(gentle music) >> Hello everyone. Welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great remote guest coming in. Joseph Nelson, co-founder and CEO of RoboFlow hot startup in AI, computer vision. Really interesting topic in this wave of AI next gen hitting. Joseph, thanks for coming on this CUBE conversation. >> Thanks for having me. >> Yeah, I love the startup tsunami that's happening here in this wave. RoboFlow, you're in the middle of it. Exciting opportunities, you guys are in the cutting edge. I think computer vision's been talked about more as just as much as the large language models and these foundational models are merging. You're in the middle of it. What's it like right now as a startup and growing in this new wave hitting? >> It's kind of funny, it's, you know, I kind of describe it like sometimes you're in a garden of gnomes. It's like we feel like we've got this giant headstart with hundreds of thousands of people building with computer vision, training their own models, but that's a fraction of what it's going to be in six months, 12 months, 24 months. So, as you described it, a wave is a good way to think about it. And the wave is still building before it gets to its full size. So it's a ton of fun. >> Yeah, I think it's one of the most exciting areas in computer science. I wish I was in my twenties again, because I would be all over this. It's the intersection, there's so many disciplines, right? It's not just tech computer science, it's computer science, it's systems, it's software, it's data. There's so much aperture of things going on around your world. So, I mean, you got to be batting all the students away kind of trying to get hired in there, probably. I can only imagine you're hiring regiment. I'll ask that later, but first talk about what the company is that you're doing. How it's positioned, what's the market you're going after, and what's the origination story? How did you guys get here? How did you just say, hey, want to do this? What was the origination story? What do you do and how did you start the company? >> Yeah, yeah. I'll give you the what we do today and then I'll shift into the origin. RoboFlow builds tools for making the world programmable. Like anything that you see should be read write access if you think about it with a programmer's mind or legible. And computer vision is a technology that enables software to be added to these real world objects that we see. And so any sort of interface, any sort of object, any sort of scene, we can interact with it, we can make it more efficient, we can make it more entertaining by adding the ability for the tools that we use and the software that we write to understand those objects. And at RoboFlow, we've empowered a little over a hundred thousand developers, including those in half the Fortune 100 so far in that mission. Whether that's Walmart understanding the retail in their stores, Cardinal Health understanding the ways that they're helping their patients, or even electric vehicle manufacturers ensuring that they're making the right stuff at the right time. As you mentioned, it's early. Like I think maybe computer vision has touched one, maybe 2% of the whole economy and it'll be like everything in a very short period of time. And so we're focused on enabling that transformation. I think it's it, as far as I think about it, I've been fortunate to start companies before, start, sell these sorts of things. This is the last company I ever wanted to start and I think it will be, should we do it right, the world's largest in riding the wave of bringing together the disparate pieces of that technology. >> What was the motivating point of the formation? Was it, you know, you guys were hanging around? Was there some catalyst? What was the moment where it all kind of came together for you? >> You know what's funny is my co-founder, Brad and I, we were making computer vision apps for making board games more fun to play. So in 2017, Apple released AR kit, augmented reality kit for building augmented reality applications. And Brad and I are both sort of like hacker persona types. We feel like we don't really understand the technology until we build something with it and so we decided that we should make an app that if you point your phone at a Sudoku puzzle, it understands the state of the board and then it kind of magically fills in that experience with all the digits in real time, which totally ruins the game of Sudoku to be clear. But it also just creates this like aha moment of like, oh wow, like the ability for our pocket devices to understand and see the world as good or better than we can is possible. And so, you know, we actually did that as I mentioned in 2017, and the app went viral. It was, you know, top of some subreddits, top of Injure, Reddit, the hacker community as well as Product Hunt really liked it. So it actually won Product Hunt AR app of the year, which was the same year that the Tesla model three won the product of the year. So we joked that we share an award with Elon our shared (indistinct) But frankly, so that was 2017. RoboFlow wasn't incorporated as a business until 2019. And so, you know, when we made Magic Sudoku, I was running a different company at the time, Brad was running a different company at the time, and we kind of just put it out there and were excited by how many people liked it. And we assumed that other curious developers would see this inevitable future of, oh wow, you know. This is much more than just a pedestrian point your phone at a board game. This is everything can be seen and understood and rewritten in a different way. Things like, you know, maybe your fridge. Knowing what ingredients you have and suggesting recipes or auto ordering for you, or we were talking about some retail use cases of automated checkout. Like anything can be seen and observed and we presume that that would kick off a Cambrian explosion of applications. It didn't. So you fast forward to 2019, we said, well we might as well be the guys to start to tackle this sort of problem. And because of our success with board games before, we returned to making more board game solving applications. So we made one that solves Boggle, you know, the four by four word game, we made one that solves chess, you point your phone at a chess board and it understands the state of the board and then can make move recommendations. And each additional board game that we added, we realized that the tooling was really immature. The process of collecting images, knowing which images are actually going to be useful for improving model performance, training