Gaurav Misra
Appearances
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It seems to me similar to the Facebook or Google business model where you have a mass consumer free product, basically, and the data is used to power essentially like a B2B paid product.
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It's interesting to think about because for the models that we train, they're diffusion models. So they actually work by starting from noise. It starts from literal noise, like static you see on TV. At every step, based on text that's provided, it looks at the noise and it tries to like predict a layer of clarity in that noise. It says man wearing blue shirt.
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So it starts to like draw a little bit of man wearing blue shirt out of noise. And then every pass it's taking through it, it's discovering a little bit more of the man wearing blue shirt. So that's the text conditioning that's helping it decide how to reach the destination of what man wearing blue shirt looks like.
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So that's how the diffusion models work, which is slightly different from like how a next token prediction model like GPT works, which is kind of just as you might think about it, just predicting the next word based on all the previous words that have been spoken, which are considered the context. So these models are different. We are still earlier on in the diffusion model training path.
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We're still in that 10 billion, 20 billion, 30 billion. Meta's movie gen was, I believe, 30 billion parameters. People haven't really scaled this up. We actually don't know how big OpenAI Sora is. They didn't, I think, release that information. But a lot of the work is going to go into scaling up these things. Video obviously is really heavy. That's what makes it different from text.
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Consumes a ton of space, a ton of processing. For us, even if we were to download, just download all of our training videos online, it will cost us a million dollars to download the training videos. That's a whole different regime than like text. It brings different types of challenges to training these models, basically.
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You never know. But honestly, I think what will save us on the video model side is actually the fact that it is an easier problem than the text problem. The text problem is intelligence, as we're talking about. And the video problem is more rendering. We already know how much rendering costs. We already know, yeah, it's GPU intensive.
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If you were to like literally CGI render a scene out, like, yeah, it will spend some time on the GPU. There's no doubt. Can we be more efficient than that? It's possible. It may not be the most efficient today. Maybe there's better ways of doing it. Maybe AI will be cheaper and faster than regular rendering. And I think if that's the case, then that's a good thing.
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But I think we know that it shouldn't be worse than that. We should be able to solve it with fewer resources than that, potentially, or at least the same. We generally understand where it's going to fall. It's still early. Just like on the training side, we're still scaling up these models and it's still, oh, it's 10 billion parameters, 20 billion parameters, whatever.
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On the inference side, it's similar learnings happening simultaneously. We don't need to do 100 steps of diffusion for inference, like 100 denoising steps to reach like a clear picture. We can distill models and have them work with a few steps of diffusion now. I think we're definitely the most inefficient we'll ever be. And it's only going to get more and more efficient.
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It could be a factor of at least an order of magnitude like 10x or something.
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I mean, it's definitely the most exciting that for anybody who's working on the engineering or product side, I feel like there's nothing more exciting than seeing direct results of I did a thing and the next day it caused an impact, right? Like people cared. There's just nothing more exciting than that.
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And I think we see that, which is great, which is why we've been able to build a great team and hire all this great talent, really set us up for success. But I think maybe the most interesting part of it for me is how you can almost think see how as you're expanding the use case, it's actually growing the potential market. And that potential market has no competitors.
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As you expand the use case, you kind of see we're doing ads now, or we're doing like higher quality video, even on that axis. And you see like entire new areas of market unlock where there's actually no competition. And actually, that's what causes the fast growth is actually nothing other than just we are the only company that can do And that will change.
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And I think that's why it's going to be interesting to see as more and more use cases unlock. At some point, all of it's going to be unlocked. All of it's going to be having competition in it. That'll be a different time. Might be years from now. I don't know when it'll be. But at least for now, what we're seeing is this ability to expand use case.
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And by the way, we really think that the use case unlocked so far is somewhere in the 1% to 5% range. We've barely scratched the surface of what's possible. As that grows, we see these entire new markets unlocked. Like, wow, this is a whole new set of people who can now do something actually useful with this. Yes, they're completely willing to pay. They're running to us.
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We don't even need to sell it. And we're the only option. It makes just growth really fast. I think that's been probably the most exciting thing for me.
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Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
When you think about us, we actually divide the product in two areas. So there's the traditional video editing and video recording, which is just as you would expect it. It's a video editor and a video recording software. And this is built for consumers, completely free.
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The play here for us is to provide a service to a large number of people, kind of a freemium business model in a way that they're already familiar with creating. But the goal is to actually upsell them into the AI use cases. You actually don't need to spend all this time video editing and recording. You can just generate it.
