part of the code. >> John: The primary (John laughs) >> part of the design. Right? So, very appealing from that point of view. The applicability of blockchains today is mostly around transactional ledgers and... basically transfer of value, and so on. I think one of the, you know-- It also has, it also comes with certain baggage. The blockchain remembers everything. You know, (John laughs) So, let me zero in on... I think everybody's trying to figure out how to actually apply blockchain beyond the traditional, beyond the ledger and so on. I think it's going to have a place. Its going to have a place in... We're already starting to see that some applications where shot-dam contracts like, for example, you're doing a building contract that is a supplier, that is actually a valuer, and it is a project. It exists for a temporary period and it goes away. All of the coordinating parties are coordinating with confidence. They're sharing and colliaberating with confidence. Blockchain actually gives them confidence, because it has-- >> So it's relevant, but it's emmerging. Still at, not, it's early innings-- >> It's relevant. It's emerging. We are very closly looking at it. I think we already have a play there, where one of the main and most important things that blockchains mean is that... Identity. As a unique identity. If you look at some of the old prducts... Customer 360, its all abnout quality of customer data. Data quality for customer data, right? There, perhaps, is a way for us to integrate the blockchain. The other place where we are already looking at is can we consume the information in the blockchain to enhance our metadata? Of course we can. >> Peter: Yeah. (Peter laughs) >> So, those are two low hanging fruits, and of course we'll keep it... We'll stay-- >> We'll have to get you down to our studio in Palo Alto. We'll do a whole segiment on unpacking blockchain. I love blockchain. I think, my personal belief is yeah, there's some low hanging fruit that'll use-cases, but if theirs money to be had in reconfiguring parties working together to create wealth-- I have some crazy thoughts on this, actually. (laughter) So we can not stipulate, because-- >> John: You're definitely coming to Palo-- We're going to go to where you are! >> Did we run out of time, or-- >> John: We're running out of time. Let's follow up Bala. Great conversation. Great fireside. Show's like a fireside chat. That was phenomoneal. Thanks for sharing the data and the insight. We're live here in San Fransisco for Informatica World 2017. More exclusive coverage from theCUBE. I'm John Furrier with Peter Burris, after this short break. Stay with us. (upbeat music)