those models, deploying those models. And if we really wanted to make the world programmable, developers waiting for us to make an app for their thing of interest is a lot less efficient, less impactful than taking our tool chain and releasing that externally. And so, that's what RoboFlow became. RoboFlow became the internal tools that we used to make these game changing applications readily available. And as you know, when you give developers new tools, they create new billion dollar industries, let alone all sorts of fun hobbyist projects along the way. >> I love that story. Curious, inventive, little radical. Let's break the rules, see how we can push the envelope on the board games. That's how companies get started. It's a great story. I got to ask you, okay, what happens next? Now, okay, you realize this new tooling, but this is like how companies get built. Like they solve their own problem that they had 'cause they realized there's one, but then there has to be a market for it. So you actually guys knew that this was coming around the corner. So okay, you got your hacker mentality, you did that thing, you got the award and now you're like, okay, wow. Were you guys conscious of the wave coming? Was it one of those things where you said, look, if we do this, we solve our own problem, this will be big for everybody. Did you have that moment? Was that in 2019 or was that more of like, it kind of was obvious to you guys? >> Absolutely. I mean Brad puts this pretty effectively where he describes how we lived through the initial internet revolution, but we were kind of too young to really recognize and comprehend what was happening at the time. And then mobile happened and we were working on different companies that were not in the mobile space. And computer vision feels like the wave that we've caught. Like, this is a technology and capability that rewrites how we interact with the world, how everyone will interact with the world. And so we feel we've been kind of lucky this time, right place, right time of every enterprise will have the ability to improve their operations with computer vision. And so we've been very cognizant of the fact that computer vision is one of those groundbreaking technologies that every company will have as a part of their products and services and offerings, and we can provide the tooling to accelerate that future. >> Yeah, and the developer angle, by the way, I love that because I think, you know, as we've been saying in theCUBE all the time, developer's the new defacto standard bodies because what they adopt is pure, you know, meritocracy. And they pick the best. If it's sell service and it's good and it's got open source community around it, its all in. And they'll vote. They'll vote with their code and that is clear. Now I got to ask you, as you look at the market, we were just having this conversation on theCUBE in Barcelona at recent Mobile World Congress, now called MWC, around 5G versus wifi. And the debate was specifically computer vision, like facial recognition. We were talking about how the Cleveland Browns were using facial recognition for people coming into the stadium they were using it for ships in international ports. So the question was 5G versus wifi. My question is what infrastructure or what are the areas that need to be in place to make computer vision work? If you have developers building apps, apps got to run on stuff. So how do you sort that out in your mind? What's your reaction to that? >> A lot of the times when we see applications that need to run in real time and on video, they'll actually run at the edge without internet. And so a lot of our users will actually take their models and run it in a fully offline environment. Now to act on that information, you'll often need to have internet signal at some point 'cause you'll need to know how many people were in the stadium or what shipping crates are in my port at this point in time. You'll need to relay that information somewhere else, which will require connectivity. But actually using the model and creating the insights at the edge does not require internet. I mean we have users that deploy models on underwater submarines just as much as in outer space actually. And those are not very friendly environments to internet, let alone 5g. And so what you do is you use an edge device, like an Nvidia Jetson is common, mobile devices are common. Intel has some strong edge devices, the Movidius family of chips for example. And you use that compute that runs completely offline in real time to process those signals. Now again, what you do with those signals may require connectivity and that becomes a question of the problem you're solving of how soon you need to relay that information to another place. >> So, that's an architectural issue on the infrastructure. If you're a tactical edge war fighter for instance, you might want to have highly available and maybe high availability. I mean, these are words that mean something. You got storage, but it's not at the edge in real time. But you can trickle it back and pull it down. That's management. So that's more of a business by business decision or environment, right? >> That's right, that's right. Yeah. So I mean we can talk through some specifics. So for example, the RoboFlow actually powers the broadcaster that does the tennis ball tracking at Wimbledon. That runs completely at the edge in real time in, you know, technically to track the tennis ball and point the camera, you actually don't need internet. Now they do have internet of course to do the broadcasting and relay the signal and feeds and these sorts of things. And so that's a case where you have both edge deployment of running the model and high availability act on that model. We have other instances where customers will run their models on drones and the drone will go and do a flight and it'll say, you know, this many residential homes are in this given area, or this many cargo containers are in this given shipping yard. Or maybe we saw these environmental considerations of soil erosion along this riverbank. The model in that case can run on the drone during flight without internet, but then you only need internet once the drone lands and you're going to act on that information because for example, if you're doing like a study of soil erosion, you don't need to be real time. You just need to be able to process and make use of that information once the drone finishes its flight. >> Well I can imagine a zillion use cases. I heard of a use case interview at a company that does computer vision to help people see