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So on the flip side of that, we offer the AI suite, which is two products, AI Creator and AI Edit. These are exactly mirroring recording and editing. AI Creator literally just makes videos of people talking, whether that's you or an actor that we provided or anybody you might choose that you have the license to use.
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We can make them say whatever you want, deliver whatever message you want, and we can even create people that don't exist. So in between that, you get a bunch of optionality of how you want your message to be delivered. A lot of the use cases like marketing and sales and these things are like very close to revenue. And then there's AI edit.
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Just a recorded video isn't exactly enough to create value. You want it to be edited in some way to tell a story that you want to tell. That's why we have AI edit. The purpose of that is take a video in and edit it for you. You actually don't have to worry about keyframes and animation curves and timelines and all these concepts. Video editing is not easy.
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And a lot of people avoid it because they just don't want to deal with this complexity. And our thesis is We have a foundation model that just does the editing for you. So you don't have to worry about actually editing anything. So that's the suite of products, basically. The traditional versus the AI. And in the AI, we have AI creator and AI edit.
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That's where it will go in the future. Currently, it's more style preferences that you provide to it. So it's in the early days of that. A lot of what will happen in the future is as we get more video editing data from our traditional products, we're going to use that to funnel into our foundation model the ability to essentially prompt with text whatever you want to say. Say things like,
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I don't like these images, we want like different images with a better vibe, or let's cut it down to like 30 seconds, like 45 is too long, or it sounds a little slow, we want to tell the story a little faster pace, general prompts, what you might actually say to an actual video editor.
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And the type of thing that someone who doesn't have the detail and intricate knowledge of video editing might say.
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Yeah, so today, like a vast majority of our users are paid users. In between AI creator and AI edit, they're both about equally popular. There's some people that just use AI Edit. There's some people that just use AI Creator, depending on the use case. And then there's a bunch of people who use both, one after the other.
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So using both lets you basically get from absolutely nothing to a fully edited video with just a couple of words typed, which is a great first-time experience. Now, some people might want to just record their own video, or they might be editing on somebody else's behalf or something like that.
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So they might actually like take a real video and pass it to AI edit to be like, okay, I already have a video, edit this for me. And the AI creator side, some people don't want the editing or they have very specific use case of what they're trying to do with it. They want to just figure it out on their own. So they just do the AI creator part. A lot of times it's like using their own likeness.
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So they can just mass produce videos of different types. But it also can be like using one of our actors. A lot of that is marketing content and things like that, things that go on social media, but also like ads and anything that might be marketing related. So those are sort of the relative popularities. I would say like they're about equal.
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To an extent, yeah. I mean, I think the most interesting thing that I've seen is a lot of new companies popping up. All of them are trying to do the same thing. Like I'll give you an example. I was at Snap before this. Literally five other people have left Snap and tried to start the exact same company Yeah, it's working. We should be doing that thing. It makes sense. I don't blame anybody.
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Like, I think it's great that they're doing it. But I think what I like about it kind of in a way the most people are copying us. I think it's like a great sign. It means that we're doing the right things and we kind of avoid looking at other companies too much. Our product strategy and what we build and what we do is really decided by our mission and vision and where we see the future being.
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It shouldn't be decided by what somebody else is doing because they may not have a strategy at all. We don't know. Their strategy might just be looking at us So a lot of times we'll look at competitors only to the extent of understanding, okay, this is what they're doing. What we really focus on is thinking about our North Star and where do we see the future being?
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And are we building towards that future? Not just from a technology perspective, from a product perspective and a user experience perspective. And I think that's the fun part, right? I think that's so much fun. When do we get a chance in history to actually invent the entire stack from the bottom to the top, all the way from the hardware level?
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Like there's bugs in the NVIDIA drivers, there's bugs in the hardware level, like It's crazy. And we get a chance to literally invent the UX. How are people going to interact with these things? I think people are not even thinking enough about this yet. They're just literally taking models and throwing it on UI and being like, press button, output. What if it was more interactive?
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What if you could see the steps of diffusion? Or you could preview things in the middle of the diffusion process, change things according to what you want it to generate. There's just so much that's still to be unlocked. Every function, whether it's design, learning about like how the technology works or technology people learning about like how the marketing is going to work.
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This is going to get so much more evolved and so much more integrated. And that's what we focus on.
