No Priors Ep. 2 | With Runway ML’s Cristobal Valenzuela

Key Takeaways

[Music] Chris welcome to the podcast thank you for having me here super excited to chat with you so can we start all the way back I think you are the only person I know with degrees in economics business design and then also went to Art School how did how'd that happen and then how did you stick an interest in ml in there that became very real at some point yeah that's an interesting question I've always been very curious about just things in general and so if you're trying to like find ways of channeling that Curiosity I'm originally from Chile and I study in Chile a combination of like business and econ and then went into design and it was a very particular design kind of like program I spent a lot of time with physical Computing which is like working with Hardware with like um Electronics mostly applied to design and like art and while I was doing that it was also Consulting so for a moment I thought I had like two lights uh I was like doing art on the one end with like arduinos and electronics and on the other side it was like Consulting for these Banks which was very like different but I love it I think it's it's its perspectives on World Views that are very opposite at the same time you gained from being at both and around that time I just spent like three four years doing that at Chilean and started like teaching myself software engineer and like programming that was just driven by curiosity again just like exploring or experimenting which I think has I would consider a constant like characteristic of how we think about what I want to do and what I want to like learn and it also has think I'm better in in the company itself in Runway looks very short I kind of like fall in love or was experimenting with early computer vision models in 2016 15 and then went into Rabbit Hole apply and got a scholarship at NYU and then spent like two years in art school ITP uh that's the name of the program it's a very unique program happy to go deeper into into that particular program because for me there was very fundamental kind of like piece in in my career of like understanding how to bridge business design art and and technology in a cohesive way but yeah I think just curiously I think charged me a lot amazing and uh now you have one life that combines those but in the art side how should I picture Arduino electronic art media arts probably is the best uh way of describing it I think it was just interested in the wild the the kind of like experimental side of Technologies how do you take like this this Hardware the systems these networks and build interactive experiences so I have the chance of presenting in a few festivals uh with with actually with some of my co-founders there we were at RS Electronica which is this large like Electronic Arts Festival midiarts I think for me media art is a way of like expressing a worldview um using technology like any other form of art like you just kind of like experimenting and like reflecting and and expressing a world view using a piece of like that tool and in this case like it happens to be that we like to express it via like computers and software and writing software so far more of Art and right making Hardware is also so form of art one thing I've I've remember earlier in my career when I was doubling between like art and business I met this very famous Chilean artist that sees a photographer and he was just like mentoring me and like we were chatting and he was speaking he was like how do you think about doing this kind of like installations and like I was exposing it a museum the like National Art Museum in lechile and I told him like it's a wonderful new world like I've never been exposed to this world and I remember the answer struck with me forever which was like Chris this is the same world we all live in the same world right it's the same right we just build like silos and like arbitrary definitions of what is what and it it just really struck with Matt I think he just said it like he wasn't really thinking about it but I really like stuck with that and and I think that's that's how I like to lick off the world like it's it's just the same world you're gonna apply different points of views and perspective on that we build arbitrary like definitions of like this is this is hard and that's that's like design and that's econ and that's business but I think through creativity and curiosity comes from just like looking at it as a whole and taking things that weren't supposed to be part of one thing and adapting them and sometimes it's hard because you need to like learn things that you've never done before and it's uncomfortable and it's perhaps you feel like an imposter like you haven't you shouldn't be doing this I've learned not to occur to be honest I just like you just drive accuracy like you'll figure something out and I really like that that's super cool yeah it seems like a lot of the history of Silicon Valley actually ties in really closely with art and the art scene so if you go back to like the Stewart brand world of the 70s or some of the early things that were being done on the Mac you even look at some of the people in technology where the the art side of them is understated you know like Paul Graham obviously wrote a whole book on this hackers and Painters and as a painter himself but there's people except cambar who started a company that googlebot and has done a lot of crypto related things he was a co-founder of cello and he he's exhibited digital art at the moment as well and so it just kind of feels like it's almost under discussed now in terms of this overlap between technology art and the two scenes except for you know occasionally when people go to Burning Man or something they bring it up other than that it seems like it's very under focused on yeah I agree to be honest I've been in New York like six years and a Runway now it's gonna turn four years and I was also so new to like just the the tech world and as I have like I've never been to SF like three years ago right so I'm relatively new to the space and but I think what how we approach it was with that same level of curiosity of like I'm gonna figure