Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]

0:00 I know firsthand how complex the tech stack is for asset management firms. And seemingly every new tool and data source makes the problem even worse, adding more complexity, more headcount, and more risk. Ridge line offers a better way forward, one unified platform that automates away the complexity across portfolio accounting. Reconciliation, reporting, trading, compliance, and more, all at scale. Ridge line is revolutionizing investment management, helping ambitious firms scale faster.

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1:29 My guests today are Dwight Churchill and Garoth Mistra. Co founders of Captions, which uses AI to generate and edit talking videos and has grown to significant scale at remarkable speed. We explore a key distinction in AI. Tackling bounded problems like video generation versus unbounded problems like general intelligence.

1:48 and what this means for building sustainable businesses. We also explore their unique data flywheel, why video generation could reach Hollywood quality within eighteen months. And why building advanced AI products doesn't require huge teams. Please enjoy this great discussion with Dwight and Garov. And a key side note, the first person you'll hear is Garoth.

2:09 So guys, the topic on everyone's mind, I think, is this shift from AI as this incredible technology that's decided. Everyone understands how amazing this is. to okay, great, what are we gonna do with it? And how can we build enduring generational businesses with this technology at the core.

2:27 You were very early in building a business. that charged customers very early on using this technology. Maybe you can begin by just riffing on the lessons that you've learned so far. About building an AI business. that are maybe distinctive from a normal software business or something.

2:45 And also get into some of the open questions that you yourselves have. trying to evolve your business model. I just think this is becoming the important question. in the marketplace right now, and you're one of the earliest adopters. So you're the perfect people to answer. Getting into it, like I think the first question behind the question that comes to mind is like

3:03 What exactly Did we actually achieve with this AI revolution. Like what is actually the difference? Like AI existed before. And it exists today. Obviously there's something magical about what is there today.

3:16 I think when you get into it you realize that it's really about The ability to train larger and larger models. Yes, that's actually a combination of We have better Hardware to do it.

3:25 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 and

3:39 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.

3:57 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 gonna have to find what are those sustainable sources of data that can continue to grow bigger and bigger models.

4:14 And I think that's gonna 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

4:24 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.

4:36 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. One of the big things that we're thinking about is how do we actually create A flywheel where

4:49 We can ingest data. on a continuous basis and a growing basis, and that data can actually create bigger and bigger models for us from 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. I think if you look at a lot of the text generation companies

5:07 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 figure that out yet and Yes, we're achieving some levels of intelligence in these models.

5:21 And there's a long way to go. 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 possible.

5:32 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.

5:40 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. And video, for example, like

6:01 CGI exists. We can make Fake things. We can make fake. Humans we can make.

6:08 fake scenery and Dragons. And so This is a solved problem. We know that there's solutions to these and with AI we're actually just making it easier to solve these problems.

6:18 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 sets of technologies.

6:27 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

6:36 you kinda have to think about this unbounded problem of like, okay, we put in a bunch of capital into it. 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.

6:51 There may be like no end to this intelligence race. Whereas if you look at the media generation companies It actually was creating an asset. And there might be very soon a point where Oh wow. It's just really good.

7:02 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 assets really expensive to create.

7:11 But once it exists. It just generates value. And it doesn't lose value that easily. So What is gonna make those models better and better? I think it's gonna be like fine tuning with more data.

7:22 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 gonna be used in ads or movies or social media or something else. But there may be a point where it's like, wow, yeah, this is pretty good.

7:36 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

7:44 that level. What is it like to work with video data? Where I imagine like just the terabytes or petabytes or however you measure it of data that you have is sort of insane. How do you think about something that might just get As good as it can get.

7:59 I love the point that If you give a Hollywood studio or Waida or something enough money, they can literally create any visual that you can imagine. the friction between imagination and output is already gone. It's just really, really expensive. So really what you're doing is just making something cheaper. When do you think

8:15 That Could be Achieved. I think it's pretty soon, honestly. I mean at the rate at which video models are growing I mean, you probably remember seeing like the Will Smith spaghetti thing. Everyone's seen this meme. Right.

8:26 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.

8:36 I only say this because if you compare, for example, like tax models to like video models. Text models are already like in the four hundred billion parameter range. People understand better how to scale LLM technology today just because more money's been put into it, more time has been put into it, like diffusion models. Still in the tens of billions.

8:53 It's still early. Not even close. So As that grows, there's just no doubt it's gonna get better and better. And like the experts kinda know

9:02 That 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 is just

9:14 We're all getting there. 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

9:24 Something coming pretty close to like indistinguishable essentially from like a real recording. Maybe even sooner. That's not like a the worst case. Yeah, I don't think people are entirely Able to grasp that yet.

9:37 I think the way that that influences how they do their work every day. the workflows that end up getting reinvented, new paradigms of all that, which is arguably part design problem, part just product problem in general. This is

9:51 Pretty Close the timelines that Garov is talking about. People are experimenting today. It's extremely early, and I think that companies' adoptions and stuff around that.