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We've actually niched down quite a bit on purpose because as you said, like video is huge. It's like a massive market and it's almost too many problems to solve. I don't think if we tried to focus on everything, we would solve all of these things. So our focus is very much on videos oriented around communication. These are talking videos, people saying stuff.
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A lot of it tends to be marketing, sales, education. These are like the big categories or maybe communications to some extent. And it's about generating those types of videos. It's about editing those types of videos. But I think generating stock video is fine. I think that's a great thing to solve. But our goal isn't to create stock video.
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It's actually to create a real video telling the actual story. of whatever it is you're trying to convey. So not just bunnies jumping around on Mars type of thing, more like telling a story, pitching a product or whatever that might be. Something really communicative, informative. And that's where we've seen a lot of our product market fit.
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We're actually the only company training a foundation model to do this type of thing today, to generate A-roll. There's a couple of technical reasons why that's the case. There's other companies in the space, but they're not training foundation models. So we'll see how the space evolves in the future. I think it will actually tend more towards what we're doing.
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Guaranteed, essentially. We'll probably start seeing the first versions of this coming out within months of now.
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you have to find training videos where people are already interacting with objects, you drinking a can of Coke or whatever it might be. And then you have to be able to identify those objects and then provide them as conditioning. So for example, it might be text conditioning. So if you can like adequately describe this particular can of Coke in text,
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That might be enough, but it may also not be right. Like Fiji water bottle has a very particular design. Unless the model has seen one before, it may not be able to precisely recreate it. And text might not be enough to describe what it looks like. So you might imagine like image conditioning. Here's a picture of a Fiji water bottle. And then text that says...
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man in blue shirt holding Fiji water bottle. And then it'll be able to figure out the rest from there because it's seen bottles in general and understand what bottles look like. If it sees it from one angle, it can predict what it looks like from the other.
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So if you're like rotating it around and moving it around, it'll guess essentially what it probably looks like on the other sides, but it'll be pretty accurate because it can see the bottle from one angle. You could imagine a world in which we provide multiple angles of the bottle just to make it a little bit more accurate.
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Maybe there's something on the other side that isn't visible in one image that you want to make sure is like clear to the model. So those are the types of things that are just obvious. This will be the first of what's going to happen.
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I mean, I think it's interesting. One comparison you can kind of draw with this is if you think about the 2010s, generally, like it was a phase of design really taking off. Companies like Canva and Figma were created in this decade. And not just that, but there were a lot of like companies that were doing make a website with a few clicks. It looks awesome.
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Great design websites, just like one click away. This was an AI. There was a huge movement to just like If you want to sell something on the internet, if you want to have a business of any sort, you need a great design website. If your website looks like it's from the 1990s, no one's going to buy anything from there.
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Yeah, it is cool now.
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Which is crazy how fashion moves, right? It all moves in cycles. That's right. Yeah. There's almost nobody that has a bad website anymore. But that doesn't mean that having a good website is not valuable. It's still valuable. If you don't have a good one, then you might still suffer today, even though it's like commodity. Essentially, everybody should have it.
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The video is more worth taking out this decade. I think we'll see more and more people adopt it. It feels like there's a lot of people adopting it today, but I think it'll be even much larger than that because the portion of creators within the video ecosystems will grow. More people will be creating it and potentially even more people consuming it.
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So I actually think that the value of the video will not shrink exactly. It'll still be high quality video will be high quality video. And it'll be a requirement if you want to like market sell or whatever you're doing. But I do think that there's going to be other things about video that are going to become more valuable.
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So for example, if you think about likeness, if models can just generate likenesses of people that don't exist at a whim and they look like great people, people you would want to represent your brand, you could even own a likeness as an IP of your company of a person that doesn't exist and have them be the spokesperson of the company. That sounds awesome. That sounds great.
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But that means that the value of the likeness is just going to zero. The average likeness is not worth anything because anyone can make one out of nothing. And what does that mean for the cost of likenesses in general or on the high end? I think it's going to be determined by who's known.
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A likeness that is actually known by people, trusted, understood by thousands, hundreds, thousands, millions of people is valuable now. It's much, much more valuable all of a sudden. And by the way, that person may not have existed either. Someone might create a completely fabricated person, post videos and stuff, become famous,
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Exactly.
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Way ahead of the times. Yeah. So that doesn't sound crazy in that world.
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We've all seen video models struggle with people at the end of the day. Fingers. Yes. Fingers, arms.
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Yeah.
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Spaghetti. Yeah. Spaghetti.