out I'm gonna learn about it and I think that the there's two like sides of that the one is that it takes time for you to adapt to that because it's just new like everything else you just you need to understand it you need to understand the patterns of that that subject that domain that area right but at the same time I'm looking at it from eyes with fresh eyes with things that the ecosystem itself has considered like Norms I've um I don't consider them nervous like I'm just like when I try new things right and I think that opens the door again to do new things and experiment with new things and that has I think being a consistent like path in both my career but also in Runway as a whole that but we look at things we try to look at things with like very fresh eyes and like pretty much with like a first principles kind of mentality to it it's like okay why are we doing this but really why and they go to the basic aspects of it and then innovate I think that's a lot of innovation comes basically from from kind of that way of looking at the world Runway is a I think a very creative shape of product it's not the kind of product you can come up with if you're just like casting around for a good idea it obviously comes from creativity and Discovery and maybe what you could do for our listeners actually just they should go try it but can you explain how Runway works as a tool and what people do with it to set contacts yeah totally also happy to set a bit of context of the company itself so I think that better helps contextualize the product itself the best way of describing Runway I would say is to think about it as I I'd apply a research company and we do core fundamental research on like neural networks for both content creation and video Automation and journey models we then transferred those models into an infrastructure system to deploy those algorithms and systems in safe ways and in in in in in ways that will make us build products that are useful for people right and those products can take different shapes and forms we have around 35 different what we call AI power tools or magic tools and those tools help serve a wide spectrum of creative tasks from traditional like editing editing videos or just audio or images has been a very expensive time consuming and sophisticated process and so we build systems that help you do that so we have tools like green screen for example which which a lot of broadcasting companies and film studios and post-production companies use to reduce the time of rotoscoping which is if you ever speak with a filmmaker that's that's the one thing no one's doing just no one's do but you have to do it and so we basically just help you reduce that time and we also have tools that help you ideate and design and craft and we have a set of like Suites for generative image editing for genetic video editing the best way perhaps to think about it is it's a creative collection of tools and systems that just help you augment your creativity in any way you want it's from an Origins perspective like you had this thesis project which were all these creative tools and it's really I remember like watching the presentation is around accessibility of you know the increasing number of algorithms that could help people in this sort of creation and editing process for different modalities now when we met in 2019 you framed it quite differently as this kind of desktop app store for ML models can you talk about the iterations from that collection of algorithms you were experimenting with to like the App Store idea to where Runway is today yeah totally a lot has happened I would say over the last decade or so when I started building Runway it was perhaps the audits net image.net like Mom and there was image classification was that the kind of like the big thing and the Breakthrough and a lot of interesting applications were coming out of that time but still very early like tensorflow was just perhaps a year old pytharch might not even have been released at the time I think python was 2016. guns were just like very early early like Inception time but what I kept saying was there's this neural like aesthetic this neural like capabilities that are impacting not just like the visual world or like the perhaps Industries and markets like I'll solve driving cars that are using a lot of these Technologies and Hardware but the outputs are very interesting from a visual perspective right there it seems to be a correlation and a approximation towards the visual domain and so I started just experimenting with what what does actually that mean right what do you mean by how do you experiment with this sophisticated algorithms that were very early that had all this like obscure Cuda dependencies and like C plus plus like libraries that were just very research centered because they were basically research right like core research but it was just fascinating by the outputs of the research elements at the same time I mean everything I would say that we consider like the Baseline today and wasn't really like there yet at the time things that progressed radically the space has been growing exponentially but of systems and like software and obstructions to tap into that potential wasn't really there so our first intuition and our first kind of like product experimentation was let's build like thin layer right like basically let's take this this research set of like models and the amount of models are coming up it's just like so interesting let's add a thin layer of like accessibility to those models specifically Target and aimed at creatives right and so if you're a designer a filmmaker an art director a copywriter you might want to tap into some of these things so you want to experiment with them but they're just very hard to get started with so we build at the time was as we're describing like a model directory it's an app store of models right you had we had that run at some point like 400 different models it was one of the first like I would say model hats I think there are a few out there now that you can like tap into and use them this is like very very early and we got the whole system around it we built an SDK we build systems for like deploying those models