>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Hey welcome back, everyone, it's theCUBE's coverage here live in Las Vegas for re:Invent 2019, this is theCUBE's seventh year covering re:Invent, the event's only been going for eight years, it feels like a decade, so much growth, so much action, I'm John Furrier with my co-host Dave Vellante, here extracting the signal from the noise in the Intel AWS studio of theCUBE, thank you for that sponsorship. Mike Miller is our next guest, he's director of AI devices at AWS, super excited for this segment, because DeepRacer's here, and we got some music, AI is the front and center, great to see you again, thanks for coming on. >> Absolutely, thank you for having me on again, I appreciate it. >> All right, let's just jump right in, the toys. Developers are geeking out over DeepRacer and the toys you guys are putting out there as a fun way to play and learn. >> Absolutely, getting hands-on with these new broadly applicable machine learning technologies. >> Let's jump into DeepRacer, so first of all, give us a quick update on what's happened between last year and this year in the DeepRacer community, there's been a lot of froth, competitiveness, street battles, and then we'll get an update, give us a quick update on the community. >> So we launched DeepRacer last year as a 1/18 scale race car designed to teach reinforcement learning, so this thing drives by itself around the tracks. We've got an online experience where customers can train models, so we launched a DeepRacer league where we plan to visit 22 sites around the world at AWS summits, where developers can come visit us and race a car physically around a track, and we had online contests, so every month we had a new track for developers to be challenged by and race their cars around the track. We've seen tremendous engagement and excitement, a little bit of competition really gets developers' juices going. >> It's been a lot of fun, congratulations, by the way. >> Absolutely, thank you. >> All right, let's get into the new toy, so DeepRacer 2.0, whatever you're calling it, just DeepRacer-- >> DeepRacer Evo. >> Evo, okay. >> New generation, so we've basically provided more opportunities to race for developers, more challenges for them to learn, and more ways for them to win. So we integrated some new sensors on this car, so on top there's a LIDAR, which is a laser range finding device that can detect other cars or obstacles in the rear of the car and to the sides, and in the front of the car we have stereo cameras that we added so that the car can sense depth in front of it, so with those new sensors, developers can now be challenged by integrating depth sensing and object avoidance and head to head racing into their machine learning models. >> So currently it's not an obstacle course, correct, it's a race track, right? >> So we call it a time trial, so it's a single car on the track at a time, how fast can you make a lap, our world record actually is 7.44 seconds, set by a young lady from Tokyo this past year, really exciting. >> And she was holding up the trophy and said this is basically a dream come true. And so, what are they trying to optimize, is it just the speed at the turn, what are they sort of focused on? >> Yeah, it's a little bit of art and a little bit of science, so there's the reinforcement learning model that learns through what's called a reward function, so you give the car rewards for achieving specific objectives, or certain behaviors, and so it's really up to the developer to decide what kind of behaviors do they want to reward the car with, whether it's stay close to the center line, reduce the amount of turns, they can also determine its position on the track and so they can reward it for cutting corners close, speeding up or slowing down, so it's really a little bit of art and science through some experimentation and deciding. >> So we had Intel on yesterday, talking about some of their AI, Naveen Rao, great guy, but they were introducing this concept called GANs, Generative Adversarial Networks, which is kind of like neural network technology, lot of computer science in some of the tech here, this is not kiddie scripting kind of thing, this is like real deal. >> Yeah, so GANs actually formed the basis of the product that we just announced this year called DeepComposer, so DeepComposer is a keyboard and a cloud service designed to work together to teach developers about generative AI, and GANs are the technique that we teach developers. So what's interesting about generative AI is that machine learning moves from a predictions-based technology to something that can actually create new content, so create new music, new stories, new art, but also companies are using generative AI to do more practical things like take a sketch and turn it into a 3D model, or autocorrect colorize black and white photos, Autodesk even has a generative design product, where you can give, an industrial designer can give a product some constraints and it'll generate hundreds of ideas for the design. >> Now this is interesting to me, because I think this takes it to, I call basic machine learning, to really some more advanced practical examples, which is super exciting for people learning AI and machine