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I think we're kind of taking the unique angle on this generally, which is that we are training specifically on people. Our data is all people. And we are specifically generating people. We also are going to have conditioning, the ability to provide like a skeleton, for example. This is the exact animation I want to play out. This is the exact TikTok dance I want you to do, for example.
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It'll just make it happen. And that actually makes the model much more likely and better to be able to learn what human anatomy looks like and what's normal and what's abnormal. People do have six fingers. It does happen. The model doesn't know that.
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Obviously, it's not that that's the training data that's causing it, but it may not fully realize that if not enough training data has been given to it that shows hands in all kinds of configurations and doing all kinds of things. So our goal is to solve that human generation problem, like just actors essentially in general.
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Definitely. I mean, I think... With all these types of things, we're always let's go with our mission and not worry about what others are doing. But yes, a lot of people care about what we're doing. In fact, I think I would say in terms of bigger companies, I think we're seeing an interesting evolution.
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We kind of fall in an interesting spot where we semi-collaborate with a lot of social networks because we're beneficial to their growth. We create content and all social networks need content. And we have un-watermarked content, content that is original. And this was a big problem for Instagram, if you remember, like when they launched Reels, everything had like a TikTok watermark on it.
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And it was recycled TikTok, basically. But we have a lot of that type of good content that's being generated on our platform, by the way, like hundreds and hundreds of thousands a day that's going to social media. And so we end up being a valuable partner for a lot of social networks. And we've seen like the social network landscape evolve in that sense.
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A lot of VCs ask the question like, what if Facebook copies you? What if Google copies you or something like that? And I think what we're starting to see is like Google and Facebook are not the copying companies anymore. They're not copying anything. They're just doing their own thing. And the copying company actually is TikTok or ByteDance more generally.
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I don't know how this shift exactly happened. Facebook suddenly became the good guys. I think Mark Zuckerberg is a hero now for putting all these models out, making all this open source stuff. Suddenly his vibe has completely shifted. And then I think TikTok has become essentially what Facebook was. Capture, kill, destroy everything that exists in every market that exists.
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don't collaborate with anybody. And I think it'll be interesting to see like how that plays out. Obviously, there's many talks happening about a ban and all this kind of thing. We'll see like where all that goes. But their leadership is very well aware of our existence. And they have tried many, many times to try to kill us.
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To their credit, they were the first to be aware of our existence of anybody else.
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They literally were to the extent of copying our app store description, our website, exactly putting that in their press release word for word, copying our brand colors, exact precise brand colors, pretending to be us beyond anything you would imagine. And just kind of crazy that like a company of that size would even try these types of tactics.
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But at the end of the day, like the software that they just create is just very mediocre. And it just works because they have great distribution through TikTok. And I think we win because we just have better product.
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So the talent side is still, I think, evolving. I don't think it's completely solved for what it's worth. As the use cases are growing, as more and more people are realizing what's possible, as more companies are getting started trying to solve similar problems, there's only going to be a more and more of a shortage of talent. Talent isn't created overnight, right?
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Like it takes years and years of experience before... someone can be considered experienced in an area. And I think we will still see continued pressure on that talent side, especially for like building generative models and foundation models and things like that. Obviously, the more VC dollars that are poured into this area, that'll have an effect.
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But I do think, interestingly, it doesn't take an army to build this type of stuff. It takes a few good people. And it might take an army to like scale it and really make it big, but to deliver world-class results can be done with maybe a dozen people or less. And you can beat everybody in the world with a team that small if you had the right ingredients in place.
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A lot of the challenge just becomes like finding those people. You can't get it wrong. You want a very specific set of people. You want a specific skill set. This is all new. So very few people have experience in it. It's all cutting edge. There's discoveries and inventions like happening every day, every week.
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So the more you care about it, you want to find people really close to the cutting edge who really know what's happening today and what are the small little wins and techniques that will get us the edge over everybody else. So it still is a challenge.
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I don't know if we completely understand it yet. Basically, like I think it's almost too early to tell in a way, because we aren't completely able to replace labor, all the different aspects. So we don't know what people will be willing to pay for it yet. In the use case graph, we're like a four, three, four or 5%, whatever, something in that range is just early.
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And we aren't able to like fully replace certain workflows or like very operationally heavy like processes that might exist in companies and stuff. And we will get there slowly and steadily. We're moving towards that. And I think we'll see what people will be willing to pay for that. So I think we'll figure it out.