10:00 We're not far off at all of really reinventing a lot of how people end up doing their everyday work. Can you describe the stages that you've gone through as a company? Maybe we'll use like the Tesla analogy. One of the beautiful things about their model is The cars, by virtue of being driven, are gathering data all the time. The product itself naturally generates data exhaust.

10:20 And I think you've had a somewhat similar story on the video side. And so you don't need YouTube or some massive proprietary library of video to do what you're doing. Can you just describe Take us back to the day one of the business, what it was to start, why you started there, and then how it's progressed since. 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

10:41 Connect the dots end to end. It's interesting. When we started the company, the first app that we made was Cashens. We launched it. And why did we make it? The goal was to get

10:51 content creators to create content on a video creation platform of some sort. Not easy. I was a Snap before this and Snap had tried this many times, they launched apps and I mean video is kind of a commodity.

11:03 Video editors or 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. Our thought was the way we're gonna crack this is we're gonna use AI to help create video somehow.

11:16 That's gonna be our differentiator. That's why people are gonna come to us. And so We saw that there was a need around Speech text. It was a technology, by the way, at that point that was pretty good.

11:27 In tech circles people were like course speech attacks, we understand that is 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

11:41 built the first product where it was just like, hey, it's just literally put text on the videos. And by the way, this was built in like two days on a weekend. really just band aid together. And we put it on the app store. Went to sleep.

11:53 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.

12:01 It blew up. And then woke up and I text White and I'm like, Hey I think there's like six hundred videos per minute being created on the app. By the way.

12:10 And so That was kind of like an instant success, but Even in that two days of work, we had already instrumentated the app in such a way that we would be able to continue training better and better models.

12:22 So that we can deliver better value to the user. So The idea was like The app Is an AI app.

12:29 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.

12:39 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. And we cover now the entire

12:50 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.

13:03 And there's data that can be collected across all of those that can improve those models. 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.

13:17 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.

13:29 And a lot of that comes through like video. So video being funneled directly into video generation models. That gives us significant advantage. That's potentially a possible way in which a future sort of business model could be set up. It actually is kinda familiar, by the way. Like it seems to me

13:46 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 B to B pay product. If you think about the literal process of training. Maybe you can explain it. to people that are curious about like how this actually works. So

14:04 You have raw video. A lot of it has voice in it. You can start it obviously by translating that voice into text. But let's say you're trying to train a model I like how you guys referred to

14:15 many people like a Sora focus on what we'll call B roll background video or just like a landscape of video. And your focus has been on A role, like the human being. on an iPhone looking video talking. How do you train a model where the output is a role like that? Like just imagine a portrait. video of someone reading an ad read or something like that.

14:36 That's indistinguishable from an actual live video taken on an iPhone. What is the literal training process? What is the target of the model as it's training? How similar or different is this to just next token prediction, what's the mental model for next X prediction or something in a video? Like how do you think about

14:53 the literal actual training process of what's happening. 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 some literal noise like static you see on TV.

15:07 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.

15:17 Says man wearing blue shirt. So it starts to like draw a little bit of man wearing blue shirt out of noise. And then every pass is taken through it. It's discovering A little bit more of the man wearing blue shirt.

15:28 So That's the text conditioning that's helping it decide how to reach the destination of what man wearing blue shirt looks like. So That's how the diffusion models work, which is slightly different from like how

15:40 A next token prediction model. Like GPT works. Which is kinda 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?

15:51 So these models are different. We are still earlier on in the diffusion model training. We're still And that Ten billion, twenty billion, thirty billion. Meta's movie gen was I believe thirty billion parameters.

16:03 People haven't really scaled us 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 gonna go into scaling up these things. Video obviously is really heavy.

16:16 That's what makes it different from text. Consumes a ton of space. A ton of processing. For us, even if we were to download Just download. All of our training videos.

16:26 It will cost us a million dollars to download the training videos. That's a whole different regime. And like text. It brings different types of challenges to Training these models, basically.

16:36 What does that mean in terms of the sap on resources that video models will represent relative to text models. Like one of the big discussions in public markets and private markets is How big do the GPU farms need to get? Are these video models to get to that point of perfection necessarily more consumptive of GPUs than text models would be? What's your two cents on this big question of

16:58 Do we need to build nukes next to data centers to train the perfect Lord of the Rings model or something? 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. Like the text problem is intelligence, as we're talking about. And The video problem is more rendering. We already know how much rendering

17:16 Costs. We already know. Yeah, it's GPU intensive. If you were to like literally CGI render is seen out, like yeah, it will spend some time with the GPU. There's no doubt. Can we be more efficient than that? It's possible.

17:27 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. I think if that's the case, then that's a good thing. But I think

17:37 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.