learning. Can you talk about the composer and how it works, because pretend I'm just a musician, I'm 16 years old, I'm composing music, I got a keyboard, how can I get involved, what would be a path, do I buy a composer device, do I link it to Ableton Live, and these tools that are out there, there's a variety of different techniques, can you take us through the use case? >> Yeah, so really our target customer for this is an aspiring machine learning developer, maybe not necessarily a musician. So any developer, whether they have musical experience or machine learning background, can use the DeepComposer system to learn about the generative AI techniques. So GANs are comprised of these two networks that have to be trained in coordination, and what we do with DeepComposer is we walk users through or walk developers through exactly how to set up that structure, how these two things train, and how is it different from traditional machine learning where you've got a large data set, and you're training a single model to make a prediction. How do these multiple networks actually work against each other, and how do you make sure that they're generating new content that's actually of the right type of quality that you want, and so that's really the essence of the Generative Adversarial Networks and these two networks that work against each other. >> So a young musician who happens to like machine learning. >> So if I give this to my kid, he'll get hooked on machine learning? That's good for the college apps. >> Plug in his Looper and set two systems working together or against each other. >> When we start getting to visualization, that's going to be very interesting when you start getting the data at the fundamental level, now this is early days. Some would say day zero, because this is really early. How do you explain that to developers, and people you're trying to get attention to, because this is certainly exciting stuff, it's fun, playful, but it's got some nerd action in it, it's got some tech, what are some of the conversations you're having with folks when they say "Hey, how do I get involved, why should I get involved," and what's really going to be the impact, what's the result of all this? >> Yeah, well it's fascinating because through Amazon's 20 years of artificial intelligence investments, we've learned a lot, and we've got thousands of engineers working on artificial intelligence and machine learning, and what we want to do is try to take a lot of that knowledge and the experiences that those folks have learned through these years, and figure out how we can bring them to developers of all skill levels, so developers who don't know machine learning, through developers who might be data scientists and have some experience, we want to build tools that are engaging and tactile and actually tangible for them to learn and see the results of what machine learning can do, so in the DeepComposer case it's how do these generative networks actually create net new content, in this case music. For DeepRacer, how does reinforcement learning actually translate from a simulated environment to the real world, and how might that be applicable for, let's say, robotics applications? So it's really about reducing the learning curve and making it easy for developers to get started. >> But there is a bridge to real world applications in all this, it's a machine learning linchpin. >> Absolutely, and you can just look at all of the innovations that are being done from Amazon and from our customers, whether they're based on improving product recommendations, forecasting, streamlining supply chains, generating training data, all of these things are really practical applications. >> So what's happening at the device, and what's happening in the cloud, can you help us understand that? >> Sure, so in DeepComposer, the device is really just a way to input a signal, and in this case it's a MIDI signal, so MIDI is a digital audio format that allows machines to kind of understand music. So the keyboard allows you to input MIDI into the generative network, and then in the cloud, we've got the generative network takes that input, processes it, and then generates four-part accompaniments for the input that you provide, so say you play a little melody on the keyboard, we're going to generate a drum track, a guitar track, a keyboard track, maybe a synthesizer track, and let you play those back to hear how your input inspired the generation of this music. >> So GANs is a big deal with this. >> Absolutely, it forms the basis of the first technique that we're teaching using DeepComposer. >> All right, so I got to ask you the question that's on everyone's mind, including mine, what are some of the wackiest and/or coolest things you've seen this year with DeepComposer and DeepRacer because I can imagine developers' creativity straying off the reservation a little bit, any cool and wacky things you've seen? >> Well we've got some great stories of competitors in the DeepRacer league, so we've got father-son teams that come in and race at the New York summit, a 10 year old learning how to code with his dad. We had one competitor in the US was at our Santa Clara summit, tried again at our Atlanta summit, and then at the Chicago summit finally won