(bright music) >> Hey, welcome back everyone, theCUBE coverage here at AWS re:Mars 2022. I'm John, your host of the theCUBE. re:Mars, part of the three re big events, re:Invent is the big one, re:Inforce the security, re:MARS is the confluence of industrial space, of automation, robotics and machine learning. Got a great guest here, Muhammad Faisal senior consultant solutions architect at Capgemini. Welcome to theCUBE. Thanks for coming on. >> Thank you. >> So we, you just we're hearing the classes we had with the professor from Okta ML from Washington. So he's in the weeds on machine learning. He's down getting dirty with all the hardcore, uncoupling it from hardware. Machine learning has gone really super nova in the past couple years. And this show points to the tipping point where machine learning's driving space, it's driving robotics industrial edge at unprecedented rates. So it's kind of moving from the old I don't want to say old, couple years ago and the legacy AI, I mean, old school AI is kind of the same new school with a twist it's just modernized and has faster, cheaper, smaller chips. >> Yeah. I mean, but there is a change also in the way it's working. So you had the classical AI, where you are detecting something and then you're making an action. You are perceiving something, making an action, you're detecting something, and you're assuming something that has been perceived. But now we are moving towards more deeper learning, deep. So AI, where you have to train your model to do things or to detect things and hope that it will work. And there's like, of course, a lot of research going on into explainable AI to help facilitate that. But that's where the challenges come into play. >> Well, Muhammad , first let's take, what do you do over there? Talk about your role specifically. You're doing a lot of student architecting around AI machine learning. What's your role? What's your focus. >> Yeah. So we basically are working in automotive to help OEMs and tier-one suppliers validate ADAS functions that they're working on. So advanced driving assistance systems, there are many levels that are, are when we talk about it. So it can be something simple, like, you know, a blind spot detection, just a warning function. And it goes all the way. So SAE so- >> So there's like the easy stuff and then the hard stuff. >> Muhammad : Exactly. >> Yeah. >> That's what you're getting at. >> Yeah. Yeah. And, and the easy stuff you can test validate quite easily because if you get it wrong. >> Yeah. >> The impact is not that high. The complicated stuff, if you have it wrong, then that can be very dangerous. (John laughs) >> Well, I got to say the automotive one was one was that are so fascinating because it's been so archaic and just in the past recent years, and Tesla's the poster child for this. You see that you go, oh my God, I love that car. I want to have a software driven car. And it's amazing. And I don't get a Tesla on now because that's, it's more like I should have gotten it earlier. Now I'm going to just hold my ground. >> Everyone has- >> Everyone's got it in Palo Alto. I'm not going to get another car, no way. So, but you're starting to see a lot of the other manufacturers, just in the past five years, they're leveling up. It may not be as cool and sexy as the Tesla, but it's, they're there. And so what are they dealing with when they talk about data and AI? What's the, what's some of the challenges that you're seeing that they're grappling with in terms of getting things integrated, developing pipelines, R and D, they wrangling data. Take us through some of the things. >> Muhammad: I mean, like when I think about the challenges that autonomous or the automakers are facing, I can think of three big ones. So first, is the amount of data they need to do their training. And more importantly, the validation. So we are talking about petabytes or hundred of petabytes of data that has to be analyzed, validated, annotated. So labeling to create gen, ground truth processed, reprocessed many times with every creation of a new software. So that is a lot of data, a lot of computational power. And you need to ensure that all of the processing, all of handling of the data allows you complete transparency of what is happening to the data, as well as complete traceability. So your, for home allocations, so approval process for these functions so that they can be released in cars that can be used on public roads. You need to have traceability. Like you can, you are supposed to be able to reproduce the data to validate your work that was done. So you can, >> John: Yeah >> Like, prove that your function is successful or working as expected. So this, the big data is the first challenge. I see that all the automotive makers are tackling. The second big one I see is understanding how much testing is enough. So with AI or with classical approach, you have certain requirements, how a function is supposed to work. You can test that with some test cases based on your architecture, and you have a successful or failed result. With deep learning, it gets more complicated. >> John: What are they doing with deep learning? Give an example of some of things. >> I mean, so you are, you need to then start thinking about statistics that I will test enough data with like a failure rate of potentially like 0.0, 0.1%. How much data do I need to test to make sure that I am achieving that rate. So then we are talking about, in terms of statistics, which requires a lot of data, because the failure rate that we want to have is so low. And it's not only like, failure in terms of that something is always detected, and if it's there, but it's also having like, a low false positive rate. So you are only detecting objects which are there and not like, phantom objects. >> What's some of the trends you're seeing across the client base, in