if anyone's jumping the fence on their company. Like, they know what a body looks like climbing a fence and they can spot it. Pretty easy use case compared to probably some of the other things, but this is the horizontal use cases, its so many use cases. So how do you guys talk to the marketplace when you say, hey, we have generative AI for commuter vision. You might know language models that's completely different animal because vision's like the world, right? So you got a lot more to do. What's the difference? How do you explain that to customers? What can I build and what's their reaction? >> Because we're such a developer centric company, developers are usually creative and show you the ways that they want to take advantage of new technologies. I mean, we've had people use things for identifying conveyor belt debris, doing gas leak detection, measuring the size of fish, airplane maintenance. We even had someone that like a hobby use case where they did like a specific sushi identifier. I dunno if you know this, but there's a specific type of whitefish that if you grew up in the western hemisphere and you eat it in the eastern hemisphere, you get very sick. And so there was someone that made an app that tells you if you happen to have that fish in the sushi that you're eating. But security camera analysis, transportation flows, plant disease detection, really, you know, smarter cities. We have people that are doing curb management identifying, and a lot of these use cases, the fantastic thing about building tools for developers is they're a creative bunch and they have these ideas that if you and I sat down for 15 minutes and said, let's guess every way computer vision can be used, we would need weeks to list all the example use cases. >> We'd miss everything. >> And we'd miss. And so having the community show us the ways that they're using computer vision is impactful. Now that said, there are of course commercial industries that have discovered the value and been able to be out of the gate. And that's where we have the Fortune 100 customers, like we do. Like the retail customers in the Walmart sector, healthcare providers like Medtronic, or vehicle manufacturers like Rivian who all have very difficult either supply chain, quality assurance, in stock, out of stock, anti-theft protection considerations that require successfully making sense of the real world. >> Let me ask you a question. This is maybe a little bit in the weeds, but it's more developer focused. What are some of the developer profiles that you're seeing right now in terms of low-hanging fruit applications? And can you talk about the academic impact? Because I imagine if I was in school right now, I'd be all over it. Are you seeing Master's thesis' being worked on with some of your stuff? Is the uptake in both areas of younger pre-graduates? And then inside the workforce, What are some of the devs like? Can you share just either what their makeup is, what they work on, give a little insight into the devs you're working with. >> Leading developers that want to be on state-of-the-art technology build with RoboFlow because they know they can use the best in class open source. They know that they can get the most out of their data. They know that they can deploy extremely quickly. That's true among students as you mentioned, just as much as as industries. So we welcome students and I mean, we have research grants that will regularly support for people to publish. I mean we actually have a channel inside our internal slack where every day, more student publications that cite building with RoboFlow pop up. And so, that helps inspire some of the use cases. Now what's interesting is that the use case is relatively, you know, useful or applicable for the business or the student. In other words, if a student does a thesis on how to do, we'll say like shingle damage detection from satellite imagery and they're just doing that as a master's thesis, in fact most insurance businesses would be interested in that sort of application. So, that's kind of how we see uptick and adoption both among researchers who want to be on the cutting edge and publish, both with RoboFlow and making use of open source tools in tandem with the tool that we provide, just as much as industry. And you know, I'm a big believer in the philosophy that kind of like what the hackers are doing nights and weekends, the Fortune 500 are doing in a pretty short order period of time and we're experiencing that transition. Computer vision used to be, you know, kind of like a PhD, multi-year investment endeavor. And now with some of the tooling that we're working on in open source technologies and the compute that's available, these science fiction ideas are possible in an afternoon. And so you have this idea of maybe doing asset management or the aerial observation of your shingles or things like this. You have a few hundred images and you can de-risk whether that's possible for your business today. So there's pretty broad-based adoption among both researchers that want to be on the state of the art, as much as companies that want to reduce the time to value. >> You know, Joseph, you guys and your partner have got a great front row seat, ground floor, presented creation wave here. I'm seeing a pattern emerging from all my conversations on theCUBE with founders that are successful, like yourselves, that there's two kind of real things going on. You got the enterprises grabbing the products and retrofitting into their legacy and rebuilding their business. And then you have startups coming out of the woodwork. Young, seeing greenfield or pick a specific niche or focus and making that the signature lever to move the market. >> That's right. >> So can you share your thoughts on the startup scene, other founders out there and talk about that? And then I have a couple questions for like the enterprises, the old school, the existing legacy. Little slower, but the startups are moving fast. What are some of the things you're seeing as startups are emerging in this field? >> I think you make a great point that independent of RoboFlow, very successful, especially developer focused businesses, kind of have three customer types. You have the startups and maybe like series A, series B startups that you're building a product as fast as you can to keep up with them, and they're really moving just as fast as as you are and pulling the product out at you for things that they need. The second segment that you have might be, call it SMB but not enterprise, who are able to purchase and aren't, you know, as fast of moving, but