a position to come back to re:Invent and race. Last year, we did the race here at re:Invent, and the winning time, the lap time, a single lap was 51 seconds, the current world record is 7.44 seconds and it's been just insane how these developers have been able to really optimize and generate models that drive this thing at incredible speeds around the track. >> I'm sure you've seen the movie Ford v Ferrari yet. You got to see that movie, because this DeepRacer, you're going to have to need a stadium soon, with eSports booming, this has got its own legs for its own business. >> Well we've got six tracks set up down at the MGM Grand Arena, so we've already got the arena set up, and that's where we're doing all the knock-out rounds and competitors. >> And you mentioned father-son, you remember when we were kids, Cub Scouts, I think it was, or Boy Scouts, whatever it was, you had the pinewood derby, right, you'd make a car and file down the nails that you use for the axles and, taking it to a whole new level here. >> It's a modern-day version. >> All right, Mike, thanks for coming on, appreciate it, let's keep in touch. If you can get us some of that B-roll for any video, I'd love to get some B-roll of some DeepRacer photos, send 'em our way, super excited, love what you're doing, I think this is a great way to make it fun, instructive, and certainly very relevant. >> Absolutely, that's what we're after. Thank you for having me. >> All right, theCUBE's coverage here, here in Las Vegas for our seventh, Amazon's eighth re:Invent, we're documenting history as the ecosystem evolves, as the industry wave is coming, IoT edge, lot of cool things happening, we're bringing it to you, we're back with more coverage after this short break. (techno music)

>> Announcer: From Miami Beach, Florida it's theCUBE, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Welcome back everyone to theCUBE coverage here at the Acronis Global Cyber Summit 2019 in Miami Beach at the Fontainebleau Hotel. Not a bad venue for an event. It's their first inaugural event around cyber protection. Our next guest is a great guest. He's going to go into great detail. Very fun job. Stressful job. Graeme Hackland, CIO of ROKiT Williams Racing Formula One team. Thanks for joining me. >> Thanks Joe. >> Great job you have. I mean, it's high pressure, high stakes, data's involved. You can nerd out on all the tech and it's a part of the business these days. Take a minute to explain the Williams Racing Team history and what are you guys up to these days. >> So Williams, this is Sir Frank Williams' 41st year with this team. 50 years in total he's been in Formula One. Won 16 world championships. Not recently, we want to do that again for him and that's the mission, right? Get up every day wanting to get back to the front of the grid and help Williams to win. I joined them in 2014. I've been 23 years in total in Formula One. I love the industry, the fast pace, everything you describe. There's a bit of stress obviously but I just love the industry and I joined Williams in 2014 to help with the digital transformation and it's been brilliant and now we're not using the transformation word anymore. We're on a digital journey. We've already put a lot of that infrastructure in place, moved to the cloud, and it's just been, it's been brilliant and we've had some success on the track. More recently it's been tough but we'll get back there. >> You know, I just had a conversation with Dan Havens who's the Chief Growth Officer, he's done all of the sports deals. We were talking about, you know, baseball and the other football, European football, and also Formula One. The competitive advantage edge is there in the data. AI is here, machine learning feeds AI, so now do you set up the infrastructure, you get operationalized properly. This is a big job. It's not just loading software. You got to really think about the wholistic system at work. >> That's the great thing, right? We've go to do the infrastructure right. So you've got to get the basics right. But then if we can do a better job with AI, with machine learning, with the analytic tools that are out there than the other teams are doing. We can beat them. We don't have the same funding levels that they do but we got really smart people, and people is our biggest asset. And then the second biggest is data and making sure that the right engineer has the right data at the right time so that they can do their job, so that we can set the fastest pit stop time or that we can challenge the cars in front of us. It is really important, so we put a lot of time and effort into data analytics, but especially video. Video has become huge for us and obviously then, the data size grows massively. But data and being able to analyze your competitors, analyze your own car, your two drivers against each other. There's a huge amount of data that we are dealing with. >> Without giving any secrets away Graeme, talk about some of the data dynamics that you have going on. What is some of the workflows? What are some of the things you're optimize... You said video. Where are you guys looking at? What are some of the key, cool things that you're seeing as an edge opportunity for you? >> So, Formula One team has this life cycle of a Formula