like into real-time to go like a restful API systems where you can use a model train a model and then deploy that model and so people were building web apps and like interactive like it was GPT one someone was training a model in like fine tuning a gpt2 modeled on a specific purpose of data and then creating an API to build like a text generation app and we had all these like very interesting kind of like layers of like applications that to be honest for us was just a way of learning um learning a lot about the space and a lot about like what was visible what was possible who was kind of like interesting in like building more of this and from there on we kind of like continuously iterating we've learned a lot from that model registry or like model Hub um we still use a lot of those in our infrastructure kind of like Parts on on the app but also we gathered a lot of insights on on how to build these kind of systems in scalable ways how did your technology stack or the approaches that you took a transition over time because I think when I look at the evolution of the area to your point you know a lot of people are doing like CNN and RNN based things and Gans and all this sort of early things in neural networks and then but the analogy maybe I know a lot of people who started companies right before AWS launched and their whole like infrastructure stack got stuck on the past set of approaches and then later a subset of them transitioned onto AWS and stuff such as continued with their own private clouds and I'm just sort of curious how you thought about it as you know obviously diffusion models I think were abandoned around 2015 Transformers 2017 but it took a couple years for all this stuff to catch on and so when did you start transitioning architectures or have you or have you thought about this sort of whole evolution of the field relative to the tools that you provide and Reinventing them over time and everything else no that's that's a great question something I actually think a lot about when you think about like product sequencing on roadmap which is just I would say one of the most important aspects of product buildings like how do you sequence everything you have to do and typically an infrastructure like what makes the most sense how do you spend time like every single day like means a lot in a startup I think for us was a few realizations to be honest one is that the moment something gets released like let's say Transformers or a particular piece of technology that you think would be interesting it could be worth experimenting with I think it takes a collective set of months like 12 24 months sometimes to understand the implications of that right and we've seen this with like language models like gpt3 has been around for some time but they took like a collected 24 hours of 24 months of like just tinkering and experimenting to truly understand like okay where can you go and what can you build and what's possible so I think that we embedded that and we always keep that in mind the second thing I would say is things are changing really fast right and so if you're thinking about building a long-term business on a long-term product which which we are you always kept the desire of like okay what are long-term Birds versus shirts and bats and I think a lot of building and software engineering and and developing products is just saying no to a lot of things there's like customers might want to ask you for to build something and could sound good it could bring your absolutely revenue and some growth but it actually might move you away from like a more consistent long-term plan I think for us was was our decision of those kind of like things and then the third one I would say is the third component of like of how we think about that stack is really understanding our users right who are we building for um and so early on it was more a technical product so you had to know Cuda and Docker containers and managing your Docker and video GPU cards and like you have all this like sophistication that I think it's in some part natural when your things are so early because just the only way of making sense and also you have to build more things but for us we've always been thinking about artists and and filmmakers and creatives of hard and really those things don't really matter that much what matters is like your idea and how you execute that idea and so from the stack perspective we've iterate a lot on the the kind of like back-end side of things but from a user perspective we do it even more on how to present those things and what abstractions and metaphors you need to build to really aim to solve the things that you want to solve but yeah it's a fast growing space so there's a lot of things that are changing in an area where the the research like nobody can keep up with the papers right um the progress is mind-blowing and has been you referred to Runway as I think an applied research lab is that the right term like where do you decide given the progress in the community like when you need to do in-house research and push the state of the art versus exploit what's out there yeah I guess going back to that that set of learnings early on um I think one thing that we realize is um models on their own are not products right a model is it's a research component and and taking a model and productionizing that model it's uh it's a different problem that actually building one single model right or one single task or problem or improving a metric in a specific kind of direction there's a lot of nuances of how that model get deployed it will get uh build how users would interact with it the unit economics of running this kind of like systems as well is very important right so they have all these complexities and as we started like leveraging perhaps open source Solutions at a time or trying to build our own we kind of like quickly realized that having control is like key like you need to be sure that you can understand your stack and you can understand and know how to fix your stack right because if things are changing really fast and you think about going in one particular direction but it then happens to be the case that there's a breakthrough somewhere else