terms of the patterns that they're all kind of, what, where's the state of their mindset and position with AI and some of the work they're doing, are they feeling, you feel like they're all crossed over across the chasm so to speak, in terms of executing, are they still in experimental mode in driving with the full capabilities is conservative or is it progressive? >> Muhammad: I mean, it's a mixture of both. So I'm in German automotive where I'm from, there is for functions, which are more complicated ones. There's definitely hesitancy to release them too early in the car, unless we are sure that they are safe. But of course, for functions which are assisting the drivers everyday usage they are widely available. Like one of the things like, so when we talk about this complex function. >> John: Highly available or available? >> Muhammad: I would say highly available. >> Higher? Is that higher availability and highly available. >> Okay. Yeah. (both laughing) >> Yeah, so. >> I know there's a distinction. >> Yeah. I mean >> I bring up as a joke cuz of the Jedi contract. (Muhammad laughs) >> I mean, in like, our architecture. So when we are developing our solution, high availability is one of our requirements. It is highly available, but the ADAS functions are now available in more and more cars. >> John: Well, latency, man. I mean, it's kind of a joke of storage, but it's a storage joke, but you know, it's latency, you got it, okay. (Muhammad laughs) But these are decisions that have to be made. >> Muhammad: They... >> I mean. >> Muhammad: I mean, they are still being made. >> So I mean, we are... >> John: Good. >> We haven't reached like, level five, which is the highest level of autonomous driving yet on public roads. >> John: That's hard. That's hard to do. >> Yeah. And I mean, the biggest difference, like, as you go above these levels is in terms of availability. So are they these functions? >> John: Yeah. >> Can they handle all possible scenarios or are they only available in certain scenarios? And of course the responsibility. So, it's, in the end, so with Tesla, you would be like, if you had a one you would be the person who is in control or responsible to monitor it. >> John: Yeah. But as we go >> John: Actually the reason I don't have a Tesla all my family would want one. I don't want to get anyone a Tesla. >> But I mean, but that's the sort the liabilities is currently on you, if like, you're not monitoring. >> Allright, so, talk about AWS, the relationship that Capgemini has with AWS, obviously, the partnerships there, you're here and this show is really a commitment to, this is a future to me, this is the future. >> Muhammad: Yeah. >> This is it. All right here, industrial, innovation's going to come massive. Back-office cloud, done deal. Data centers, hybrid somewhat multi-cloud, I guess. But hybrid is a steady state in the back-office cloud, game over. >> Muhammad: Yeah. >> Amazon, Azure, Google, Alibaba done. So super clouds underneath. Great. This is a digital transformation in the industrial area. >> Muhammad: Yeah. >> This is the big thing. What's your relationship with AWS >> Muhammad: So, as I mentioned, the first challenge, data, like, we have so much data, so much computational power and it's not something that is always needed. You need it like on demand. And this is where like a hyperscale or cloud provider, like AWS, can be the key to achieve, like, the higher, the acceleration that we are providing to our customers using our technology built on top of AWS services. We did a breakout session, this during re:MARS, where we demonstrated a couple of small tools that we have developed out of our offering. One of them was ability to stream data from the vehicle that is collecting data worldwide. So during the day when we did it from Vegas, driving on the strip, as well as from Germany, and while we are while this data is uploaded, it's at the same time real time anonymized to make sure it you're privacy aligned with the, the data privacy >> Of course. Yeah. That's hard to do right there. >> Yeah. And so the faces are blurred. The licenses are blurred. We also, then at the same time can run object detection. So we have real time monitoring of what our feed is doing worldwide. And... >> John: Do you, just curious, do you do that blurring? Is that part of a managed service, you call an API or is that built into the go? >> Muhammad: So from like part of our DSV, we have many different service offerings, so data production, data test strategy orchestration. So part of data production is worldwide data collection. And we can then also offer data management services, which include then anonymization data, quality check. >> John: And that's service you provide. >> Yeah. >> To the customer. Okay. Got it. Okay. >> So of course, like, in collaboration with the customer, so our like, platform is very modular. Microservices based the idea being if the customer already has a good ML model for anonymization, we can plug it into our platform, running on AWS. If they want to use it, we can develop one or we can use one of our existing ones or something off the shelf or like any other supplier can provide one as well. And we all integrate. >> So you are, you're tight with Amazon web services in terms of your cloud, your service. It's a cloud. >> Yeah. >> It's so Capgemini Super Cloud, basically. >> Exactly. >> Okay. So this we call we call it Super Cloud, we made that a thing and re:Invent Charles Fitzgerald would disagree but we will debate him. It's a Super Cloud, but okay. You got your Super Cloud. What's the coolest thing that you think you're doing right now that people should pay attention to. >> I mean, the cool thing that we are currently working on, so from the keynote today, we talked about also synthetic data for validation. >> John: Now That was phenomenal. So that was