 Hello everyone, welcome to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We've 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. You know, 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 has been talked about more as just as much as the large language models and these foundational models are emerging. 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 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 20s again because I would be all over this. It's just so, it's the intersection of 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 batten all the students away kind of trying to get hired in there probably. I only imagine you're hiring regimen, I'll ask that later. But first, talk about what the company is that you're doing. What's it, 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, we're going 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 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 rewrite access. So, do you think about it with the programmers 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 100,000 developers, including those in half the Fortune 100 so far in that mission. Whether that's Walmart understanding the retail and 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, 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 want to start and I think it will be, should we do it right, the world's largest in writing the wave of bringing together the disparate pieces of that technology. What was the motivating point of the formation? Was it, 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 ARKit, 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 better, 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 Imgur, 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 3 won the product of the year. So we joked that we share an award with Elon or share in blowing titties. But frankly, so that was 2017. Roboflow wasn't incorporated as a business till 2019. And so, you know, when we made Magic Sudoku, we, I was running a different company at the time. Rad was running a different company at the time. And we kind of just put it out there and we're 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 you're fridging, 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 that 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 they 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 gonna 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 really seeing that externally. 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, break the rules, see how we can push the envelope on the board games. That's how companies get started, great story. I got to ask you, okay, what happens next? No, okay, you realize this new tooling but this is like how companies get built. Like they saw their own problem because they had, because they realized this one but then there has to be a market for it. So you obviously 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 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, 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 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 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 on theCUBE all the time, developers of the new de facto standards bodies because what they adopt is pure, you know, meritocracy and they pick the best if it's self-service and it's good and it's got open source community around it. They're in, it's all in and they'll vote. They'll vote with their code. So, 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 Wi-Fi. And the debate was specifically computer vision that facial recognition, someone was, 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 Wi-Fi. 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, the 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 because 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 warfighter for instance you might want to have highly available and maybe high availability. I mean, these are words that mean something. You've 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 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 river bank. 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 it's a zillion use cases. I heard of a use case interview to a company that does GPU division to help people see if anyone's jumping the fence in 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, so many use cases. So how do you guys talk to the marketplace when you say, hey, we have generate AI for computer 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? Like here's 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 don't know if you know this but there's a specific type of white fish 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 and the sushi that you're eating. But security camera analysis, transportation flows, plant disease detection, really, you know, smarter cities with 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 John for 15 minutes and said, let's guess every way computer vision can be used, we wouldn't be able to fill, we would need weeks to list all the example use cases. And we miss everything. And we miss it all. 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? Cause I imagine if I was in school right now, I'd be all over it. Do you, are you seeing master's thesis is being worked on with some of your stuff? Is there, is there is the uptaken 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 Rubble Flow 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 industry. 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 site building with Rubble Flow pop up. And so that helps inspire some of the use cases. Now, what's interesting is that the use case is relatively, useful or applicable for the business for 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 Rubble Flow and making use of open source tools and tandem with the tooling that we provide just as much as industry. And 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 good order of period of time. And we're experiencing that transition. Computer vision used to be kind of like a PhD, multi-year investment endeavor. And now with some of the tooling that we're working on and open source technologies, 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, I've got a great front row seat, ground floor, present at creation wave here. I'm seeing a pattern emerging from all my conversations on theCUBE with founders that are successful like yourself, 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. So can you share your thoughts on the startup scene, other founders out there and talk about that. And then I have a couple of questions for like the enterprises, the old school, the existing legacy, a little slower, but the startups are moving fast. What are some of the things you're seeing as startups emerging in this field? I think you make a great point that independent of Rebelflow, 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 you are and pulling the product out of you for things that they need. The second segment that you have might be calling SMB but not enterprise who are able to purchase and aren't 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 to pulling the product out of you. Currently, 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 viewers 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 and market that's being created. It's interesting. I mean, 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 because that's what you're talking about as a category creating. And I think developer tools, so people often talk about B2B and B2C businesses. I think developer tools are in some ways a third way. I mean, ultimately they're B2B. You're selling to other businesses and that's where your revenue is coming from. However, you look kind of like a B2C company in the ways that you measure product adoption and kind of go to market. In other words, 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 B2B 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 yeah, you ultimately make your money from the enterprise who has these de-risk 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 currently have the commercial validation and customers telling you the things that they need to be built for them next to provide commercial, 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 joke with the VC. I'm like, it's so easy. Your job is so easy to pick the winners. What do you talk about so easy? I go, just watch what the developers jump on. And it's not about who started it. It could be some of the dorm room to the board room person. You don't know because that B to C, the C is that it's B to D, you know, it's developer because that's a human, right? It'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 multi-modal, but you could use the audio signal at the same time as the 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 for 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 you can think about it like the clasp 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 computer vision, you can get there more quickly. It also opens up the, I mean, CLIP has been a 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 gonna 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 when that image was captured or had someone describe what they see in that image when the image was captured, which one's gonna 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, just define CLIP for the folks that don't know what that means? Yeah, CLIP is a model architecture. It's an acronym for Contrast of 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 the bigger sizes of bigger encodings of longer strings of text 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 of 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, it doubles every whatever, six months. What's your culture like at Roboflow? I mean, what's it, if you had to, if you had to describe that culture, I'll see, love the hacking story of you and your partner, what the game's going number one on product hunt with next to Elon and Tesla. And then, hey, we should start a company. Two years later, what, that's kind of like a curious, inventing, building, hard charging, but laid back. That's my take. What's your, 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. A second is we're an incredibly transparent place to work. For example, the, 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 used 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 a place 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 you need to work on to progress the company's goals. Any of 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. We're looking 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 the, basically you're either building the product or helping customers be successful with the product. So in the building cap for other 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 kind of be your own user as an engineer. And then if you're enabling people to be successful with the product, 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 slash careers. And one thing that I actually kind of want to mention, John about 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 on-sites and at Roboflow everyone gets, for example, $2,500 a year that they can use to spend on visiting coworkers. And so what sort of organically happened is heat numbers have started to pull together these resources and rent out like lavish Airbnb's for like a week and then everyone kind of like sends 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. So. Yeah, quality people that are creative and doers and builders. You give them some cash and let the self-goverting begin, you know, and the creativity goes through the roof. That's a great story. I think that sums up the culture right there, Joseph. Thanks for sharing that. And thanks for this kind of 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 develop and developers are driving the change. Check out Roboflow. This is theCUBE. I'm John Furrier, your host. Thanks for watching.