you need to react really fast right and you need to be able to incorporate that and if you're just relying on third parties or like just some other Solutions then it might be very hard right and so for us it was a survival kind of like realization that if we really want to make and move the standard of like creative tools in the ways and uh Vision that we had we had to own our stack and so we started building this research team right and this research team has very deep like understandings and knowledges and perspectives on how to build models and we've done this when we've collaborated and contributed to like uh breakthrough moments in like the creative eye space but most importantly we have these researchers working really closely with creatives like half of our team have Arts backgrounds right which is which is very unique and we put a lot of emphasis on finding those those very unique like they're very hard to find folks that can speak Both Worlds like I just went back to the world it's kind of like analogy and so in one single table you can have like a PhD scientist that's been contributing to like fundamental research on the space working really closely with someone who's working on video for 20 years right who's been editing and post-producing films or content right and the things they learn from each other is just so so unique it's so radically different and it helps inform how we build products right and so we don't treat research as a standalone kind of like Department that comes every six months with here's a paper and just like do something with it we see it as an apply thing it's at the core of who we are and like how we drive the product forward and it helps just drive the product in different way I think that the only thing I've learned is that building that muscle takes time right it's not that something you can just like I'm gonna hire a bunch of like creatives and a bunch of researchers so I just put them in a room and like you figure something out it's it's a lot of like learning and like a lot of processes and like Frameworks of how you make decisions how you understand what's worth what's really possible versus what's visible and there's a lot of just just like nuances of how to do that yeah it seems like there's a lot of um Founders now who come from the research community in the AI and ml world and you know you've navigated that extremely well in terms of saying okay let's be very product Centric and yet still capture the best of what new technology has to offer a new research has to offer what do you think are common pitfalls that research-centric Founders should avoid or things that they should think about more as they sort of start their own companies yeah I think it's just phenomenal to see like that progression of more researchers that being perhaps in Academia for too long progressing or moving until it just the operational like Building Products I think it's a great regulation of like you're working or something for six eight months a year but you see something else in the world of someone using something very similar to what you just built and impacting the world in very like meaningful ways I think this is that's great to see people transitioning more I think we need more of that I still think that there's a lot to be learned around the difference between a model and a product and again it there's a lot of like back and forth of how you've embed models into usable products and so coming up with training a model or improving some sort of like quality of Benchmark in some particular way even you have a very cool demo it's a long way to go to like actually build a business and like a reliable like system that will continuously iterate over that and so I think having that more product perspective is always just good and releasing and working with real people as fast as you can I think that's just key I think a lot of researchers just assume how people work and how creatives work and so we'll just do that but like the realities might be very different and so having having tools being used by people is I think the best way of learning how to develop products are there specific areas of research that you're especially excited about when it comes to video or images right now yeah for sure I think I mean everything we've seen on the explosion of diffusion has featured us so exciting to see I think I'm particularly excited about multi-moralities and like combining uh different kind of like input or like outputs in ways that they are yet to be explored I think we're moving away from like very siled like domains so like someone who could be an NLP researcher and computer vision researcher right I think we're like starting to like see them broadly converge and Max and so building a diverse team that can understand like those multi-domains is really interesting and I'm excited to see how that's gonna play out in video and and in images and I'd like to to think also of how you translate again and go back to product with product obsessed but how you translate that into into products that are that are useful right I think a common a common natural evolution of just the creative stack or the creative software Solutions out there they tend to be very specific to the domains of of content so you have a tool that's specialized on like image editing and then you have a tool that specialized on vector graphics and you have a tool that specializes on Motion Graphics which is different from video editing which is different from like post compositive which and you have all this like very sophisticated like software stacks and I think that the very interesting aspect of what I would like to see and we'll probably see more with multi-model systems is that you're able to merge all of those and what I really find interesting about that is that's how we humans think right we don't you don't go to a movie and watch the the video first and then you stop and you hear the audio and you stop and you read the subtitles It's a combination of all of those things right and an art director thinks in all of those things at the short time as well right so having systems that can translate ideas and text descriptions into videos and then having a