One car where you start in aerodynamics, either in a wind tunnel with a physical model or you do virtual wind tunnel with computational fluid dynamics. There's CFD, so that computation power is really important. Then you go into design, CAD design, that really turns it into something that you can make so then we're into manufacturing. Then we got a race engineer, and all the tools that they use to get the optimum out of the car that they're given on a race weekend. And then you feed that back in so that every race were adding performance to the car, and all through the season. We'll add one and a half to two seconds per lap of performance onto that car every season. And so that's a really important loop that you need to be constantly doing. And if you don't, you know, we've had some issues in this year, if you don't get that completely right, you will lose time to your competitors. >> Give me an example where it didn't work out, where you've gone back to the drawing board. >> So, I think there's been, and it's been well publicized, Clay Williams has talked about it. There's been a bit of a gap between the results we were getting in the wind tunnel and the reality that was happening on the track. And so we've had to bring that back and make sure that there was a correlation between the tunnel and the track. And our engineering group will be working really hard on that, so that kind of thing can happen. >> Talk about the engineering backgrounds that are going on behind the scenes. A lot of people look at Formula One's, only the hardcore nerd that are nerding out and geeking out on the sport know that the depth but, what's going on in the engineering front because there's a lot of investment you guys are making on engineering. >> Yeah, and so, Formula One fans love the data. I think they really love to see the data and work with it and, fortunately, the people who run Formula One are opening more of that data to the fans. If you left it to the teams, we wouldn't share it with the fans because then our competitors see it and we see it as a competitor's advantage. But if something's shared for everyone then that's fair. So, I think the fans love to see the data and see what we're doing. What we're trying to look at now is automation. Humans making decisions has been okay up until probably the last couple of years where some errors have been made in strategy, in real-time where you've got a few seconds to make a decision. Are you going to pit? Virtual safety car has just been called. You've got three seconds to make a decision. Sometimes the humans are making the wrong decision. So we see automation, AI, as really having a role in that real-time decision making. But we think AI can help us in our factory. The things that we're making, something happens at the track, and now we have to change that design. We think introducing automation and AI into that process will really help us as well. >> Yeah, sports market, sports teams, and sports franchises, to me, optimize digital transformation or digital journey because the fans want it. >> Graeme: Yeah. >> There's competitive advantage in running the team. There's the player's decision making whether it's baseball or a driver. >> Graeme: Yup. >> And then there's the fans. So, I got to ask ya on, what are you guys thinking about the fan experience because now you got some data opening up, you got visualization, potentially apps that show you that cars in 3D space and some virtual reality potential. >> Yup. >> The old experience was, ooh, there's a car, goes by again, hey we're (giggles) comes by again. So, bringing, extending the digital fan-based experience, what do you guys, what's your view there? >> Oh, there's a huge amount of work happening in Formula One and it's great to see the people who are running Formula One talking about a digital transformation, not just the teams, right. And it was all about the fan experience. We want the fan to feel like they're a part of it. So Williams did a couple of experiments with virtual reality, so that you could either be one of the pit crews, so you could be the person holding the gun, feel the car coming in, and changing the tire. >> That's awesome. >> Or you could have the driver's view. So the cameras that are on the car are above the driver's head so you don't get an accurate view. So we brought that down into the helmet and now you're getting the view of what it's like to be the driver. >> Wow. >> So, there's been a lot of focus on that fan experience and making sure that you're not at a disadvantage sitting in this, you know, at the track, compared to someone who's at home with two televisions or multiple devices that they're tracking the data on. And the GPS data of where the cars are and hearing some of the commentary of why they're making the decisions they are and when the driver's challenge their engineers, I love that bit. So the engineers got all that data, tells the driver we're going to do this strategy and the driver challenges it because they're in the car feeling how the car feels. >> I think you guys have a great opportunity as an industry because, you look at Esports and the gaming culture, the confluence of that experience based product coming to Formula One. >> Graeme: Yup. >> It's just the perfect