phenomenal. >> We are working on digital twin creation. So we are capturing data in real world creating a virtual identity of it. And that allows you the freedom to create multiple scenarios out of it. So that's also something where we are using machine learning to determine what are the parameters you need to change between, or so, you have one scenario, such as like, the cut-in scenario and you can change. >> John: So what scenario? >> A cut-in scenario. So someone is cutting in front of you or overtake scenario. And so, I mean, in real world, someone will do it in probably a nicer way, but of course, in, it is possible, at some point. >> Cognition to the cars. >> Yeah. >> It comes up as a vehicle. >> I mean, at some point some might, someone would be very aggressive with it. We might not record it. >> You might be able to predict too. I mean, the predictions, you could say this guy's weaving, he's a potential candidate. >> It it is possible. Yes. But I mean, but to, >> That's a future scenario. >> Ensure that we are testing these scenarios, we can translate a real world scenario into a digital world, change the parameters. So the distance between those two is different and use ML. So machine learning to change these parameters. So this is exciting. And the other thing we are... >> That is pretty cool. I will admit that's very cool. >> Yeah. Yeah. The other thing we like are trying to do is reduce the cost for the customer in the end. So we are collecting petabytes of data. Every time they make updates to the software, they have to re-simulate it or replay this data, so that they can- >> Petabytes? >> Petabytes of data. And, and physically sometimes on a physical hardware in loop device. And then this >> That's called a really heavy edge. You got to move, you don't want to be moving that around the Amazon cloud. >> Yeah. That that's, that's the challenge. And once we have replayed this or re-simulated it. we still have to calculate the KPIs out of it. And what we are trying to do is optimize this test orchestration, so that we are minimizing the REAP simulation. So you don't want the data to be going to the edge, >> Yeah. >> Unnecessarily. And once we get this data back to optimize the way we are doing the calculation, so you're not calculating- >> There's a huge data, integrity management. >> Muhammad: Yeah. >> New kind of thing going on here, it's kind of is it new or is it? >> Muhammad: I mean, it's- >> Sounds new to me. >> The scale is new, so- >> Okay, got it. >> The management of the data, having the whole traceability, that has been in automotive. So also Capgemini involved in aerospace. So in aerospace. >> Yeah. >> Having this kind of high, this validation be very strictly monitored is norm, but now we have to think about how to do it on this large scale. And that's why, like, I think that's the biggest challenge and hopefully what we are trying to, yeah, solve with our DSV offering. >> All right, Muhammad, thanks for coming on theCUBE. I really appreciate it. Great way to close out re:MARS, our last interview our the show. Thanks for coming on. Appreciate your time. >> I mean like just one last comment, like, so I think in automotive, like, so part of the automation the future is quite exciting, and I think that's where like- >> John: Yeah. >> It's, we have to be hopeful that like- >> John: Well, the show is all about hope. I mean, you had, you had space, moon habitat, you had climate change, potential solutions. You have new functionality that we've been waiting for. And, you know, I've watch every episode of Star Trek and SkyNet and kind of SkyNet going on air. >> The robots. >> Robots running cubes, robot cubes host someday. >> Yeah. >> You never know. Yeah. Thanks for coming on. Appreciate it. >> Thank you. Okay. That's theCUBE here. Wrapping up re:MARS. I'm John Furrier You're watching theCUBE, stay with us for the next event. Next time. Thanks for watching. (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)

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Hello everyone. Welcome back to the cubes. Day three coverage of ADAS reinvent in Las Vegas. It's the cubes coverage. Want to thank Intel for being the headline sponsor for the cube two sets. Without Intel, we wouldn't make it happen. We're here extracting the signal from the noise as usual. Wall-to-wall SiliconANGLE the cube coverage. I'm John Feria with student men and men doing a keynote analysis from Verner Vogel. Stu, you know Vernor's, they always, they always got the disc, the format jazzy kicks it off. You get the partner thing on day two and then they say Verner flask could nerd out on all the good stuff. Uh, containers. Coobernetti's all under the hood stuff. So let's jump in a keynote analysis. What's your take? What's Verner's posture this year? What's the vibe? What's the overall theme of the keynote? >>Well, well, first of all, John, to answer the question that everybody asks when Werner takes the stage, this year's t-shirt was posse. So Verner usually either has a Seattle band or it's usually a Dutch DJ, something like that. So he always delivers it. The geek crowd there. And really after seeing it of sitting through Werner's keynote, I think everybody walks out with AWS certification because architecturally we dig into all these environments. So right. You mentioned they started out with the master class on how Amazon built their hypervisor. Super important. Nitro underneath is the secret sauce. When they bought Annapurna labs, we knew that those chips would be super important going forward. But this is what is going to be the driver for outposts. It is the outpost is the building block for many of the other services announced this week. And absolutely the number one thing I'm hearing in the ecosystems around outpost but far gate and firecracker micro databases and managing containers. >>Um, they had