conversation with what's the input of those videos into like audio and then I think that's the kind of like creativity and set of tools that I'm really excited to to discover and build how do you organize your product after aspects I think to your point you have a really unique approach in terms of effectively turning Research into products or being product Centric in terms of what you're asking from the research organization is there a specific structure you know for example um at one of the companies I started color we basically would embed somebody with a very deep bioinformatics background with the systems team so that they basically inform that team around the needs of what they had and then the rest of the team would build it it sounds like in your case you have people who kind of are in both worlds is there a specific structure or you're like I always put three you know full stack Engineers with a researcher with a product person or the the researcher is the product person or how do you kind of approach all that yeah we're a small team we consistently historically some small team until like two weeks ago we didn't have a product person product was led by a combination of research design and engineering and I think that drives a lot of the fundamentals of truly understanding the things that need to be explored we've iterated a lot on building squads European and like teams or having more autonomy I think it really depends I think you have a you tend to have a different company every like four five six months it's if you've successfully built stuff it can it's a continuous like process and the thing that worked when we were like five people sitting on a table it's not going to really work when you're like 20 and you have new technologies insisting things available and so I don't think there's one answer in particular I think we're pretty much with how we think about product we like to iterate a lot right now we're working a lot with squats and so we've come to a kind of like a place in time where the organization can have a bit more of like domain expertises and like instead of having like very generalized Engineers we tend to like more specializes a little more so you can still jump and be and collaborate but you tend to have a bit of a focus of Aria and we're iterate it without it and seeing how that works maybe we can talk about an example of like what that iteration looks like so you mentioned rotoscoping and like green screening as like a uh like one of the magic tools that Runway creates when you were when we were building that feature like what was hard what were the iteration processes like I think that green screen is a great example of how to build and how to deploy useful like AI products at scale like if you've if when we were building that model directory and we're just like early stages of understanding limitations and capacities and directions if we quickly realized a type of like user that was coming for segmentation models right and at the time we didn't have a green screen tool it was just like a image documentation model and those folks were coming from a specific domain and they're actually applying a model that was image based into a video task and so they're like exporting themselves with FM pack creating these like sequences of images to then render them back in video and it was like why are you doing that why are you what's going on and the thing is like image models don't really work really well with like video right you have the temporal consistency component on like so it's really hard and so we started interviewing them and we we got to open it was like wait it seems like this this could be something we could like improve and we're bringing our research team so we started like iterating more on that but no one ever asked for uh one of one-click solution for green screen right if you ask people what they wanted they wanted a better alternative that was faster to create masks from their current stack right and they're probably using something like rotobrush too right so uh whoa what I would really like would be like a better brush to just brush over my frames right and I think customers and people are really good at telling you like what their problems are they're really hard verbalizing like Solutions and so you aggregate that amount of data you see what parts of the research you see you chat model people and you start prototyping a lot and then we came to like the recession that we could build and we have the expertise to build a system that will help you automate that right and most literature around video objects augmentation which is that in filmmaking is basically known as rotoscoping or green screen right was around like fully automated systems right you fit in a video and the video automatically like understands like subjects and then rotoscopes or segments right one specific Central object or two let's say but like just a few minutes of chatting with a professional filmmaker he'll probably discover that that's rarely the case because the shots the scenes and the compositions and the camera angles really depend and you might want to if you have a shot of 10 people you might want to rotoscope the one on the left but maybe you want the arm from the person on the left and maybe you want the depending on your idea right it's a creative like tool right so it can be should be General and so what we did was we've instead of like relying on fully uh automatic systems we embedded a human in the loop kind of like component in it right and we thought it would be great if before you start doing that you can guide the model like you can tell what kind of like selections or areas of the video then you can zoom in and Define you want and that helps really helped us train the model because we train a model on we build a probabilistic model of like human simulated like human clicks uh on a mask and the model was trained on that knowledge right from the very Bare Bones and that help the the product itself because people were using that model in that particular way and that recession was I would say like a combination of different things it was research like some research knowledge and understanding of what was visible can you