are stable and getting value and able to get to production. And then the third type is enterprise, and that's where you have typically larger contract value sizes, slower moving in terms of adoption and feedback for your product. And I think what you see is that successful companies balance having those three customer personas because you have the small startups, small fast moving upstarts that are discerning buyers who know the market and elect to build on tooling that is best in class. And so you basically kind of pass the smell test of companies who are quite discerning in their purchases, plus are moving so quick they're pulling their product out of you. Concurrently, you have a product that's enterprise ready to service the scalability, availability, and trust of enterprise buyers. And that's ultimately where a lot of companies will see tremendous commercial success. I mean I remember seeing the Twilio IPO, Uber being like a full 20% of their revenue, right? And so there's this very common pattern where you have the ability to find some of those upstarts that you make bets on, like the next Ubers of the world, the smaller companies that continue to get developed with the product and then the enterprise whom allows you to really fund the commercial success of the business, and validate the size of the opportunity in market that's being creative. >> It's interesting, there's so many things happening there. It's like, in a way it's a new category, but it's not a new category. It becomes a new category because of the capabilities, right? So, it's really interesting, 'cause that's what you're talking about is a category, creating. >> I think developer tools. So people often talk about B to B and B to C businesses. I think developer tools are in some ways a third way. I mean ultimately they're B to B, you're selling to other businesses and that's where your revenue's coming from. However, you look kind of like a B to C company in the ways that you measure product adoption and kind of go to market. In other words, you know, we're often tracking the leading indicators of commercial success in the form of usage, adoption, retention. Really consumer app, traditionally based metrics of how to know you're building the right stuff, and that's what product led growth companies do. And then you ultimately have commercial traction in a B to B way. And I think that that actually kind of looks like a third thing, right? Like you can do these sort of funny zany marketing examples that you might see historically from consumer businesses, but yet you ultimately make your money from the enterprise who has these de-risked high value problems you can solve for them. And I selfishly think that that's the best of both worlds because I don't have to be like Evan Spiegel, guessing the next consumer trend or maybe creating the next consumer trend and catching lightning in a bottle over and over again on the consumer side. But I still get to have fun in our marketing and make sort of fun, like we're launching the world's largest game of rock paper scissors being played with computer vision, right? Like that's sort of like a fun thing you can do, but then you can concurrently have the commercial validation and customers telling you the things that they need to be built for them next to solve commercial pain points for them. So I really do think that you're right by calling this a new category and it really is the best of both worlds. >> It's a great call out, it's a great call out. In fact, I always juggle with the VC. I'm like, it's so easy. Your job is so easy to pick the winners. What are you talking about its so easy? I go, just watch what the developers jump on. And it's not about who started, it could be someone in the dorm room to the boardroom person. You don't know because that B to C, the C, it's B to D you know? You know it's developer 'cause that's a human right? That's a consumer of the tool which influences the business that never was there before. So I think this direct business model evolution, whether it's media going direct or going direct to the developers rather than going to a gatekeeper, this is the reality. >> That's right. >> Well I got to ask you while we got some time left to describe, I want to get into this topic of multi-modality, okay? And can you describe what that means in computer vision? And what's the state of the growth of that portion of this piece? >> Multi modality refers to using multiple traditionally siloed problem types, meaning text, image, video, audio. So you could treat an audio problem as only processing audio signal. That is not multimodal, but you could use the audio signal at the same time as a video feed. Now you're talking about multi modality. In computer vision, multi modality is predominantly happening with images and text. And one of the biggest releases in this space is actually two years old now, was clip, contrastive language image pre-training, which took 400 million image text pairs and basically instead of previously when you do classification, you basically map every single image to a single class, right? Like here's a bunch of images of chairs, here's a bunch of images of dogs. What clip did is used, you can think about it like, the class for an image being the Instagram caption for the image. So it's not one single thing. And by training on understanding the corpora, you basically see which words, which concepts are associated with which pixels. And this opens up the aperture for the types of problems and generalizability of models. So what does this mean? This means that you can get to value more quickly from an existing trained model, or at least validate that what you want to tackle with a computer vision, you can get there more quickly. It also opens up the, I mean. Clip has been the bedrock of some of the generative image techniques that have come to bear, just as much as some of the LLMs. And increasingly we're going to see more and more of multi modality being a theme simply because at its core, you're including more context into what you're trying to understand about the world. I mean, in its most basic sense, you could ask yourself, if I have an image, can I know more about that image with just the pixels? Or if I have the image and the sound of when that image was captured or it had someone describe what they see in that image when the image was captured, which one's going to be able to get you more signal? And so multi modality helps expand the ability for us to understand signal processing. >> Awesome. And can you just real quick, define clip for the folks that don't