fit. >> Well, it's gone, the Esports Formula One has gone huge. We run a team as well. Most of the Formula One teams now have an Esports team. And actually, the people who are driving in the Esports teams, their skills are transferrable. I remember one of the competitions a couple of years ago was to win a drive in the simulator. You became a development driver for one of the Formula One teams. And that shows that those skills are transferrable, so it's great. >> Yeah, that's beautiful stuff. All right, I want to get back to the Acronis cyber.. >> Yup. >> Global Cyber Summit 2019. You're here talking to folks, also sharing knowledge, you guys were hit with ransomware. >> Graeme: Yup. >> Not once, but twice. >> Graeme: Yup. >> I think you had just joined, I think at that time before.. >> It was during 2014 when I first joined and we would, I know, we had put as much investment as we could into our cyber security and to our protection. But we had gaps and I think, so the first ransomware that we got hit by was inside our network and it encrypted 50,000 files before we discovered it. Now we were lucky. We were able to recover all the data from back-up, but we knew that, because it had happened in the middle of the day, someone had looked at some websites during their lunch break and within a couple of hours we had discovered it, contained it, corrected it, restored the data. But the second time we got hit, it was an individual on their computer off network, and we lost data. And that's the thing I hate the most. That data is so precious to us. Losing it was really upsetting. And so we went out into the market, how can we make sure that our data is being backed up? But more than that, how can we make sure that backed up data is protected? And there's a number of reasons we want to protect it. We want to protect it from things like ransomware, but also, the thing that people often don't thing about with their data is, how do we make sure that it's not tampered with at any point? So, when we're at the track, and the car's running around the track, we're pushing data locally, inside the network. We're pushing it to the cloud to do computation and we're sending it back to the UK so that engineers at base can work with it. >> Yeah. >> What it someone was in those stream of data tampering with it? >> Yeah. >> And we then had fake data? And as we go to more machine learning and automation, if those decisions are being made on bad data, that's going to be a real problem. So, we wanted to make sure that our data couldn't be tampered with, so we can adopt new technology. So that was really important. But, Williams also have an advanced engineering company, so beyond Formula One, we apply that knowledge and know how, to all sorts of other industries. From healthcare to retail to automotive. We've been helping Unilever with some really interesting projects to make ice cream better and more efficiently and to help with soap powder. We got to make sure that that customer data is never tampered with. If we're going to put technology into road cars, that's a very different challenge to Formula One. >> John: Yeah. >> We got to make sure that, that whole, the IP chain, how we develop that technology can be proven and isn't tampered with. >> It's interesting, supply chain concepts data protection merging together. Data protection used to be thought after.. Oh, we've got a design. Well let's brush up, we'll get back it, bolt it on. Not anymore. >> Now having to build it into the solutions up front. As we're preparing technology for customers, we're having to make sure that we're thinking about the data challenge. So if it's in a car, so we did battery technology, we won the supply for the first ever gas to electric model, right. As that car is driving around, there's going to be data that's important around the health of the battery. >> John: Yeah. >> And information that is going to be needed by the driver, but also for later for when they're doing the servicing on the car. We got to make sure that that data is protected properly. >> You guys are pushing the envelope on instrumentation, sensors, data, real-time telemetry? >> To be honest, Formula One has always been like that. We put our first data logger in 1979 on a Formula One car. Honestly, it's been an IOT device since then. (laughs) It's not a new thing for F Ones. I think we are really experienced. Our electronics group are real experienced in how to protect that data as it comes off the car and we've applied that knowledge to road cars as well. >> Well you, what's great about you guys and the whole industry is that, that innovation for the sport is now translating as a benefit for society. >> Exactly. >> And I think that is really kind of a, I think, an example of where innovation can come from. Places you least expect it. The people doing hard work pays off. >> It always worried me that Formula One, we spend all the money we spend, right, hundred million pounds, three hundred million pounds per year. And at the end of the year, the product that we created gets retired and we create a whole new product. It always worried me that that technology wasn't reused. Williams are reusing it. You know, we take the carbon fiber that we use to protect a driver in a Formula One