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One of the big questions there is like, how does that split between consumer and B2B? I think consumer pricing is evolving pretty clearly. I think we're starting to see what that looks like. It seems like it's coming down to consumer subscription. And it also seems like people are willing to pay a little bit more than they would have otherwise.
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So for example, traditionally for like video related apps on the App Store, for example, web apps and Android, whatever, the standard price is somewhere in like the $7.99 to $12.99 range. That's just considered normal. And there's a freemium business model to it. I think what we've seen different is like for us, for example, we have been for a long time, like we were completely premium.
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There's no free product. You cannot even use it once for free. And that worked just fine. People were like, okay, whatever. Here's money. Let's move on. And so that wouldn't have worked in an older world without the newer technologies and stuff. People would have been like, yeah, I'm not paying for this, right? I'm moving on to the next one.
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I think the other thing we're starting to see is can we charge $25 a month? Yes, we can. People are paying that. And so people are clearly willing to pay much higher prices. Like if you look at a lot of different AI companies out there, the video generation companies and stuff, they're going across this range too. And people are paying all these prices.
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People are paying up to like $2,000 a month consumer subscription. I think there's a lot more ability to go higher on the subscription pricing than there was previously. Now that might change. I think a big factor of that might be like, there's just not enough competition still.
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Still might be like, there's only maybe one or two models in the world that are like off that quality and people care about that quality. So you really don't have a lot of choice. Maybe if there's like a ton of these Tesla models floating around, maybe the price comes down in the future. So that's what we're seeing on the consumer side.
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And then on the B2B side, I think that's where we will figure out a lot of it, right? I think some of the big things that need to be solved there is will businesses buy models that are trained on unlicensed data? That's like an open question. And yeah, they are to an extent. We'll see kind of how all that plays out. We're planning to go much more on the fully licensed side.
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That's going to be one of our main differentiators because we are uniquely positioned for that. We actually collect data at like a massive scale. So we can actually train fully licensed models.
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My feeling is that towards the end game, not today, but as this area gets very saturated, so maybe many years from now, I think things like having fully licensed models will factor in, because you'll be able to win on that very easily in like a competitive deal. and people will care about those types of things.
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And people might even be willing to pay more for that type of guarantee or just like the reps that it's licensed. And then I think besides that, it really just comes down to like how much of the use case we'll be able to cover. And that's the big question. Okay, we're at 5% today, but is the limit 100%? Is it 75%? Is it 50%? Where does this stop?
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My guess is we can go all the way to 100% or at least very close to just because it's a solved problem. We know that this is solvable. And I think if we can get there, I think a lot is going to change about how video workloads work in the world.
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I actually think that that applies to the text side too. Even on text, we already have created... Essentially, what is a tool for intelligence? It's like intelligence in a box. Intelligence you can just apply onto something to solve a bounded problem. So whether that's coding now, think of it in the coding context. I think as Dwight was saying, engineers are smart people.
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Does that mean we need AGI to solve coding? Not necessarily, because... Essentially, what it's doing really is just translating. Think of how computers evolved over time. We used to literally do the punch card thing. Then we were writing assembly language. Who knows that anymore? Then we were doing C++, right? Exactly.
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Yeah. Then we were writing C++. And then there's these higher level languages like Python coming to the modern era.
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perfect yeah and then we're kind of just saying like hey the new programming language is english that's not a crazy job it's actually a very bounded problem it's a problem of like inventing a new programming language essentially like a programming language that is even more understandable to people because they already know it it's a language that we already know intelligence is a special case
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Exactly. Like the general intelligence idea of, oh, we're like creating consciousness. Oh, it's like a thing that's going to exist, go around, do things, like have its own thoughts and have its own like dreams and hopes and stuff. And maybe it'll start a company at some point.
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That's a whole different mission than like solving intelligence in a box, which essentially already exists and it's getting better and better.
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The way we think about it for our business specifically is that there is a bounded cost that actually solves this problem. That bounded cost is probably in the hundreds of millions of dollars, but it actually gets us to a solution. It gets us to something that, hey, this is actually reasonably good at generating anything that a CGI studio might be able to do.
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And that is the level that we need to be at. Now, will that evolve? Yes, it will need to fine-tune it. But fine-tuning is... generally cheap. It's actually not even close to as expensive as like training a foundation model from scratch. And yeah, new data will come in, which we already have a flywheel we're building for. And it's going to be massive amounts of data.