know what that means? >> Yeah. Clip is a model architecture, it's an acronym for contrastive language image pre-training and like, you know, model architectures that have come before it captures the almost like, models are kind of like brands. So I guess it's a brand of a model where you've done these 400 million image text pairs to match up which visual concepts are associated with which text concepts. And there have been new releases of clip, just at bigger sizes of bigger encoding's, of longer strings of texture, or larger image windows. But it's been a really exciting advancement that OpenAI released in January, 2021. >> All right, well great stuff. We got a couple minutes left. Just I want to get into more of a company-specific question around culture. All startups have, you know, some sort of cultural vibe. You know, Intel has Moore's law doubles every whatever, six months. What's your culture like at RoboFlow? I mean, if you had to describe that culture, obviously love the hacking story, you and your partner with the games going number one on Product Hunt next to Elon and Tesla and then hey, we should start a company two years later. That's kind of like a curious, inventing, building, hard charging, but laid back. That's my take. How would you describe the culture? >> I think that you're right. The culture that we have is one of shipping, making things. So every week each team shares what they did for our customers on a weekly basis. And we have such a strong emphasis on being better week over week that those sorts of things compound. So one big emphasis in our culture is getting things done, shipping, doing things for our customers. The second is we're an incredibly transparent place to work. For example, how we think about giving decisions, where we're progressing against our goals, what problems are biggest and most important for the company is all open information for those that are inside the company to know and progress against. The third thing that I'd use to describe our culture is one that thrives with autonomy. So RoboFlow has a number of individuals who have founded companies before, some of which have sold their businesses for a hundred million plus upon exit. And the way that we've been able to attract talent like that is because the problems that we're tackling are so immense, yet individuals are able to charge at it with the way that they think is best. And this is what pairs well with transparency. If you have a strong sense of what the company's goals are, how we're progressing against it, and you have this ownership mentality of what can I do to change or drive progress against that given outcome, then you create a really healthy pairing of, okay cool, here's where the company's progressing. Here's where things are going really well, here's the places that we most need to improve and work on. And if you're inside that company as someone who has a preponderance to be a self-starter and even a history of building entire functions or companies yourself, then you're going to be a place where you can really thrive. You have the inputs of the things where we need to work on to progress the company's goals. And you have the background of someone that is just necessarily a fast moving and ambitious type of individual. So I think the best way to describe it is a transparent place with autonomy and an emphasis on getting things done. >> Getting shit done as they say. Getting stuff done. Great stuff. Hey, final question. Put a plug out there for the company. What are you going to hire? What's your pipeline look like for people? What jobs are open? I'm sure you got hiring all around. Give a quick plug for the company what you're looking for. >> I appreciate you asking. Basically you're either building the product or helping customers be successful with the product. So in the building product category, we have platform engineering roles, machine learning engineering roles, and we're solving some of the hardest and most impactful problems of bringing such a groundbreaking technology to the masses. And so it's a great place to be where you can kind of be your own user as an engineer. And then if you're enabling people to be successful with the products, I mean you're working in a place where there's already such a strong community around it and you can help shape, foster, cultivate, activate, and drive commercial success in that community. So those are roles that tend themselves to being those that build the product for developer advocacy, those that are account executives that are enabling our customers to realize commercial success, and even hybrid roles like we call it field engineering, where you are a technical resource to drive success within customer accounts. And so all this is listed on roboflow.com/careers. And one thing that I actually kind of want to mention John that's kind of novel about the thing that's working at RoboFlow. So there's been a lot of discussion around remote companies and there's been a lot of discussion around in-person companies and do you need to be in the office? And one thing that we've kind of recognized is you can actually chart a third way. You can create a third way which we call satellite, which basically means people can work from where they most like to work and there's clusters of people, regular onsite's. And at RoboFlow everyone gets, for example, $2,500 a year that they can use to spend on visiting coworkers. And so what's sort of organically happened is team numbers have started to pull together these resources and rent out like, lavish Airbnbs for like a week and then everyone kind of like descends in and works together for a week and makes and creates things. And we call this lighthouses because you know, a lighthouse kind of brings ships into harbor and we have an emphasis on shipping. >> Yeah, quality people that are creative and doers and builders. You give 'em some cash and let the self-governing begin, you know? And like, creativity goes through the roof. It's a great story. I think that sums up the culture right there, Joseph. Thanks for sharing that and thanks for this great conversation. I really appreciate it and it's very inspiring. Thanks for coming on. >> Yeah, thanks for having me, John. >> Joseph Nelson, co-founder and CEO of RoboFlow. Hot company, great culture in the right place in a hot area, computer vision. This is going to explode in value. The edge is exploding. More use cases, more development, and developers are driving the change. Check out RoboFlow. This is theCUBE. I'm John Furrier, your host. Thanks for watching. (gentle music)