car. We've now applied that to babies in hospitals when they get moved around. We built a carbon fiber unit that moves them around. Aerodynamics design, we've applied to fridges to make them more efficient. If you've got an open fridge, the cold air doesn't come out into the aisle of the supermarket. We push it back into the fridges. I love that. Reuse, taking loose end leaf batteries and putting them into a unit that you bought on the side of a house and it helps to power the house over night. >> You know, it's interesting Graeme, you mentioned digital transformation versus digital journey, you guys are operationalize it as it's used. >> Graeme: Exactly. >> Difference, there's nuance but transformation. You have yet transformed. >> Graeme: Yup. >> You guys up transformed so you're on a journey. I got to ask you, what is some learnings in your operationalize digital? I mean, obviously you got your sport, but now it's translating out to other areas. What's the big learnings that you take away from, as a professional and as an individual in the industry, from all this? >> I think, initially, we were quite conservative and we only went with big players that we were convinced were going to be around in three to five years. I think, there's a lot more established cloud providers now but early on we only went with the big guys because we wanted to make sure we could get our data out. If they disappeared, we weren't going to lose our data. I think what the partnership with Acronis and other partnerships we've done has helped us to be more aggressive in terms of our approach towards CAD vendors. We can now take risks with a smaller player. We've got a really niche product but it's something that could give us a competitive advantage for half a season, three, four races sometimes. We'd go for it. Whereas, I think we were a bit conservative at first. I think all CIOs have to think about what's their appetite for risk. We did a really good process of mapping that out, discussing it all the way to board level. What exactly are we prepared to risk? There's some things, you know, car data, we're just not prepared to risk that. >> Yeah. >> But there are some things that we can afford to take risks with. And I've talked to CIOs at finance institutes, they're starting to take risks now. There's core data that they won't be able to, either by regulation or just doesn't make sense. But there's a lot you can commoditize and put out into the cloud. >> And if you have a cyber protection foundation, you can take those risks. >> Graeme: Exactly. >> You don't want to be looking over your shoulder worrying. >> Because you own the data. And sometimes when you go with a cloud provider, it feels almost like they own the data. But when you've got a partnership like the one we have with Acronis, we know that we own the data. We're backing that data away from the cloud vendor so we can always get it back. >> Graeme, thanks so much for the insight. Love this conversation. I think it's really innovative, cutting edge, and great fun to talk about. Thanks for coming on theCUBE, appreciate it. >> Thank you very much, cheers. >> CUBE coverage here at Miami Beach at the Fontainebleau Hotel for Acronis Global Cyber Security 2019 Summit, I'm John Ferrier, stay with us for more CUBE day two coverage after this short break. (fun music)

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Oracle Park on the shores of McCovey Cove. We're excited to be here. It's a pretty interesting event. Sports Tech Tokyo World Demo Day. It's kind of like an accelerator, but not really. It's kind of like YCombinator, but not really. It's a little bit different, but it's a community of tech start-ups focusing on sports with a real angle on getting beyond sports. We're excited to have our next guest who's an investor and also a mentor, really part of the program to learn more about it, and she is Gayatri Sarkar, the managing partner from HYPE Capital. Welcome. >> Thank you. Thank you for inviting me here. >> Pretty nice, huh? >> Oh, I just love the view. >> So you said before we turned on the cameras... Well, first off, HYPE Capital, what do you guys invest in? What's kind of your focus? >> So HYPE Capital is one of the biggest ecosystem in sports, which is HYPE Sports Innovation. We have 13 accelerators all around the world. We are just launching the world's first Esports accelerator with FC Koeln and SK gaming, one of the biggest gaming company. So we are part of the ecosystem for a pretty long time. And now we have HYPE Capital or VC Fund investing in Europe, Israel, and now in US. >> So you mentioned that being a mentor, as part of this organization, as something special. Think you're the first person we've had on who's been a mentor. What does that mean? What does it mean for you, but also what does it mean for all the portfolio companies? >> Sure. I'm a mentor at multiple accelerators, but being a part of Sports Tech Tokyo, I saw the very inclusive community that is created by them. And the opportunity to look at various portfolio companies and also including our portfolio companies as part of it. One of our portfolio company where we are the lead investors, Fund with Balls, they are part of this. So-- >> What's it called? Fun with Balls? >> Fun with Balls, very