17:47 Just like on the training side. We're still scaling up these models and it's still oh it's ten billion parameters, twenty billion parameters, whatever. On the inference side, it's similar learnings happening simultaneously. We don't need to do a hundred steps of diffusion for inference, like a hundred denoising steps. to reach like a clear picture.

18:04 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 gonna get more and more efficient. It could be a factor of at least an order of magnitude, like ten X or something.

18:18 Can you talk about the felt experience of having a business? We won't quote how big the business is, but it's very big and it's grown ridiculously fast. One of the things you hear That's like a common idea now is that a new technology like this unlocks distribution. distribution used to be really expensive late in the mature last SaaS cycle or something. People sort of had their tools. But when tools are just ten X better or more, hundred X better.

18:41 distribution for time becomes really easy. I think you've been beneficiaries of that unlocking of distribution. Just talk about what that is like. What is it like to see revenue and users and all this stuff scale at this pace because It seems like the revenue growth rates of some of these AI application companies are faster than anything we've ever seen.

19:01 And I would just love you to riff on that a little bit and One describe what it was like, but also just reflect on anything that it teaches us. I mean 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.

19:18 And the next day it caused an impact, right? Like people cared. There's just nothing more exciting than that. 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

19:30 Set us up for success. But I think Maybe the most interesting part of it for me is how You can almost See how as you're expanding the use case. It's actually growing the potential market.

19:44 And that potential market has no competitors. 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 access. 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

20:02 Nothing other than just We are the only company That can do something. For a period of time. And

20:08 That will change. And I think that's why It's gonna be interesting to see as more and more use cases unlock, at some point all of it's gonna be unlocked. All if it's gonna be having competition in it. That would be a different time. Might be years from now. I don't know when it'll be.

20:21 But at least for now what we're seeing is this ability to expand use case. And by the way. We really think that. The use case unlocked so far is somewhere in the one to five percent range. We've barely scratch the surface of what's possible.

20:34 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. We don't even need to sell it.

20:45 And we're the only option. It makes just growth really fast. I think that's been probably the most exciting thing for me. Can you level set what the platform can do today? The major

20:54 use cases like everyone can imagine feeding it a video, getting a captioned video back. That's very simple. Can you lay out the other ones and give us a sense for their relative popularity? What is the revealed preference of how people want to use a platform like Captions? When you think about us, we actually divide the product in two areas. So there's the traditional video editing and video recording.

21:14 Which is just As you would expect it. It's A video editor and a video recording software. And this is Built for consumers.

21:23 Completely free. 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.

21:37 You actually don't need to spend all this time video editing. And recording. You can just generate it. So on the flip side of that, we offer Yeah I

21:46 Sweet, which is Two products. AI Creator. And AI edit. These are exactly mirroring recording. and editing.

21:53 Yeah, creator. Literally just makes videos of people talking. Whether that's you Or an actor that we've provided or anybody you might choose that you have the license to use.

22:03 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

22:16 These things are like very close revenue. And then there's AI edit, 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 A edit.

22:28 The purpose of that is Take a video in. and edit it for you. You actually don't have to worry about key frames and animation curves and

22:37 Timelines and All these concepts. Video editing is not easy. And a lot of people avoid it because they just don't want to deal with this complexity. And

22:45 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.

22:54 The traditional versus the AI. And in the AI we have Yeah, creator and add it. Just to clarify, like in something like edit, am I prompting it to say I want it to do this specific thing and then is it sort of like prompting That's where it will go in the future. Currently it's more style preferences that you provide to it.

23:11 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 gonna use that to funnel into our foundation model the ability to essentially prompt with text whatever you want to say. Say things like

23:27 I don't like these images. We want like different images with a better vibe, or let's cut it down to like thirty seconds, like forty five is too long. Or it sounds a little slow, we wanna tell the story a little faster pace. general Proms. Well you might actually say to an actual video editor.

23:42 And the type of thing that someone who doesn't have the detailed and intricate knowledge of video editing Might say. So what is like the relative breakdown of what tools people use? The free versus the paid and the editor versus creator? Like how does it shake out? Yeah, so today like a vast majority of our users are paid users. In between AI Creator and AI edit.

24:01 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. So Using both lets you basically get from

24:17 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. So they might actually like take a real video. and pass it to AI edit to be like, Okay, I already have a video.

24:35 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 out on their own. So they

24:45 Just to the AI creator part. A lot of times it's like using their own likeness. So they can just mass produce videos of different types. But it also can be like using one of her actors. Oh, that is marketing content and things like that. Things that go on social media.

24:59 But also like ads and anything that might be marketing related. So those are sort of the relative popularity. about equal. Does it feel like you're in

25:09 an arms race right now with other companies. 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 a Snap before this. Literally five other people have left Snap and tried to start the exact same company.