some enterprises up on stage talking about transformation, picking up on the themes that Andy started with his three hour keynote just yesterday. But um, it's a lighter on the news. One of the bigger things out there is we will poke Amazon about how open and transparent they are. About what they're doing. And one of the things they announced was the Amazon builders library. So it's not just getting up on stage and saying, Hey, we've got really smart people and we architected these things and you need to use all of our tools, but Hey, this is how we do things. Reminded me a little bit of a, you know, just echoes of what I heard from get lab, who of course is fully open source, fully transparent, but you know, Amazon making progress. It's Adrian Cockcroft and that team has moved on open source, the container group. >>I had a great interview yesterday with Deepak saying, and Abby fuller, the container group actually has a roadmap up on containers. They're so sharing a lot of deep knowledge and good customers talk about how they're taking advantage, transforming their business. In serverless, I mean, John, coming out of Andy's keynote, I was like, there wasn't a lot of security and there wasn't a lot of serverless. And while serverless has been something that we know is transforming Amazon underneath the covers, we finally got to hear a little bit more about not just Lambda but yes, Lambda, but the rest of it as to how serverless is transforming underneath. >>You know ain't Jessie's got along three hour keynote, 30 announcements, so he has to cut save some minutes there. So for Verner we were expecting to go in a little bit more deeper dive on this transformational architecture. What did you learn about what they're proposing, what they're saying or continuing to say around how enterprises should be reborn in the cloud? Because that's the conversation here and again, we are, the memes that are developing are take the T out of cloud native. It's cloud naive. If you're not doing it right, you're going to be pretty naive. And then reborn in the cloud is the theme. So cloud native, born in the cloud, that's proven. Reborn in the cloud is kind of the theme we're hearing. Did he show anything? Did he talk about what that architecture is for transformation? Right. >>Did actually, it was funny. I'm in a watching the social stream. While things are going on. There was actually a cube alumni that I follow that we've interviewed at this show and he's like, if we've heard one of these journeys to you know, transformation, haven't we heard them all and I said, you know, while the high level message may be similar is I'm going to transfer math transform, I'm going to use data. When you looked at what they were doing, and this is a significant, you know, Vanguard, you know the financial institutions, Dave Volante commenting that you know the big banks, John, we know Goldman Sachs, we know JP Morgan, these banks that they have huge it budgets and very smart staffs there. They years ago would have said, Oh we don't need to use those services. We'll do what ourselves. Well Vanguard talking about how they're transforming rearchitecting my trip services. >>I love your term being reborn cloud native because that is the architecture. Are you cloud native or I used to call it you've kind of cloud native or kinda you know a little bit fo a cloud. Naive is a great term too. So been digging in and it is resonating is to look, transformation is art. This is not trying to move the organizational faster than it will naturally happen is painful. There's skillsets, there's those organizational pieces. There are politics inside the company that can slow you down in the enterprise is not known for speed. The enterprises that will continue to exist going forward better have taken this methodology. They need to be more agile and move. >>Well the thing about the cloud net naive thing that I like and first of all I agree with reborn in the cloud. We coined the term in the queue but um, that's kinda got this born again kind of vibe to it, which I think is what they're trying to say. But the cloud naive is, is some of the conversations we're hearing in the community and the customer base of these clouds, which is there are, and Jesse said it is Kino. There are now two types of developers and customers, the ones that want the low level building blocks and ones who want a more custom or solution oriented packages. So if you look at Microsoft Azure and Oracle of the clouds, they're trying to appeal to the folks that are classic it. Some are saying that that's a naive approach because it's a false sense of cloud, false sense of security. >>They got a little cloud. Is it really true? Cloud is, it's really true. Cloud native. So it's an interesting confluence between what true cloud is from a cloud native standpoint and yet all the big success stories are transformations not transitions. And so to me, I'm watching this it market, which is going to have trillions of dollars in, are they just transitioning? I old it with a new coat of paint or is it truly a skill, a truly an architectural transformation and does it impact the business model? That to me is the question. What's your reaction to that? >>Yeah, so John, I think actually the best example of that cloud native architecture is the thing we're actually all talking about this week, but is misunderstood. AWS outpost was announced last year. It is GA with the AWS native services this year. First, the VMware version is going to come out early in 2020 but here's why I think it is super exciting but misunderstood. When Microsoft did Azure stack, they said, we're going to give you an availability zone basically in your data center. It wasn't giving you, it was trying to extend the operational model, but it was a different stack. It was different hardware. They had to put these things together