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We're going to be continuously training the model and making it aware of what's happening today and what things people might want to generate today. But that's just incremental fine tuning. It's going to be a low cost that's underlying the business. On top of that, inference costs are going down. So I think it's going to start looking more and more like a traditional software business.
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I think what's going to happen is initially with these Tesla models existing, whoever truly solves this problem will have a moat for a while, as long as they are ahead. I think for us, we're also trying to build that data moat simultaneously so that we are permanently ahead. And then once enough data is out there, enough people have
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raised enough money and have tried the exact same playbook and built these models. And this could be many, many, many years in the future. It's going to become a software race, building the workflows, building all the traditional stuff that we know about pricing and packaging, like all this stuff is going to become really important. We've seen it all.
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People are going to do APIs, they're going to do like B2B consumer, all this stuff. There's going to be all these use cases. And I think that's where the real competition will happen. And there's going to be winners in that. I think our theory and strategy on this is the winners are going to be really determined by who has the best model that's consistently outperforming everybody else.
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All that comes down to like data acquisition, flywheel, essentially, and the ability to constantly improve the model. I do think this won't be the end, though. I think new problems will get unlocked. And we already have line of sight into that, what those other problems look like. And those problems will have their own foundation models and their own data to be collected.
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And essentially, you could imagine a series of foundation models that are solving like a family of problems across a whole set of a workflow that's broad across like video and maybe even other types of media, different types of use cases like film, TV, whatever you want, basically. Maybe it's dubbing, maybe it's... Hands. Yeah, hands, post-production, like, I don't know, right?
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Lots of different possible use cases. So as always, that will happen. No doubt about that. You actually can see that these models will reach a point of maturity.
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I mean, I think if we actually achieve that within a reasonable timeframe, I think that would be just the beginning. Because I think you could go so much beyond that. I think these industries are massive. Like you could imagine a social network based on something like this. You could imagine film and TV and stuff being dominated by these types of technologies.
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Getting into it, I think the first question behind the question that comes to mind is what exactly did we actually achieve with this AI revolution? What is actually the difference? AI existed before and it exists today. Obviously, there's something magical about what is there today. I think when you get into it, you realize that it's really about the ability to train larger and larger models.
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You could imagine education being completely transformed. The list is essentially like endless. This would be the starting point of a potential complete transformation across like multiple industries. So I think today we're really excited about accomplishing this particular mission, but I think the possibilities beyond that are practically endless.
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I mean, I think Snap had, as any company, a lot of good things and some bad things. I think the great things that I was able to get from Snap was the ability to work with a lot of great people. I think Snap was in a tough spot in many ways. They were in one of the most competitive possible businesses you can exist.
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monopolistic by nature, where it's really difficult to get something started and very easy to get killed. And only the biggest one actually wins and survives. And in that arena, they were able to make a place for themselves, mainly because of innovation. And this is just comes down to the CEO, he was able to out of all the random noise, see something and understand, yes, this will work.
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And nobody will see it. But I know why it will work. And I think at the core of it was he had an understanding of the product and the customer in a way that nobody did. And nobody even came close to it. There were many moments in the company's history where he was like, we're going to do this. And everybody was like, no, we shouldn't do that. This is a bad idea. And he'd be like, I don't care.
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We're doing it. We did it. And it was the best thing we ever did. That's the level to which his intuition was there. Snap was famous almost for like constantly innovating. Stories came out of Snap. The old Maps location sharing product and idea came out of there. There's so many things that we're innovating on. I think they kind of lost a little bit on sort of the public TikTok thing.
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But that actually was something that didn't fit in their like pillar vision strategy, basically, because they're a private sharing platform. Their whole purpose was low abuse. People don't feel like you can't even reshare posts because that's a way to like embarrass somebody by like reposting that thing to other people who are not supposed to see it.
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Everything was designed around feeling good, having fun and sharing with friends, which was really everything that people cared about at that time. And I think they kind of missed the TikTok thing because it was the exact opposite of that. It was actually shared to everybody.
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And interestingly, it created similar dynamics where like sharing to everybody actually made you feel more private because there were so many people that people you know would never see it. Somebody else would see it. But that's the details.
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I think on the downsides of Snap, one of the interesting things there is, and one of the learnings there is product market fit often doesn't have a lot to do with what people are doing day to day within the company. And once it exists, it can stay there despite the actions of the people.
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So I think what ends up happening sometimes in bad cases is like people think that the wrong actions that they're taking are the contrarian right view because, well, the company is growing. So, of course, whatever I did was the right thing. But actually, the company is growing despite the wrong thing that was going on at the time.