build that sanitation model what data sets and what what what do you need to do it who would be using it for how we're going to test if it works and the first version of green screen was working at like four frames per second right it was like incredibly slow it was like not as good as the one we have now which is incredible but it didn't matter it was significantly better than anything else that was at the time right and people were like scrambling to use it just because it proved to be a percentage of uh amount better than anything out there right and people were hacking things and we're trying to like incorporate it's like great that means that like that you've hit something and then we started iterating a lot and so we keep iterating a lot on it but the fundamental piece of how we build products is still pretty much similar to that very cool let's zoom out and talk about Runway as a business so you as you said now you're very intent on building like a long-term durable business who uses and pays for a Runway today sure um again we're devoted to like storytelling and like creative exploration and ideation and that's a wide spectrum of people where you can consider work in the storytelling business right on the one end you have professional really professional people that have been doing this for years right folks working in post-production agencies pfx agencies um broadcasting companies that are creating video as their main business like this is basically what you do right It's Entertainment is sometimes Sports we have a lot a lot of sports companies you know that's kind of counter are it's kind of counterintuitive because like one of the sort of beliefs of many people who look at the research which is fast progressing is like you can't get the quality level for like the sort of highest production value type assets with with today's research so it's really interesting that like you're talking about VFX Studios and and sort of that type of content yeah I think I think there for us is like what the goal is right if you're trying to automate the entire process of like the whole end-to-end system of making a movie yeah like we're not there right we're very far from that there's a lot of things to be that have to be developed that have to be the like research and kind of like understood and test it but going back to the green screen if you look into the processes and the nuances of how video is created and you look at the inefficiencies of how people are doing it right now and you offer these people like a hundred even like 10 or 20 or like whatever percentage of like speed and cost reduction is just so radically better right and it's radically better for two reasons of course it has helped reduce the cost of like you have you have less you have you can do things faster so it's just easier at the same time you can explore creatively more right and this happens a lot I was speaking with this director who's like working on a film and was using Runway and he came up with this idea of like when he was chatting with his editor he was like we should just run with that right just run with the thing that you want to like do and before runwaying something they had to marry themselves or like just log one specific idea right okay let's let's try doing that with that character on the left and that's it because if we try to do two other things it's gonna take us too much time and we just don't kind of for that like every creative is always in a deadline there's always something to do with it it's very waterfall era right you must use the direction and do the whole thing exactly and now now he's telling me like now I can do the three right you can just see the three and pick the one that I like the most right I'm not constrained by the time and the cost I'm constrained by whatever idea I think works the best and that's just phenomenal right and so our goal and I think our goal still is it's not to like build this like kind of like autonomous like systems that don't engage in any sort of like relationship with humans or with creatives on the contrary it's like you have humans coming up with great ideas and they want to express those ideas how do you build systems that will help them get there really quick and sometimes what you need is to get 80 there 90 there and you research going from 80 to 100 is really hard I think that you'll be seeing that in like autonomous vehicles where like it's always like two years ahead and it's like always eighty percent but like that that 20 percent is just really hard because this is really hard but in it's really hard in that domain because if there's a one percent failure like someone might die right in Creative domains it's not the case like even if you're 80 there the 20 like sure you could worry about it I can improve it I can like find ways of like work with that but you've you've made incredible progress from that perspective I think that's actually an interesting like filter for what domains are interesting for Applied research today like areas where there's built-in tolerance for you know lower levels of accuracy um this is one way to look at it and you always integrate with uh there's ways of like combining existing tools right so for produce one for example you can get 80 there and then if your professional filmmaker working on nuke or flame you can do the 20 in that stack right but you still say to yourself like days of work right so it's still better than anything you're using before right so did you advance a lot and I think over time more models will get to like higher numbers and we'll have higher outputs but there's a lot yet to be developed and I think we're still scratching the surface of what's coming what was the moment you know you mentioned there's an evolution both in terms of the number of tools you provided as well as their relative quality in terms of 80 versus more or less and things like that was there a specific moment where you really felt that you had product Market fed or where you felt that okay this is something a lot of people want and they want to use is that was it immediate was it after a specific tool came out like when when was that moment for you I like to think of product Market