and really it's been a failure. The architectural design point of outpost is different. It is the same stack. It is an extension of your availability zone, so don't think of it of I've got the cloud in my data center. >>It's no, no, no. What I need for low latency and locality, it's here, but starting off there is no S3 in it because we were like, wait, what do you mean there's no S3 in it? I want to do all these services and everything. Oh yeah. Your S three bucket is in your local AC, so why would you say it's sharing? If you are creating data and doing data, of course I want it in my S three bucket. You know that, that that makes that no, they're going to add us three next year, but they are going to be very careful about what surfaces do and don't go on. This is not, Oh Amazon announces lots of things. Of course it's on outpost. It has the security, it has the operational model. It fits into the whole framework. It can be disconnected song, but it is very different. >>I actually think it's a little bit of a disservice. You can actually go see the rack. I took a selfie with it and put it out on Twitter and it's cool gear. We all love to, you know, see the rack and see the cables and things like that. But you know, my recommendation to Amazon would be just put a black curtain around it because pay no attention to what's here. Amazon manages it for you and yes, it's Amazon gear with the nitro chip underneath there. So customers should not have to think about it. It's just when they're doing that architecture, which from an application standpoint, it's a hybrid architecture. John, some services stay more local because of latency, but others it's that transformation. And it's moving the cloud, the edge, my data center things are much more mobile. Can you to change and move over? >>Well this spring you mentioned hybrid. I think to me the outpost announcement in terms of unpacking that is all about validation of hybrid. You know, VMware's got a smile on their face. Sanjay Poonen came in because you know Gelson you're kind of was pitching hybrid, you know, we were challenging him and then, but truly this means cloud operations has come. This is now very clear. There's no debate and this is what multi-cloud ultimately will look like. But hybrid cloud and public cloud is now the architecture of the of it. There's no debate because outpost is absolute verification that the cloud operating model with the cloud as a center of gravity for all the reasons scale, lower costs management, but moving the cloud operations on premises or the edge proves hybrid is here to stay. And that's where the money is. >>So John, there's a small nuance I'll say there because hybrid, we often think of public and private as equal. The Amazon positioning is it's outpost. It's an extension of what we're doing. The public cloud is the main piece, the edge and the outposts are just extensions where we're reaching out as opposed to if I look at, you know what VMware's doing, I've got my data center footprint. You look at the HCI solution out there. Outpost is not an HCI competitor and people looking at this misunderstand the fundamental architecture in there. Absolutely. Hybrid is real. Edge is important. Amazon is extending their reach, but all I'm saying is that nuance is still, Amazon has matured their thinking on hybrid or even multi-cloud. When you talk to Andy, he actually would talk about multi-cloud, but still at the center of gravity is the public cloud and the Amazon services. It's not saying that, Oh yeah, like you know, let's wrap arounds around all of your existing, >>well, the reason why I liked the cloud naive, take the T out of cloud native and cloud naive is because there is a lot of negativity around what cloud actually is about. I forget outpost cloud itself, and if you look at like Microsoft for instance, love Microsoft, I think they do an amazing work. They're catching up as fast as they can, but, and they play the car. Well we are large scale too, but the difference between Amazon and Microsoft Azure is very clear. Microsoft's had these data centers for MSN, I. E. browsers, global infrastructure around the world for themselves and literally overnight they have to serve other people. And if you look at Gardner's results, their downtime has been pretty much at an all time high. So what you're seeing is the inefficiencies and the district is a scale for Microsoft trying to copy Amazon because they now have to serve millions of customers anywhere. This is what Jessie was telling me in my one-on-one, which is there's no compression algorithm for experience. What he's basically saying is when you try to take shortcuts, there's diseconomies of scale. Amazon's got years of economies of scale, they're launching new services. So Jesse's bet is to make the capabilities. The problem is Microsoft Salesforce do is out there and Amos can't compete with, they're not present and they're going into their customers think we got you covered. And frankly that's working like real well. >>Yeah. So, so, so John, we had the cube at Microsoft ignite. I've done that show for the last few years. And my takeaway at Microsoft this year was they build bridges. If you are, you know, mostly legacy, you know, everything in my data center versus cloud native, I'm going to build your bridge. They have five different developer groups to work with you where you are and they'll go there. Amazon is a little bit more aggressive with cloud native transformation, you know, you need to change your mindset. So Microsoft's a little bit more moderate and it is safer for companies to just say, well, I trust Microsoft and I've worked with Microsoft and I've got an enterprise license agreement, so I'll slowly make change. But here's the challenge, Don. We know if you really want to change your business, you can't get there incrementally. Transformation's