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And so it's difficult to tell what actually is causing the company to grow and what's the good thing and what's the bad thing. And a lot of people walk away from these types of high product market companies thinking that all the things they did were good things and there were no bad things because the company grew. But the reality is the company is growing despite those actions.
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Yes, that's actually a combination of we have better hardware to do it. We have better ML architectures, like there's transformers, there's diffusion models, there's all these new types of architectural unlocks that we've created. And then there's other techniques that we've created too, which just allow us to train larger and larger models.
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I think identifying those was a skill that I had to like really work on building to understand like how can we truly measure what we're launching, what we're building and understand what's a good thing and what's a bad thing. So what I'm really grateful for from that time is the ability to work with the CEO there.
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He really brought me into the circle, like he had a great design team that he had built. A lot of the decision making was driven through the design team. It was a small set of people, like 10 to 12 people on that team, even when the company was many thousands and thousands of people overall post IPO.
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So being a part of that team, learning from the great people on that team, I evolved my design career through this process. And I think his ability to like identify this is a person who will fit in well and will be able to learn and figure these things out. Props to him. Definitely doing something right.
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Now I can't use that answer.
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Yeah, I mean, I think... It's a little unfair. Besides that, I would say... Likewise, by the way, for me. If I were to give you a different answer, I think it would be just my parents making sure that I was born in the U.S., literally. Because, like, I was only here for, like, the first couple of years of my life.
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I was born when my dad was doing his PhD at Northeastern University of Science and Economics. So he was there for, like, four or five years, basically, until then I was born in the middle of that. Then moved back to India after that. But I had the U.S. citizenship. Without that, I'd still be in India. Yeah. Yeah.
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And turns out the larger and larger you make these models, the more problems they can solve, the better they can be at solving like text generation or like towards AGI or video generation or media generation in general. I think when you realize that, what actually you get to is that what really matters is the data at the end of the day.
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A lot of companies are like scraping the internet and the internet is also limited in some ways. There's only so much information on the internet even, and that's growing every day. But I think at the end of the day, beyond that, we're going to have to find what are those sustainable sources of data that can continue to grow bigger and bigger models.
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And I think that's going to be the fundamental question behind who actually ends up winning in a lot of these different areas that AI is excelling in today. I think for us, being on the video generation, video editing side, it comes down to like video data, which is actually much heavier, much rarer to find, not as common as text or even audio.
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and potentially much more expensive to train on as well, much more limited in terms of being created in the world. And so that tends to be like a big challenge.
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One of the big things that we're thinking about is how do we actually create a flywheel where we can ingest data on a continuous basis and a growing basis, and that data can actually create bigger and bigger models for us and keep us at the forefront. I also want to call out here, like there's a pretty fundamental difference between different types of AI companies that are out there.
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I think if you look at a lot of the text generation companies, they're not solving text generation. Like we don't call it text generation. They're actually kind of solving a totally different problem, which is intelligence. Intelligence is an unsolved problem. No one's figured that out yet. And yes, we're achieving some levels of intelligence in these models. And there's a long way to go.
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It may not end at human intelligence. There's people in the world who are really smart. There's people in the world who are not so smart. They both exist. And clearly, so there's a range of intelligence as possible. There's not one value for like you're intelligent or not. So yeah, is there a chance that there's the ability to go smarter than the smartest human? It's possible.
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But that's a frontier that we've never reached. And so it's kind of solving this unsolved problem. But I think if you think about audio generation or video generation or music generation or these types of things, right, it's I think a little bit less of solving an unbounded intelligence problem and a little bit more of solving actually rendering a solved problem.
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And video, for example, like CGI exists. We can make fake things. We can make fake humans. We can make fake sceneries and dragons. And so this is a solved problem. We know that there are solutions to these. And with AI, we're actually just making it easier to solve these problems.
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Not just a little bit, but like a hundred times easier, which in the end, that means more accessible, larger market, more people can use these types of technologies.
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So I think that's one of the fundamental differences there is if you look at business models for like artificial intelligence companies that are really working on AGI, then you kind of have to think about this unbounded problem of like, okay, we put in a bunch of capital into it.
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We create a model only for that model to be beat by the next model and that model becoming essentially useless and obsolete. And then there's the next model after that. And how long does this go on for? Actually, we don't know. It may go on forever. There may be like no end to this intelligence race. Whereas if you look at the media generation companies, it actually is creating an asset.