25:27 Yeah, it's worry. We should be doing that thing. It makes sense. I don't blame anybody. Like I think it's great that they're doing it. But I think what I Vike 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 kinda 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. It shouldn't be decided by what somebody else is doing because

25:53 They may not have a strategy at all. We don't know. Their strategy might just be looking at us. So a lot of hands 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?

26:07 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? Like 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. Like there's bugs in the NVIDIA drivers. There's bugs in the hardware level. Like

26:26 It's crazy. And we get a chance to like literally invent The UX, how are people going to interact with these things? Like I think people are not even thinking enough about this yet. They're just literally taking models and throwing it on UI and be like, press button. output. What if it was more interactive? What if you could see the steps of diffusion or you could like preview things in the middle of the diffusion process, change things according to like 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 gonna work. This is gonna get so much more evolved and so much more integrated and

27:00 That's all we focus on. I think the arms race is ensuring that we're delivering always way in front of what our customer even needs today. Whenever we releasing something It gets commercialized on day zero.

27:13 Immediately. We're not like testing it with a bunch of people and seeing what they need and seeing if we're actually solving anything. No. We're building this for their work. We're incredibly ingrained in how they do their work. Whether you're a large enterprise or all the way down to the free consumer. Ultimately, to Garo's point,

27:30 by inventing those design patterns and the way Someone can interact with these new models. We're literally paving the way for how people even think about doing their work. And that's the really exciting stuff. That is the arms race in my mind, but that's not necessarily against another company. What are the trade offs that you've had to choose one way or another as you build?

27:51 Video's a big category. That could mean I get to make a Lord of the Rings quality movie, or it could mean something much more provincial than that. We've actually niche 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.

28:09 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.

28:19 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

28:27 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. It's actually to create a role video. Telling the actual story.

28:42 Of whatever it is you're trying to convey. So Not just bunnies jumping around on Mars, type of thing. More like

28:49 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 power market fit.

28:58 We're actually the only company training a foundation model. to do this type of thing today to generate a role. 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 like how the space evolves in the future.

29:12 I think it will actually tend more towards what we're doing. What are the like surprising hard limitations of what the models I'm imagining

29:23 We're sitting at this table, there's a bunch of stuff on the table. My specific brand of water bottle or something. I wanna tell the thing to be able to like hold it like this certain way. And like I wanna be able to sort of direct an object that's not the person But that interacts with the person. Is something like that relatively straightforward? Yeah, I think that will happen.

29:41 Within six months. Guaranteed, essentially. We'll probably start seeing the first versions of this coming out within months of now. How does that work? Are you creating like a three D representation of this thing somehow? What are the steps that go into

29:54 The ability to create something like that. Do you have to find training videos where People are already interacting with objects. You drinking a cat 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

30:08 For example, it might be text conditioning. So if you can like adequately describe this particular can of coat. In text. That might be enough, but It may also not be right, like C water bottle.

30:18 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

30:27 You might imagine like image conditioning. She's a picture of a feed you water bottle. And then text that says Man in blue shirt holding Fishi water bottle. And then it'll be able to figure out the rest from there. Because it's seeing bottles in general, I don't understand what bottles look like. If it sees it from one angle, it can predict what it looks like from the other.

30:42 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 You can see the bottle from one angle. You could imagine a world in which we provide

30:54 multiple angles of the bottle, just to make it a little bit more accurate. 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.

31:05 This will be the first of what's gonna happen. How do you think the value of these things will change over time as the cost and frictions to create them falls? Humans are really good at scarcity and assigning value to scarce things. And so a beautiful video that shows a product was valuable because it's costly to create in some sense, probably.

31:25 How does the availability of perfect high fidelity Unbelievable quality video. At a moment's notice. How do you think that that changes? the value of the video itself. And I'm just curious if like other knock on effects of what you're doing that you've thought about.

31:40 I mean I think it's interesting one comparison you can kinda draw with this is if you think about the twenty ten's 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.

31:57 It looks awesome. Great designed websites just like one click away. This wasn't 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 nineteen nineties, No one's gonna buy anything from there. I think that's cool again, though. Yeah, it is cool now. Yeah. Which is crazy how fashion moves, right? Yeah. It all moves in cycles, yeah.

32:19 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. The video is more what's taking up 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.

32:39 Because the portion of creators within the video ecosystems will grow. More people will be creating it. And potentially even more people consuming it. So I actually think that the value of the video will not shrink exactly. It'll still be

32:53 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 gonna be other things about video that are gonna become more valuable. So for example, if you think about lightness.

33:08 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.

33:23 That sounds awesome. That sounds great. 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 is gonna be determined by

33:40 Who is known. A liken that is actually known by people, trusted, understood. Bye. Thousands.