important for innovation. So the battle is amazing. You can't be wrong for betting on either Microsoft or Amazon these days. Architecturally, I think Amazon has clear the broadest and deepest out there. They keep proving some of their environments and it has, >>well the economies of scale versus diseconomies scale discussion is huge because ultimately if Microsoft stays on that path of just, you know, we got a two and they continue down that path, they could be on the wrong side of the history. And I'll tell you why I see that and why I'm evaluating Microsoft one, they have the data center. So can they reach tool fast enough? Can they, can they eliminate that technical debt because ultimately they're, they're making a bet. And the true bet is if they become just an it transition, they in my opinion, will, will lose in the long run. Microsoft's going all in on, Nope, we're not the old guard. We're the new guard. So there's an interesting line being formed too. And if Microsoft doesn't get cloud native and doesn't bring true scale, true reliability at the capabilities of Amazon, then they're just going to be just another it solution. And they could, that could fall right on there, right on their face on that. >>And John, when we first came to this show in 2013 it was very developer centric and could Amazon be successful in wooing the enterprise? You look around this show, the answer was a resounding yes. Amazon is there. They have not lost the developers. They're doing the enterprise. When you talk to Andy, you talked about the bottoms up and the top down leadership and working there and across the board as opposed to Google. Google has been trying and not making great progress moving to the enterprise and that has been challenging. >>Oh, I've got to tell you this too. Last night I was out and I got some really good information on jet eye and I was networking around and kind of going in Cognito mode and doing the normal and I found someone who was sharing some really critical information around Jedi. Here's what I learned around this is around Microsoft, Microsoft, one that Jed ideal without the capabilities to deliver on the contract. This was a direct quote from someone inside the DOD and inside the intelligence community who I got some clear information and I said to him, I go, how's that possible? He says, Microsoft one on the fact that they say they could do it. They have not yet proven any capabilities for Jedi. And he even said quote, they don't even have the data centers to support the deal. So here you have the dynamic we save, we can do it. Amazon is doing it. This is ultimately the true test of cloud naive versus cloud native. Ask the clouds, show me the proof, John, you could do it and I'll go with, >>you've done great reporting on the jet. I, it has been a bit of a train wreck to watch what's going on in the industry with that because we know, uh, Microsoft needs to get a certain certification. They've got less than a year. The clock is ticking to be able to support some of those environments. Amazon could support that today. So we knew when this started, this was Amazon's business and that there was the executive office going in and basically making sure that Amazon did not win it. So we said there's a lot of business out there. We know Amazon doing well, and the government deals Gelsinger was on record from VMware talking about lots of, >>well here's, here's, here's the thing. I also talked to someone inside the CIA community who will tell me that the spending in the CIA is flat. Okay. And the, the flatness of the, of the spending is flat, but the demand for mission support is going exponential. So the cloud fits that bill. On the Jedi side, what we're hearing is the DOD folks love this architecture. It was not jury rig for Amazon's jury rig for the workload, so that they're all worried that it's going to get scuttled and they don't want that project to fail. There's huge support and I think the Jedi supports the workload transformational thinking because it's completely different. And that's why everyone was running scared because the old guard was getting, getting crushed by it. But no one wants that deal to fail. They want it to go forward. So it's gonna be very interesting dynamics do if Microsoft can't deliver the goods, Amazon's back in the driver's seat >>deal. And John, I guess you know my final takeaway, we talked a bunch about outpost but that is a building block, 80 West local zones starting first in LA for the telco media group, AWS wavelength working with the five G providers. We had Verizon on the program here. Amazon is becoming the everywhere cloud and they really, as Dave said in your opening keynote there, shock and awe, Amazon delivers mere after a year >>maybe this logo should be everything everywhere cause they've got a lot of capabilities that you said the everything cloud, they've got everything in the store do great stuff. Great on the keynote from Verner Vogel's again, more technology. I'm super excited around the momentum around Coobernetti's you know we love that they think cloud native is going to be absolutely legit and continue to be on a tear in 2020 and beyond. I think the five G wavelength is going to change the network constructs because that's going to introduce new levels of kinds of policy. Managing data and compute at the edge will create new opportunities at the networking layer, which for us, you know, we love that. So I think the IOT edge is going to be a super, super valuable. We even had Blackberry on their, their car group talking about the software inside the car. I mean that's a moving mobile device of, of of industrial strength is industrial IOT. So industrial IOT, IOT, edge outpost, hybrid dude, we called this what year? Yeah, we call that 2013. >>And John, it's great to help our audience get a little bit more cloud native on their education and uh, you know, make sure that we're not as naive anymore. >>Still you're not naive. You're certainly cloud native, born in the clouds do, it's us born here. Our seventh year here at Amazon web services. Want to thank Intel for being our headline sponsor. Without Intel support, we would not have the two stages and bringing all the wall to wall coverage. Thanks for supporting our mission. Intel. We really appreciate it. Give them a shout out. We've got Andy Jassy coming on for exclusive at three o'clock day three stay with us for more coverage. Live in Vegas for reinvent 2019 be right back.