feed as a spectrum of like you have either really strong product Market fit or like weak product Market fit and like as you build new products and your research you're always seeking to be very on the strong side of things of course I think for us there are a few factors that we've kind of like realized that what we were building was beyond just like a niche because I think we started with a very Niche like audience and everyone like dismissed a little bit of what we're doing like as toys she's just sort of like art students building like some toys uh and I think you shouldn't dismiss toys are like very interesting to learn a lot and I've learned that over time but it's by the time you're building those of course it's just like you're focusing on the output and they're bleachy and they're like abstract and it's just weird and can make sense of it it's only 128 by 128 pixels exactly exactly I was I was actually I remember like uh we had a version of like fairly early like gun system that the the text to image translation we actually still have them online and the outputs are so like this 120 pixels like exactly you're saying uh images that were just blurry it looked like abstract paintings right you just like I mean you type I don't know about blue ocean and you get like a blue form with something so you see if you close your eyes like 10 meters away maybe and you saw Beauty yeah I really like it but at the same time I remember like showing it to advertisers and like I went to like this exciting meeting at this top agency in New York and it's like here guys here's the thing you will be using to work right and they're like Chris this this is a totally like great I mean fascinating technology whatever but like we have work to do come on like move on right and I think I I the main mistake for me was like you're looking at this singular moment in time of that technology you should really be looking at the rate of progress right that thing that I can type a Word and send an image wasn't visible a year ago just it didn't exist right now we have this so just compound and try to like imagine where we'll be in like four or five six years right but the thing is it's really hard because you can't imagine it and I remember people at the time like when they show some of those demos and specifically for Journey models is people ask me like hickers how are you collaging these images right you're taking existing images and you're pasting them together right it's like no this is you're generating them this model has learned patterns around for sure a data set and you're then generating them on the fly but they're these images don't really exist just don't access and so there's a lot of I think mental models that need to be adjusted to really understand it and we've been adjusting that that those mental models and from a product perspective and from a product micro feed perspective I think there's the right moment for the market to use technology and I think that moment has matured and we've seen it more as more people have been exposed to generative models and the potential of them and for us is still like uh there's a lot to build and to develop and to kind of like improve but a few realizations were like when people were starting using runways right you just run with that okay that that means something then you start seeing people just like creating tutorials and like speaking about the product online right with no we don't we sorry for a long time never had like a marketing team or a Content strategy team like everything was just basically people making things and then sharing them online I think that really drives I would say relationship okay we're into something like people are using this every day they're coming and they're sharing with their friends right and they're they're thinking about it every day and they're like I remember arnardius and a person who earlier Runway adopted which is a song We love with the product he sent me like he painted a picture like and he just sent me the picture like to my to my home just like here's like just once you have the first piece I ever made with AI and I was like 2018. oh was it a cat no it was the an abstract painting where it was like he he generated something that was very abstract right and then he painted a canvas and then he used like mixed techniques to just like improve some sounds and like change some colors it was very new and novel at the time it's like wow that's just I don't know interesting but fascinating so with all the uh wisdom of you know Chris four years in and Ronnie's going well like is there is there anything you do really differently if you're gonna start from scratch today to be honest I think we're I'm still learning a lot I think I wouldn't consider like oh we're that's it we're we've I don't know we have a lot of users a lot of companies and that we're basically like chilling or on the contrary I'm like this is the time to be very bold and like continuously learn like the one thing I think it's just great for everyone building the space at least for for for for us from just on a very personal level is I had to do less of the selling like I don't have to tell people like hey this is useful this could be useful like it's just great to like have more of a conversation around yeah possible use cases and it's also great to drive the conversation in very productive and positive ways and understand what's visible and you get more people to build with you right I really would really try and something I'm I tend to code less now but something I try to do a lot is just really think from a company perspective and cultural perspective how do we keep those DNA elements of what makes Runway really unique really really unique and really consistent as you grow because as you grow there are other challenges like organizational advice of like communication and like structure right and hiring as well like you need to bring like the best the best people that but this also like needs to be like a cultural fit right um and that's I'm really obsessed with that I think what I've come to like the recession is like I code less but I code the company in a way yeah and I try to like find ways of optimizing that