33:47 hundreds of thousands of millions of people. Is valuable now. is much, much more valuable. All of a sudden. And by the way, that person may not have exist either. Someone might create

33:56 Uh Completely Fabricated person. Post videos and stuff, become famous. Yeah, little Michaela's way ahead of his time. Exactly, right. Shout out to Trevor McFaddy's way ahead of the times, yeah. So

34:07 That doesn't sound crazy in that world. I mean, I think you can go crazy with this stuff. What are the surprising limitations of these things? What would people be surprised that they have an especially hard time doing. We've all seen Video models struggle with

34:22 People. At the end of the day. Fingers. Yes. Fingers, arms. Drinking. Yeah. Well then, yeah. Spaghetti. Yeah.

34:31 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.

34:43 People. We also are gonna have conditioning The ability to provide like a skeleton, for example. This is the exact animation I wanna play out. This is the exact tick tock dance I want you to do, for example. It'll just make it happen.

34:56 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. Obviously, it's not that that's the training data that's like causing it. But

35:13 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 you know doing all kinds of things. So Our goal is to solve that human generation problem, like just actors essentially in general. The scarcity aspect too is that

35:28 Some of these are not new problems. The corollary around movies is that a Michael Bay film, two hundred and fifty million dollar budget or something like that blow up half LA transformers or something, I don't know. Tons of people go out and see it. Blockbuster film.

35:44 But all those people are paying twenty five dollars per ticket or something. The same thing happens for a low budget film if they can get into the box office. But the ticket price is the exact same. I'm actually very excited about A world in which lower budget filmmakers, uh and video creators in general can just create more and do more complex things with not necessarily the budget restraints. That's a massive hurdle for film creators and just creators in general.

36:08 I think it just up levels everyone. I think the craft maybe shifts a little bit or this and that, but those high budget films, as Grav mentioned, like Technically it's generated. It's not synthetic or it's not real. Some of those things are real, I realise, but It maybe even creates more premium on some of those aspects.

36:24 What does it feel like? In the competitive landscape. to have established something So successful and important. I love the idea that companies pass some level of maturity when someone else tries to kill them for the first time.

36:35 Have you had that experience yet? I'm really curious for like the sharper, rougher elbows part of building something so fast. Any experiences like that that are interesting? Definitely. I mean I think With all these types of things, we're always less go with our mission and like s 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

36:54 In terms of bigger companies. I think we're seeing an interesting evolution. Like we kind of fall in a 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 on watermark content.

37:11 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. And it was recycled TikTok, basically. But

37:22 We have a lot of that type of good content that's being generated on our platform, by the way, like hundreds of 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

37:35 social network. landscape evolve in that sense. 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 copy anything. They're just doing their own thing.

37:51 And the copying company actually is TikTok. Or by death more generally. I don't know how this shift exactly happened. Facebook suddenly became the good guys.

38:01 I think Mark Zuckerberg is a hero now. For putting all these models out, making all this open source stuff. Suddenly his vibe is completely shifted. And then I think TikTok

38:10 has become essentially what Facebook was. Capture, kill, destroy everything that exists in every market that exists. 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.

38:25 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. To their credit, they were the first to be aware of our existence of of anybody else. What does that look like them trying to kill you? Literally just copying the brain. They literally were to the extent of copying our app store description, our website.

38:45 Exactly putting that in the press release word for word. Copying our brand colors, exact, precise brand colors, pretending to be used. Beyond anything you would imagine. And just kinda crazy that like a company of that size would even try these types of tactics, but At the end of the day, like the software that they just create is just very mediocre.

39:04 And it just works because they have great distribution to TikTok. And I think we win because we just have better product. It seems like early days of all these models getting built that research talent was one of the most important scarce resources in extremely short supply.

39:20 Can you talk through? What you've learned about that, how it's changed. Is it still a handful of people that you really need a couple of them to be able to build the cutting edge thing? What is the role of extreme research talent in building the models that fuel all this great product? So the talent side is still

39:37 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 for shortage. Of talent talent isn't created overnight, right? Like it takes years and years of experience before Someone can be considered

39:59 Experienced in an area. And I think We will still see continued pressure on that talent site. Especially for like building generative models and foundation models and things like that.

40:10 Obviously the more V C dollars that are poured into this area. That'll have an effect. But I do think interestingly It doesn't take an army. to build this type of stuff. It takes a few good people.

40:22 And It might take an army to like scale it. And really make it big. But To deliver world class results.

40:30 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. 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.

40:49 It's all cutting edge. There's discoveries and inventions like happening every day, every week. 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?

41:05 For everybody else. So It still is a challenge. What our duty ends up being then is We have to give them all the resources and such to be able to do their work. There are a lot of

41:15 These folks you know in AI labs that Can't release anything they're working on. For better or worse. At least from the that place's opinion and When you're able to

41:24 Bring some of the ingredients that we have. Whether it be like compute, data. The environment. It ends up not being that complicated in terms of recruiting negotiation and stuff. How do you think these products will price over time?