(light music) You are Cube alumni. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) You are Cube alumni. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) You are Cube alumni. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) >> Today as a country, as a universe. >> Narrator: Congratulations, Reggie Jackson. You are Cube alumni. (light music) (light music) >> Narrator: Live from the Silicon Valley, It's the Cube. Covering Google Cloud Next 17. >> Hello and welcome to the Cube special coverage of Google Next 2017. This is the Cube's two days of live coverage here in Palo Alto studio. We have reporters and analysts on the ground. We have all the Wikibon analysts in San Francisco. Have been up there since Monday for the Google analyst summit. As well as reporters at the keynote. We're going to be going live to folks on the ground for a reaction and commentary from the keynotes. As well as all the big break outs and news coverage. Again, two days of live coverage and we want to put a shout out to Intel for their sponsorship and allowing us to do the two days of in depth coverage. Really breaking down the Cloud. And really talking about this new mega trend around Cloud service providers where it's a multi-cloud game, which is pretty clear that's happening. And then the SaaSification of the world with AI machine learning. Really changing the game on infrastructure, software development. This is the digital transformation. This is the May trend. And here to help kick off our two days of coverage is venture capitalist, Scott Raney, who's a partner at Redpoint Ventures, who has a lot of history in network software SaaS. Scott, thanks for joining us on the kickoff here. >> My pleasure. >> For our coverage. Yeah, the big story I on Google News is obviously Diane Green, great executive. She gets a lot of criticism for her presentation. Some people were saying it's a little bit sleepy, but she's got a folksy kind of, I call it the Berkeley kind of vibe, but she's super smart. She's a very cool person. But she came in from VMWare, which has a lot of chops in the enterprise so it's no surprise that Google Cloud is now marching heavily towards the enterprise. They have all the window dressing. You're seeing the all the check boxes next to the sales and marketing, some of the things that they're doing. But the end of the day, it's an AI machine learning at the center of all this. Where data and a new cloud developer or new developer market has been emerging very fast. They call it cloud native. You're investing in this space. Give me your thoughts on this because you guys have to look at the 20 mile stare down the road. Look at kind of that five year horizon or plus for investments whether it's early stage or what not, but you guys have done a lot with startups that have been successful. Twilio went public that you're on the board of. You have a lot of investments in there that are doing very, very well. The developers, the opportunities, what's your take as an investor writing big checks. >> Yeah, well I think Google is a really interesting way to start this conversation. Not just the Google Cloud platform, but Google as an entity. I think Google is frankly been defining about 10 years ahead of where enterprises are in terms of how they're thinking about building and deploying applications. And so, if you look at Google, the work they've done to actually support their internal efforts, these guys then create white papers, the white papers are then disseminated, and then a whole set of industries get kicked off around those. So obviously one of the great examples of that is what happen around Hadoop and that wave. I think what we're in the process of seeing right now is a whole series of innovations that are being developed around more kind of cloud native technologies. I think Kubernetes is a great example, which is really the outgrowth of work that Google had done around Borg. And so we spend a lot of time thinking about the work that Google's, the things that Google is working on now. Recognizing that's the future of enterprise computing. Obviously, it takes a while to get there. But, there have been massive industries you can create from that. >> And it's transformative too. Again, I mentioned Twillio. They went public. Great service. We saw Snap go public. They're now running on Google Cloud and some on AWS. There's game changing opportunities out there that are going to come out of these unique perspectives that developers and entrepreneurs might have. And say hey I'm going to innovate on camera technology. That becomes Snap, which becomes kind of a unique, weird app and then to a main stream. This is not a one off. I mean there's a lot happening around creative, young entrepreneurs and old, some guys our age. But either way, it's not just apps. It's transformation at the network level. All the way up to the top of the stack. >> Yeah. >> What are the trends around that? I mean because machine learning is obviously hot. What are you hearing for pitches? What's coming through your door? What are you looking at? You guys see a lot of deals. What's the trends that are coming out of there? >> Well, every pitch we see has machine learning in it. Every company has become an AI company at some level. So that's clearly a big trend. I think for us the way that we look at it in terms of investments is we're recognizing that the algorithms are really becoming commoditized in some level. And Google, with TensorFlow, is actually helping make that happen. As we just talked about, they're democratizing machine learning at some level. The key there is data. And so, when we look at these companies, we're looking for companies that have a unique, proprietary access to data that they can apply those algorithms to, deliver insight. I think one of the more interesting areas or applications around that we're seeing is in the SaaS space. Kind of upper level at the cloud space, how it's really not enough now to build a SaaS application that just automates a business process. What you have to do is deliver insights. You have to help make the people that are using these applications better at there job at some level and the way to do that is through things like machine learning. >> What's interesting, Peter Burris, who's one of our heads of research for Wikibon pointed out, last week when we we're covering Mobile World Congress, he goes it's interesting, you know years ago, when I was breaking into the business in the late 80s, early 90s, it was known processes, unknown technology, and those were automated. Now you have known technology and unknown