that set of decisions yeah it feels like every company eventually has two products the product they sell and the company as a product instead of your point I feel like a lot of Founders eventually move into a mode where they're like engineering the company or they're designing the company or the roadmapping the company and that's that's a really important transition and so it's awesome you're doing that I feel so many people wait a very long time and then things start breaking because they didn't think ahead just like you'd think about your back end scaling you know you have to have your company be able to scale and I'm I'm still I'm still learning on exactly that but I think just keeping that learning until it has been yes useful for me yeah one one thing I'd love to get your perspective on somebody because you have such a unique mix of background and skills and customers and everything else is you know there's this emerging debate um in the art World about the role of AI and art and I think if you go back to our history there's always been ongoing questions and contentious not just around technology and art but the role of an artist relative to the art they create and I think like the sort of old school canonical example was Marcel Duchamp signing the urinal you know with our Mutt and um I think it was called the the fountain or something right it was a piece that he submitted and it got refused and it created uh a bunch of uh sort of Scandal at the time or you know Andy Warhol had like the factory and other people would assemble a lot of the art actually with sort of him overseeing it and so it seems like there's been a long history of sort of different approaches to art that at the time seemed very um controversial and now you're just like yeah of course that's that's how you do things or how things were done what do you think about the debates right now in terms of Art and Ai and you know what do you think of the important threads that people are talking about and what do you think of the areas that in 10 or 20 years people look back and say yes it was just part of sort of this art history debate but it in hindsight wasn't really that important I'd like to think a lot about uh what I guess previous moments in history and time as you were referring before that has taught us something about how to like both understand art and like look at the tools that we use for art for me art is is the way of looking at the world and expressing that view of the world in a particular way right and an artist's role I think should be to explore and experiment with different mediums that would allow you to express that in the best way you think possible right um and so people experience with different techniques and different systems and different like structures and and pigments and like tools themselves right even prefer like to Champion even before like Warhol you had previous moments in times where like technical revolutions enable people to look at their world in very different ways and then Express those views of the world in very different ways to whatever was feasible at the time or possible at the time um and an example I go back to often is this idea of in the 1700s like before even painting was like a massive like thing that you can do in any condition situation or like location painting was the realm of like this very sophisticated painters that were like painting in studios right painting was the realm of like people who can afford and were able to understand and master the techniques of the Masters right and and more importantly from a tools perspective it was really hard to get pigments right it's a very practical thing but like you couldn't just pigments didn't access like you could just go to a store and get a get red white yellow and like open something and I have a canvas the way you mix pigments was this very sophisticated thing where you had to hire a master that knew like this obscure techniques and you were like mixturing them and then you you store them in like this sophisticated bladders and you seal them and it was the incredible like complex and expensive process and this time I was like hey we should just like build a tube and then like have this and carry it around right and this maybe it's easier and it was and it was it was a very radical Innovation very simple at the time I think it was very radical the time it's very simple for us now but that would allow us like for a whole new generation of artists to look at Art and be like great I want to take this painting and there's some Mountain that I really like there you didn't see it when the canvas I'm gonna paint in planar air which is that which is which is a thing you paint in planner right you're painting in air in the in the wild and you're able to look at the world and the sky and you're able to like quickly brush like the light right and just being outside of the studio was just not specifically you just stand like before that and then gave birth to impressionism right an impression was like a whole Revolution like impression everything was not really well received because it's like hey this is this is not hard it's like these are just brushes of like things like they're not I mean no right and then impressionism really started to pick up people like started to really understand the medium and then it evolved and continues to evolve and evolve right and you find similar moments in time where the pain to benefit becomes relevant and like photography for me was the very similar one right and then Cinema for sure and then the digital world the transition to like film and every single step of the way you have already experimenting with this technology and using them to put a perspective of the world I think right now what we're seeing right now with AI and and and there's also been I like to think of two AI Art Waves there was like the 2015 to 2022 where like the VQ gun and like the early gun experimenters and there was a lot of artists experiencing with it and then now the diffusion kind of like and the Transformers kind of like world has enabled a whole new wave of people to explain with it the first wave and now this particular way I think we're in the paint tubes kind of