>>LA Las Vegas. It's the cube hovering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Hey, welcome back everyone. This is the cubes live covers Las Vegas anus. Re-invent. I'm John furrier with Dave Alante extracting the signal from the noise sponsored by Intel and AWS. They put the stage together, two big stages. Day two, we're here day Jew, I rapid fire a devil's execs coming on. Dave McCann, cube alumni, VP of ADAS migration marketplace and control services known most for the marketplace and a lot of stuff going on. That's exciting in the marketplace. It's where all the ecosystem actions happening. Congratulations on you six. I know you're busy, you've got new stuff, but the marketplace seems to be changing the procurement and the consumption of software and solutions, whether it's SAS or images and technology, your demand on the marketplace. So great to be back, Kimberly. It's another reinvent. This is my sixth. Um, so lots going on. Marketplace has got a lot bigger in the last year. >>We're up to 260,000 customers, so not substantial growth from last year. And we're adding thousands of customers every month. Um, big headline I have to start with is marketplace has been a marketplace for software for the last seven years. And two weeks ago we launched a marketplace for data and it's a new service that we call AWS data exchange. And instead of allowing you to point, click subscribe to software, and if you're a data consumer and a bank and you're an analyst or you're a researcher and a pharma company, you actually buy data from hundreds of companies, you know, you can go into the new console, find the product and market, please go over to this console called data exchange. And you can go buy research data or you can buy healthcare data from change healthcare. You can buy news data from Thomson Reuters, you can buy consumer data from Experian. >>And we've launched 1400 products from 19 data providers and we've made it available globally. So it's a whole new class of intellectual property data sources in there as well. There's some open source public sources as well. And we're adding literally dozens of products every day. So really easy API. And the cool thing is that after you subscribe, you copy it right into your S three bucket, moves into your VPC and then you move it into your project and you can actually create a Lambda function with the next version of the data. The next day gets updated and know the data just gets updated. And the use case here is like, if I'm a retail outlet, I could buy or go and get weather data and do some things. Is that kind of the model? Exactly. Right. I mean companies all over the world by $150 billion worth of data, but it's all delivered thousands of different EPA. >>Dave, we got cube data, we put all of our advanced data out there, which might be an opportunity. But seriously, Q three 65 is our new listing on the market place. So we have a Q cloud service, little plug for the cube cube three 65 on the marketplace and we're, we're happy. But I want to ask you because one of the things that's coming up is, um, from your team in the marketplace, the industry is this notion of buying through the marketplace. The trend is increasing private offers is a hot feature that you guys have put in place. And there's some news there. Could you explain how private offers is changing the game in the marketplace? I'd love to show you, if you think about it, a lot of our customers are developers and builders and they're working on something on test and it's a pilot and you use it for a few hours or a week. >>But once a company contracts for software and if you're contracting for a lot of software, procurement, one's best price, legal one's best terms, and there's going to be in negotiation and we call that negotiation of private offer. And so that involves salespeople. And so our top software vendors like a Splunk and new Relic of trend micro, uh, Palo Alto, their sales guys, or negotiate our sales ladies and negotiating with the customer for a couple hundred thousand dollars and there's a price and terms. When are you going to pay? What clauses do you agree? How many of you buying? Where are you going to deploy? All of that's negotiated and no, we have a portal for the sailor. We've had it for a year, we've made some really good changes and the central, they arose the seller to automate that price court rate into your account and then the buyer subscribes, and this is allowing our sailors to do quotations in the hundreds of thousands, the millions and sometimes in the tens of millions on a contract rate through marketplace, you're doing millions of dollars of business with with private offers today we've seen vendors write contracts for over $10 million, Peter over three years SAS contracts. >>So we've had that program available for the last year and we'd be working on a lot of features with the help of people at Splunk and new Relic today, we've made it available for all ISBNs and marketplace. You say all the iterations get to take place in the market place, so it's all those informations. I should just speak, just make sure I get it right before private offers were invite only kind of thing. Now you're making it available to all ASVs. Correct. We've got one. As