processes. So getting those insights to get that discovery could really disrupt existing incumbents, big players. So someone can innovate, say hey, I'm going to innovate on a new process that's emerging. This seems to be the big trend that's going on and again the software model is changing. So how do you guys see entrepreneurs looking at the AI and are they that focused on that? Or do they see that? I mean what are the key areas? Do they actually say hey, I'm going to disrupt this marketplace with this one feature? We always hear the MVP or pick something and do it great. What are some of the things that you've seen? >> We're really seeing two things in the AI and ML space. We're seeing one is the general kind of platform play. People that are trying to actually offer machine learning to developers in some way, shape or form. And the reality is I think those are very difficult businesses to build. I think Google Cloud is actually extremely well positioned to be able to actually kind of drive that forward for developers based on all the work they've done internally and they way that cloud is built and architected. The second are applications are AI and ML. And that's where we're spending the vast majority of our time because we think that's where the most value we be created there for folks that don't own a cloud like Google. >> The thing that's interesting about entrepreneurs is it's been a nice thing, the cloud you can get into the game with open source and build a business. You don't have to get all the, provision the data center. That's kind of been talked about, it's not new news. Yeah, you can get up and running, but it's interesting. It was easy to get into the enterprise and then all of sudden now, as it gets more complicated, we're almost going back to the old days of it was really hard to crack the code in the enterprise. It seems to be a lot of new table stakes are emerging. It used to be could native, oh we're going to go to the enterprise. And you saw box.net, now being Box and Dropbox, they're getting in the enterprise very easily. But now, as we go I'd say post-2012, all these new requirements start to rear their ugly head around it's hard to get into the enterprise. So this is something that Google is certainly challenged with right now is that they have a lot of tech, they're serious about the enterprise, that's clear. But to be an enterprise contender and winner and winning deals, how hard is it to win the enterprise? And is that some that you see where the enterprise landscape has changed where it's harder or is it easier? What's your thoughts in the complexities in the enterprise? >> Yeah, I maybe have a different point of view than you do. Which is actually, I actually think it's actually easier now to penetrate the enterprise at some level than it ever has been before. But it has to start with product. And open source is an incredible phenomenon that we're seeing that's kind of overtaking the way that enterprises think about building infrastructure today. I don't think you can build an infrastructure company unless you're offering it as open source software. And so, what we look for in terms of investments and I think what entrepreneurs need to do is think about how do I build products that enterprises will love and release that as open source and open to see some level of adoption. When you see that then that's the best path to be able to go in and sell to them and building revenue around it. Kind of transitioning back to Google and what they're doing with the cloud effort, I think that their approach is actually, it's intriguing. You know, Diane is a world class executive in this way and, you know, I think brought in the last big transition that we've seen through the work she did with virtualization. And I wouldn't bet against her here. I think the things that those guys are doing is offering a pretty compelling set of higher level services now that are getting traction with things like BigQuery. I think TensorFlow is obviously very interesting. And then what they now announced recently with Spanner as a service. These are all technologies that Google understands and mastered and are very compelling technologies that I think the average developer will want. And they are highly differentiated from the services that are available from the Amazon's and Microsofts' of the world. >> Yeah, Spanner certainly got that horizontally-scalable mojo going on. They still got some work to do outside of MySQL and there on the relational database side, which we're watching. But they know that. I mean Google is clearly not saying they're, you know, fully-baked. They're actually candid in the analyst meeting. They were very candid on the security side and very candid on some of these things that they know they've got to do. But they are peddling as fast as they can. So I got to ask you the venture capital question. Developers are out there. Because there was a line, literally a blockbuster as they called it. People around the block to get in. Google IO had similar attraction. Those events are awesome. Google runs great events. They have, I would call them the technology store. People love to go in there and see what they have. But as an entrepreneur coming in, I'm going to build on a stack, whether it's Amazon or Google or somewhere else, you got to worry about the viability when you have the big gorillas out there. You got Amazon, now Google. What's the formula for and what do you worry about as an investor because the things you must think about is okay, what's the approach, where's the viability, is there a marketplace, is there monetization, can they get traction, can they go beyond the first three million in sales, because SaaS you can get there pretty quickly, as it's been discussed. What are the fears that you worry about and what advice would you give entrepreneurs as they start to start really innovating and saying hey I'm going to take the democratization of AI and I'm going to do some damage. I want to enter a market. These are considerations that you got to think about and you, as an investor, where's the risk? And what's the opportunity? >> Oh man, well there are lots of risks starting a company. We could talk for an hour about the challenges associated with being an entrepreneur. It's probably the hardest job you can imagine having. You know I think that the first and foremost is you got to build products that people love. And you got to solve a real problem. And so, I think for us as investors, we look for that. It's different now in enterprise investing in infrastructure than before where there