41:38 This is like always the weird question with software where the marginal delivery of it costs nothing. Lot of people have been talking about Let's look at let's say Accenture's market cap or something like that. And it's a two hundred and fifty billion dollar company. Basically selling labor.

41:53 Very high expensive labor, important labor. Do you think that AI applications will take labor budgets and be priced like Heavily discounted labor. Because that's what they're doing and replacing, or is it just going to end up pricing like all software does? We've run these playbooks for 20 years and we kind of know how to do it.

42:10 What's your sense of how People should think about pricing AI software applications. And what its equilibrium state will be in the future. 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.

42:28 all the different aspects so we don't know what people want to be willing to pay for it yet. in the use case graph were like a four three, four or five percent, whatever, something in that range it's just early 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.

42:47 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. One of the big questions there is like how does that split between consumer and B to B? I think consumer pricing is evolving pretty clearly. I think we're starting to see

43:01 What that looks like. It seems like it's coming down to 'Cause your merch discretion. And it also seems like people are willing to pay a little bit more than they would have otherwise. 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

43:20 Seven ninety nine to twelve ninety nine 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 worth completely premium.

43:33 There's no free product. You cannot even use it once for free. And That worked just fine. People who were like Oh yeah.

43:40 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? Like moving on to the next one. I think the other thing we're starting to see is can we charge twenty five dollars 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.

44:01 They're going across this range too and people are paying all these prices. People are paying up to like Two thousand dollars a month. Consumer subscription. I think there's a lot more

44:10 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. Still might be like There's only maybe one or two models in the world that are like Of that quality and people care about that quality.

44:26 So You really don't have a lot of choice. Maybe if there's like a ton of these tested model floating around, maybe the price comes down in the future. So that's everything on the consumer side. And then

44:36 On the B to B 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 on licensed data?

44:47 That's like an open question. And yeah, they are, to an extent. We'll see kinda how all that plays out. We're planning to go much more on the fully licensed side. That's

44:58 uniquely positioned for that. We actually collect Data at like a massive scale. So we can actually train fully licensed models.

45:06 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

45:17 Will Factor in. Because you'll be able to win on that. Very easily. in like a competitive deal.

45:24 And people will care about those types of things. 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.

45:37 Okay, we're at five percent today, but is the limit A hundred percent. Is it seventy five percent? Is it fifty percent? Where does this stop? My guess is we can go all the way to a hundred percent, or at least very close to. Just because it's a solved problem. We know that this is solvable.

45:52 And I think if we can get there. I think a lot is gonna change. about how video workflows work in the world. And uh pricing around labor and hot topic right now is C licensing versus

46:03 Labor or like aligning to labor costs or something. I think People are maybe rushing into the labor argument that it actually has a very similar path or has had a very similar path. Turns out the CFO would like that number to go down. It's not some special number or something. And if you remove the human element to it, my guess is that

46:21 That probably only Put more downward pressure on it. It's like great, we can do more with less. And it's like perfect. Whatever the software is doing, if it's writing code or if it's the automated SDR, you know, whatever it might be, like there is downward pressure against those things. I think people are getting a little excited about running towards that and Don't get me wrong, pricing towards output and such is pretty cool.

46:39 I'm sure there is something there and there's some equilibrium we'll find. But I do think people are rushing to it maybe a little faster than they should be in that there actually might be more continue alpha in the typical subscription. Yeah, sure, maybe that's not the Salesforce seat, the classic comparable, but there's just some market exploration that needs to happen. And we probably haven't fully seen that yet. I think the entire world of investors

47:03 VCs, growth equity investors, public investors. Pretty much every single one of them is trying to figure out how to think about AI and its implications on their companies, prospective companies, equity valuations, all the normal important questions.

47:18 How would you advise them from the other side of the table? You've talked, I'm sure, to a lot of the great investors. You have several of them that have invested in your company. What do you think investors Understand? About AI? Well, or do they

47:32 They're not understanding as much detail as you do from the builder side. Give us your lay of the land of how you think investors are doing. Give him a grade or something. at understanding Yeah, if I have to grade it. Maybe from a public equity side, there are a lot of smart people out there, so I'm not gonna give them to our

47:48 I don't think it's ful being appreciated how much this is changing. like everyone saying that and all that, but I think there's continued talk of Oh, there's all this R and D spend or capital expenditure and it's like where's the value and stuff and I think there's just so much Attention on the

48:05 Large AI labs that are effectively what Gar was talking about before is that solving intelligence. That's a very, very different mission than the company who maybe is creating the automated software developer. Two very different worlds. And so

48:22 I think paying more attention to things outside of that is pretty important for to really understand. how this is changing inside of their companies. I think it would be hard to find a company today successful company today that hasn't and or isn't exploring AI tool of some sort.

48:39 to either completely replace an activity inside the company. Or quote unquote do more with less. in another capacity. I think that's true of every function of Essentially every successful company today.