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And there might be very soon a point where, oh, wow, it's just really good. It's just perfect or close to perfect. And we've kind of solved it. And then it's an asset. And then after that, it's just software company. And the asset's really expensive to create. But once it exists, it just generates value. And it doesn't lose value that easily.
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So what is going to make those models better and better? I think it's going to be like fine tuning with more data, fine tuning for specific use cases, different types of things you want to generate, different types of visuals, whatever it might be. Use cases like, oh, it's going to be used in ads or movies or social media or something else.
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But there may be a point where it's like, wow, yeah, this is pretty good. It's realistic. I think that's a pretty important thing we're thinking about right now. How do we bootstrap that data flywheel to be able to reach that level?
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I mean, at the rate at which video models are going, I mean, you probably remember seeing like the Will Smith spaghetti thing. Everyone's seen this meme, right? And it went from like really horrible to like, wow, this is actually good. And I think really, really good is probably around a year, year and a half away.
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I only say this because if you compare, for example, like text models to like video models, text models are already like in the 400 billion parameter range. People understand better how to scale LLM technology today just because more money has been put into it, more time has been put into it. Like diffusion models, still in the tens of billions. It's still early, not even close to the text models.
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So as that grows, there's just no doubt it's going to get better and better. And like the experts kind of know that this is all possible. It's just that very few companies in the world have the funding and the expertise to actually go after this. So like it just takes some time. Like it's not like some unsolved problem. People know what needs to be done. It's just we're all getting there.
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We're all moving towards it. And we'll see those models getting better and better, especially on the video side. I could easily see within a year and a half or so something coming pretty close to like indistinguishable, essentially from like a real recording, maybe even sooner. That's not like the worst case.
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It has been a pretty interesting journey and like we've been through some interesting twists and turns through it. But I think if you like connect the dots end to end, it's interesting. When we started the company, the first app that we made was captions. We launched it. And why did we make it? The goal was to get content creators to create content on a video creation platform of some sort.
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Not easy. I was at Snap before this and Snap had tried this many times. They launched apps and I mean, video is kind of a commodity. Video editors are commodities. A lot of these companies are actually foreign. And that's because we're just trying to minimize costs at this point and really difficult to compete in.
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Our thought was the way we're going to crack this is we're going to use AI to help create video somehow. That's going to be our differentiator. That's why people are going to come to us. And so we saw that there was a need around speech to text. It was a technology, by the way, at that point, that was pretty good.
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Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
In text circles, people were like, of course, speech to text, we understand that it's pretty good at this point. But I think the average person actually didn't understand how good the tech had gotten and how accurate it was with names and like obscure terminology and all kinds of stuff. So when we built the first product where it was just like, hey, it's just literally put text on the videos.
Invest Like the Best with Patrick O'Shaughnessy
Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
And by the way, this was built in like two days on a weekend, really just bandaid together. And we put it on the app store, went to sleep. The next morning it was top of the app store. There's no explanation. We didn't do anything to make that happen. Somebody saw it. They posted it on something. It blew up and then woke up and I text Dwight.
Invest Like the Best with Patrick O'Shaughnessy
Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
I'm like, hey, I think there's like 600 videos per minute being created on the app, by the way. And so that was kind of like an instant success. But even in that two days of work, we had already instrumented the app in such a way that we would be able to continue training better and better models so that we can deliver better value to the user.
Invest Like the Best with Patrick O'Shaughnessy
Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
So the idea was like, the app is an AI app where people come in, they use the app, we use the data to make the model better and deliver even better experiences the next time the person comes back. That was done from day one, literally. That was the original plan. Now, post the launch of the app, we've added so many more features over time, expanded the offering so much more.
Invest Like the Best with Patrick O'Shaughnessy
Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
And we cover now the entire space of everything from like script writing to recording to video editing, distribution as well. and how AI can like transform each of these different areas, because there's applications in all of them. And there's data that can be collected across all of those that can improve those models.
Invest Like the Best with Patrick O'Shaughnessy
Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
And that's what makes our offering really unique, because all the other companies are not really thinking about the data collection side and just generating outputs. And that's why they have to kind of scrape the internet to make their models better. And for us, really, it's more about growing a user base so that the data can actually power better and better models.
Invest Like the Best with Patrick O'Shaughnessy
Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
And a lot of that comes through like video. So video being funneled directly into video generation models. That gives a significant advantage. That's potentially a possible way in which a future sort of business model could be set up. It actually is kind of familiar, by the way.