used to be 10, 20 million dollar efforts required to build the technology and then you take it to the enterprise. And you would hope that it would sell. Now, with a couple million dollars, you have the ability to go out and write some compelling software, release it in the open source and see whether or not it gets traction. And then, really the challenge is figuring out whether you can monetize that or not, right. And in today's model, that's really where we struggle. It's ultimately in how you ultimately package this and sell it. I think that the primary models that we're seeing are either some form of upsell on open source, so either service support, open core, or an enterprise grade application built on top of the open source. The other alternative is to deliver it as a service. And we see lots of folks that are taking that open source and saying we're going to run this as a service. We have a company, a platform of mine, that does that for cribinetties, but there are companies like Data Bricks that are doing that for Spark and the whole data pipeline. And that is potentially a very compelling model too. >> Do you have a formula or an algorithm for investment? I remember talking to Jeremy Lu way back in the day and I just saw him in an interview on Snapchat, was an investor and he actually jumped into the stats with Evan Spiegel and saw the traction cause he was skeptical. A lot of people had passed on it, but you know that story. Is there an algom that you look for besides the team and being an exceptional team of people, you know technical chops and product chops. Is there a way that you look at to identify traction in this marketplace because it could be, there's a lot of turbulence, mircoservices, you got Kubernetes, another Google innovation that's kind of becoming a glue layer if you will across services. Is there a way to say oh that's got traction, I like that? Or here's some benchmarks that I look for for hurdles in ventures. >> Yeah, within this infrastructure space primarily around models that are going to be delivered as open source, there's a couple things that we can look at. We'll track GitHub stars and so we'll get a sense from that how the community views this. Whether this is something that they are particularly interested in and the level of traction they're getting within that community. It's almost like that is almost like a stamp of approval from the technology community that says this is a really cool project, right? And then, beyond that you start to look at download volumes. And to understand just how widespread the adoption of this technology is. Those are imperfect metrics, you know. And so, a lot of times it comes back to >> Market forces or whatever. >> Switching gears and looking at the customers and asking them the kinds of problems they are experiencing and whether or not these technologies have a chance to actually address real long standing challenges that they've had in either building or deploying or running applications. And so, it's different than consumer. Yeah, consumer is a little bit easier to measure. And you have a lot of data. Consumer has it's own challenges and it's very difficult to kind of predict a priority or what's going to be successful. But the good news for us is that with high-quality teams, these guys typically know where to focus and where to spend time and ultimately will be able to create it. >> And customer traction is always a great one to look at. I mean sell the data points. Scott Raney, what's new with Redpoint Ventures? Give a quick plug for what you guys are doing, what you're investing in, size of the fund, how much dry powder you have as they say. Are you still writing checks? What kind of checks? >> We are in business and we're looking for great entrepreneurs. So we have two funds. One is a 400 million dollar early stage fund that focuses primarily on Series A and an occasional Series B. And then we have a 400 million dollar early growth fund that is really more an occasional Series B and Series C. You know our attitude to the entrepreneurs is they should be indifferent to which fund they're in. We treat every investment the same. Really, we just want to be a part of great companies and get a chance to work with great entrepreneurs. >> And you guys also sponsored the party last night with the CNCF After Cloud Native Compute Foundation. >> Yeah. >> How'd that go? What were some of the conversations in the hall way there? Or in the hall way, in the event, it was a social event, but you know great community, the CNCF After Development. A couple new projects emerging. >> They've done some great work. And the projects that are coming in represent a lot of the foundation work that's going to be required to build cloud native applications. The first thing we did at this event last night is try to find what cloud native actually is. (laughs) And I think everybody has a different definition for that. >> What's the most common one? Is there a trend pattern in there? >> Yeah, I think people were saying these are applications that are built, traditionally built, using containers. They're built leveraging microservices. And they are built with the assumption that the underlying infrastructure is going to be ephemeral in some way. So you know built... >> And you have a pony in that game with Azicorp so update on those guys? >> It's a company that is doing extremely well and solving a broad set of problems around helping developers build and run applications on top of the cloud and I think what were setting there and we're seeing kind of across the board is a general desire to start to think about multi-cloud. To start to understand what it takes to actually deploy applications and run applications across multiple clouds. And also to be more agnostic about what they underlying substrate looks like. And those are trends that bode well for Google and Microsoft. >> Yeah, we're excited, we're going to be watching. Scott, thanks for coming on. We're going to be watching that. Kubernetes, that orchestration layer that's going on around microservices that's a hot I'd say battleground around innovation, a lot of good things happening there. Great opportunities when there's a lot of turbulence. Great opportunities to invest. Good luck with your investments. Scott Raney, partner at Redpoint Ventures. Very active in the community. A great VC, check him out. It's the Cube two days of live coverage all day. Going to 4:30, 5:00 pm today. And then tomorrow, Thursday. And then we're off to South by Southwest again. More coverage, we wrap with more coverage after the short break.