48:51 And that's where you're seeing like a lot of the adoption. So even discussing the foundation model versus some of these companies who are just fine tuning a model as something open source or something. there's a ton of alpha in getting these tools inside of their company. So if you're talking to someone who's How do we do this and roll it out as a larger enterprise? I think there are already examples.

49:10 There are massive enterprises that you go in. Someone was telling me the other day that L'Oreal the beauty company, you can go in and they have like a internal GPT basically, uh internal L M of some sort. Any employee can ask any question. I don't know how much that's really being baked into their thoughts.

49:25 I think there's just so much attention. Towards these particular AI labs and the way that they're running their businesses. Which is extremely different than some of the other companies. And particularly if they have the backing of Microsoft or something. Yeah, it's inherently just being uh driven differently. And Yeah, I think if you were to go into those, I think your viewpoint would potentially change.

49:44 And that could definitely inform. a better in understanding on how this is actually gonna change work. I love this framing of the unbounded problem nature of intelligence versus the bounded problem nature of video or some of these other things. Kind of a fascinating bifurcation. I actually think that that applies to the tech side too. Even on text, we already have

50:04 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.

50:15 So Whether that's coding now. Think of it in the coding context. I think as Dwight was saying. Engineers are smart people. Does that mean we need AGI to solve coding? Not necessarily. Because

50:26 Essentially what it's doing really is just translating. Think of how like computers evolved over time. We used to literally do the punch card thing. Then we were writing assembly language. Who knows?

50:36 that anymore than we were doing C plus plus. Right. Just you. Yeah. Yeah. Then we were writing C and then there's these higher level languages like Python coming to the modern era. Scott from cognition was the guest today. So he's building the next layer. Perfect. Yeah. And then we're kind of just saying like hey The new program language is English. That's not a crazy job. It's actually a very bounded problem.

51:02 It's a problem of like inventing a new programmage, essentially, right? Like a programming language that is even more understandable to people because they already know it. It's the language that we already know. Intelligence is a special case. Exactly, right the general intelligence idea of oh, we're like creating consciousness. Oh, it's like a thing that's gonna 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. That's

51:26 A whole different mission. Then like solvens in bots, which essentially already exists and it's getting better and better. I'd love to just extend the analogy one step further to business model. Most of the commentary on AI businesses has been again focused on foundation model companies that have Well, have had two problems.

51:43 huge capex outlays to train the models. And then huge inference bills, so Often early on. really negative gross margins. Just to service their twenty dollar a month subscription product.

51:54 Inference has fallen a hundred X in cost in the last eighteen months or something crazy. These costs are going down. But those were the two criticisms of the business model was, Oh my God, this unbounded race, I gotta spend 10 X every time to build the next thing. When am I ever gonna make some money? It seems like this other category of more bounded problems.

52:12 Have pretty normal, great business models. Is that right? Is your sense that you guys are just gonna have really high gross margins like a normal software company and Yeah, you have to spend money training your foundation models, but it's not ten billion dollars and Walk me through the business model expectation, margins, capex.

52:28 things like that, what the J curve looks like in these businesses. Educate us a little bit on this second category. 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.

52:46 But it actually gets us to a solution. It gets us to something that Hey, this is actually reasonably good. and generating anything that a CGI studio might be able to do. And that is the level that we need to be at. Now Will that evolve?

53:00 Yes, it will need to fine tune it, but fine tuning is Generally cheap. Actually not even close to as expensive as like Training a foundation level from scratch. And

53:09 Yeah, new data will come in, which we already have a fly bill we're building for. And it's gonna be massive amounts of data. We're gonna be continuously training the model and making it aware of what's happening today and what's things people might wanna generate today. But that's just incremental fine tuning. That's gonna be a low cost that's underlying the business. On top of that, inference costs are going down. So I think it's gonna start looking more and more like a traditional software business. I think what's gonna happen is like initially.

53:33 With these test of models existing, whoever like truly solves this problem. We'll have a moat for a while. As long as they are ahead. I think for us, we're also trying to build that data mode simultaneously so that we are permanently ahead. And then.

53:47 Once Enough data is out there, enough people have 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 gonna become a software race, building the workflows, building all the traditional stuff that we know about. Pricing and packaging, like all this stuff is gonna become really important.

54:05 We've seen it all. People are gonna do APIs, then do like B to B consumer, all this stuff. There's gonna be all these use cases. And I think that's where the real competition will happen.

54:15 And there's gonna be winners of that. I think our theory and strategy on this is the winners are gonna be really determined by Who has the best model that's consistently outperforming everybody else? All that comes down to like data acquisition. Fly wheel, essentially, and the ability to constantly improve the model. I do think

54:34 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.