of today, we've over 1,500 ASVs in the marketplace. You're one of them. And with those 1500 vendors within our go into marketplace, there's a new button and the seller portal and it says create Piper offer and any over ISV can note create a private. So I'm going to put my little seller hat on. I have a SAS application. Look at, I don't have a big Salesforce. >>How can you guys help me? How do I, how do I get more sales? Is there a, there's the money just following my bank account. Oh, are you overstaffed to do marketing? You have to do some discussions. You know, we had a company in the UK called Matilda MAF last year on, on the cube. Medallian Staffan was 17 engineers and new salespeople and now they're like 300 people, two runs of venture and everything's through marketplace. Big booth here. Well, congratulations to those guys. We love them. And to come Mytilene again, they engage rafted with you guys. It is all the sales and go to market through AWS complete everything goes through marketplace. Okay. We've made it available to 1,500 vendors today. Okay. So changing procurement. I love that trend. You kind of modernizing the procurement process with the marketplace. What about um, resellers? What's the update there? >>So the big update there is, you know, for the first six years of marketplace we couldn't handle the resaler. We didn't conceive of the VAR or the consulting partner and we got a lot of feedback that we had to do work. And so we've taken private offers and we've designed consulting partner, private offers and no, we've saved up over a hundred top consulting partner resellers, the likes of an OCT of an Ashi, a Rackspace in Europe computer center and Softcat and they were working with all of the world's top resellers and know if you are a Splunk or trend micro, you can authorize computer center to offer private prices to their customers and you can actually authorize a wholesale price from Splunk directly to computer and get paid for. Well, they could actually set the price. Mark it up. I got to ask you, Dave, what's your vision for marketplace? >>Because you're doing a great job. It seems like you're paddling as fast as you can constantly improving the service. I know you've got a big to do list, you want to make it easy or make it faster, all that good stuff, but what's the vision? Where do you see marketplace evolving? You know, Jeff Bezos says it's only day one. We're seven years old. We've barely scratched the surface. Global software is 450 billion growing 8% data is 150 billion growing at 3% you've got a $600 billion industry. Marketplace has not touched a tiny percentage. We want all of our customers to be able to find, discover, provision, and run all of their software and their data out of marketplace and it's gonna take us another 10 years and you get a lot of teen. How big is the team? We never publish JFK K but just let's say the Andy Jassy continues to invest in the business and as we add engineers and we add business people and development people, you know we work well with our partners. >>We cool market. Yeah, we grew up well, as Andy always says, you know, and you always say this, the customer needs come first. That's kind of a vetting process. Then working backwards documents, we know all about that history. What is the number one customer need that you're hearing, that you're addressing, that you see coming up around the corner, you're constantly working on and new potentially new requests that are coming in that are relevant to your business. There's two or three big customer needs. The number one is governance. So while engineers are going fast, innovating, legal, finance and procurement need to be confident that the contracts are being written well and is the spend under control. And so we're doing a lot of work around tagging or the resources so that it's tagged to the right project. Did you overspend on the project? And then on the contracting inside we launched this thing called enterprise contract and we're continuing to work with customers. >>We just integrated into the leading procurement system called ACP a Reebok and we launched that last week. And so we know have a procurement workflow that says procurement's happy it finances happy legal needs to be happy because the engineers want to go quick, but we can't leave the it finance legal professionals behind because they protect the risks for the kinda, the contracts too are all there. So you're modernizing procurement. We are transforming the supply chain for data and for software, you know big. You know I'm a big fan of what you do and I know you got a lot of hard work, a lot of demand, there's a lot of money to be made there, water customers to make happy and you know we've got great customers that BP or shell or Coca Cola, Coke industries that are using marketplace on a regular basis and we have customers now with over a foes and subscriptions from over 50 vendors and that's a single customer. >>Dave, thank you so much for coming on. I know you're super busy and making the time for wrestling the cube means a lot. You've been with us the entire journey for the Ravens, our seventh reinvent. You've been a great one. I missed one but usually patients man it's just you. You saw it working backwards and it's happening. It's working well and you know we learn from our customers and I'm having a dinner tonight with 40 more and I'm sure they'll hit us with more requirements. I'll check my email for the invite. I'm sure it's in there somewhere. Dave McKenna inside the cube. Good friend of the cube, hardworking, billable in the next generation, the next gen marketplace. Check it out. Of course, the cube three 65 our new offering is up there as of Monday. It's kind of a soft launch, but we're telling you now, I'm John Freud. Dave Volante. Thanks for watching back with more. Thanks and have a short break.