interesting name. >> Good name. >> Yeah. (laughing) They're from Germany and they came all the way from Germany to here. So, yeah, I'm very excited because as I said, it's an inclusive community and sports is big. So we are looking at opportunities where deep techs, where it can be translated into various other verticals, but sports can also be one of the use cases. And that's our focus as investors. >> Right. You said your focus is really on AI, machine learning. You have a big data background, a tech background. So when you look at the application of AI in sports, what are some of the things that you get excited about? >> Yeah, so for me, when I'm looking at investments, definitely the diversification of sports portfolio, how can I build my portfolio from Esports gaming, behavioral science in sports to AI, ML, AR opportunities in material science, and various other cases? Coming back to your question, it's like how can I look into the market and see the opportunities that, okay, can I invest in this sector? As I said, what's the next big trend? And that's where I want to invest. Obviously, founder market fit, product market fit, promise market fit because there's the fan engagement experience that you get in sports, not in any other market. The network effect is huge and I think that's what we VCs are very excited in sports. And I think this is, right now, the best time to invest in sports. >> So promise market fit, I've never heard that before. What does that mean when you say promise market fit? >> Interesting question. So promise market fit was coined by Union Square Venture VC Fund. And they think that where there's the network effect, or your engagement with your consumers, with your clients, with your partners, can create a very loyal fan base and I think that's very important. You may see that in other technology sector, but it is completely unparallel when it comes to sports. So I request all the technologies that are actually trying to build their use cases. They should focus on sports because the fan engagement, the loyal experience, they opportunities, you'll not get anywhere else. >> And I think this is the market that I and other investors are looking forward. If deep tech investors and deep tech technologies are coming into this market, we see the sports ecosystem, not to be a trillion-dollar, but a multi-trillion dollar market. >> Right. But it's such a unique experience, though, right? I mean, some people will joke their fans don't necessarily root for the team, they root for the jersey, right? The players come and go. We're here at Oracle Park, which was AT&T Park, which was SBC Park, which was I can't even remember. Pac Bell, I think, as well. So is it reasonable for a regular company that doesn't have this innate, kind of, a connection to a fan base that a lot of sports organizations do that's historical and family-based, and has such deep roots that can survive, maybe, down years, can survive a crappy product, can survive, kind of, the dark days and generally they'll be there when things turn back around. Is that reasonable for a regular company to try to get that relationship with a customer? >> So you asked me one of the most important question in the investor's relationship or investor's life, which is the cyclicality of the industry. And I feel like sports is one industry that has survived the cyclicality of that industry. Because, as you said, a crappy product will not survive. You have to focus on customer service. You have to focus that, okay, even if you have the best product in the world. How can I make my product sticky? I think these are the qualities that we're looking into when we are investing in entrepreneurs. But the idea is that if we are targeting start-ups and opportunities, our focus is that, okay, you may have the world's best product, but the founders should have the ability to understand the market. Okay, there are opportunities. If you look at Facebook, if you look at various other companies, they started with a product, which maybe, okay, friends saw a dating site and they pivoted. So you need to understand the economy. You need to understand the market. And I think that's what we are looking into the entrepreneurs. And as to answering your question, the family offices, they're actually part of this world start-up ecosystems. They're seeing if there's an opportunity, because they're big, they're giant, and they're working with legacy techs like Microsoft, Amazon. It's very difficult for the legacy techs to be agile and move fast. So it's very important for them if they can place themselves at a 45 degree angle with the start-up ecosystem and they can move faster. So that's the opportunity for them in the sports start-up ecosystem. >> All right. Well, Gayatri, thanks for taking a few minutes and hopefully you can find some new investments here-- >> No, thank you so much. >> over the course of the day. >> Thank you so much for your time. >> Absolutely, she's Gayatri, I'm Jeff. You're watching theCUBE. We are at Oracle Park on the shores of historic McCovey Cove. I got to get together with big John and practice this line. (laughing) Thanks for watching. We'll see you next time. (upbeat music) >> Camera Crew: Clear. >> Jeff: John Miller. >> Gayatri: Oh, yeah.