54:47 And essentially you could imagine A series of foundation models that are solving like a family of problems across A whole set Oh. 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. Post production, like I don't know, right? Lots of different possible use cases. So

55:09 As always, that will happen. No doubt about that. You actually can see that These models will reach a point of maturity. Yeah, I think on the like

55:19 What does this end up looking at a mature business end? call it some threshold. I genuinely believe that these can look like very high margin businesses. Whether that be the deflationary behavior of GPU and just compute in general, like It's incredibly early, like We're talking about the videos, latest trip, et cetera. You're already seeing the cost come down.

55:38 From on H one hundreds as the H two hundred architecture and stuff is being rolled out like throughout history, like these prices have never gone the other way. It's highly deflectionary as the next one rolled out because that ultimately that is their business model. They make them more efficient, they make them more powerful, whatever it is. So I think generally speaking that that is a hundred percent guaranteed. At least from my perspective. I think the Interesting thing though is that like when you're earlier stage and companies are earlier stage, and just talking about startups in general, is that

56:05 Higher margin businesses actually sound to me like perfect attack. vectors for another entrepreneur. And I think you should be very wary of companies that are operating at really high margins in the particular earlier stage. For these types of businesses, there's ton of margin expansion opportunities.

56:22 And then that also goes for the later stage companies though, too. I think you're seeing it right now, the CRMs and scan these, you know, operating eighty, ninety percent margins. It's great. Typical SaaS kind of stuff. They seem like great opportunities for companies to essentially go right after.

56:37 in uh reinventing some of the stuff. those companies don't have the same pricing power that they do that they did fifteen, twenty years ago. At the same time though, the great ones are thinking about that right now and reinventing themselves and so It does feel like a little bit of, you know, as we discuss some of these business model changes, I think it is a bit of a shifting ground. If you think about the future now.

56:57 What is on the other side? Uh The mission accomplished banner of You just did all video and you can create anything you can imagine in CGI with a hundred million dollar budget. Now you can do in captions. Then what?

57:10 What do you think you would do then? I mean, I think if we actually achieve that within a reasonable time frame, I think that would be just the beginning. Because I think you could go So much beyond that. I think these industries are a massive like you could imagine a social network based on something like this. You could imagine

57:27 Film and T V and stuff being dominated by these types of technologies. You could Imagine Education being completely transformed. The list essentially like handless. This would be the starting point of a potential complete transformation across like multiple industries.

57:42 So I think today we're really excited about accomplishing this particular mission. But I think the possibilities beyond that are

57:52 Practically endless. Any major lessons from your time at Snap, which strikes me as a very unique culture. And an extremely product. Centric, like a good place to train for product, maybe. What lessons do you take from your time there?

58:06 And what lessons do you leave behind? 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

58:19 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. Monopolistic by nature.

58:29 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

58:46 able to out of all the random noise See something and understand. Yes, this will work, and nobody will see it, but I know why it will work. And I think at the core of it was he had like an understanding of the product and the customer

59:02 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 gonna do this. And everybody was like, No, we shouldn't do that. Like, this is a bad idea.

59:14 And he'd be like, I don't care, 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

59:23 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 tick tock thing.

59:36 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 but like reposting that thing to other people who are not supposed to see it.

59:55 Everything was designed around Feeling good. Having fun. And share with friends. Which was

1:00:02 Really Everything. that people cared about at that time. And I think they kinda missed the TikTok thing because it was the exact opposite of that. It was actually Share to everybody. And

1:00:13 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. I think on the downsides of Snap.

1:00:27 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.

1:00:38 And once it exists. It can stay there. Despite the actions of the people. So I think what ends up happening sometimes in bad cases is like People. Think that the wrong actions that they're taking

1:00:53 Are the contrarian right view? Because Well the company is growing. So of course whatever I did was the right thing. But actually

1:01:02 The company is growing despite the wrong thing that was going on at the time. 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

1:01:23 Those actions. I think identifying those was a scale 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.

1:01:35 So one I'm really grateful for from that time is the ability to work with the CEO there. 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 was a small set of people, like ten to twelve people on that team. Even when the company was many thousands and thousands of people. Overall.

1:01:55 Cl. 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

1:02:07 This is a person who will fit in well and will be able to learn and figure these things out. Promised to him. Definitely doing something right. The closing question I ask everyone in this show, it's fun to get to do this twice today. What is the kindest thing that anyone's ever done for you? I mean it's hard for me to not say

1:02:23 The kindest thing is probably my wife and we started this company. We were already married. We had our first kid. Pretty hard not to call it that. It could have obviously not gone that way. I decide not to start the company, not to do a bunch of this stuff. And yeah, enable me to like take more risk and yeah. Yeah. No, I can't use that answer.

1:02:44 Yeah, I mean I think 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 US. Literally. Because like I was only here for like the first couple of years of my life. I was born while my dad was doing his PhD at North East when he was economics. So

1:03:04 He was like. four five years basically to that I was born in the middle of that. Then move back to India after that, but I have the US citizenship. Without that, I'd still be in India, yeah. Simple and powerful.

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