Invest Like the Best with Patrick O'Shaughnessy
Gaurav Misra & Dwight Churchill - Building Captions - [Invest Like the Best, EP.405]
Tue, 07 Jan 2025
My guests today are Dwight Churchill and Gaurav Misra, 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 and what this means for building sustainable businesses. We also explore their unique data flywheel, why video generation could reach Hollywood quality within 18 months, and why building advanced AI products doesn't require huge teams. Please enjoy this discussion with Dwight and Gaurav. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Ramp is the fastest-growing FinTech company in history, and it’s backed by more of my favorite past guests (at least 16 of them!) than probably any other company I’m aware of. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. – This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Imagine completing your research five to ten times faster with search that delivers the most relevant results, helping you make high-conviction decisions with confidence. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. – This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:07:49) The Evolution and Impact of AI (00:09:14) Challenges in Video Data and AI (00:10:36) AI in Media Generation (00:12:07) Building a Sustainable AI Business (00:14:56) The Journey of a Video AI Company (00:25:41) AI Video Editing and Creation Tools (00:29:58) Future of AI in Video and Business (00:37:51) The Future of Likeness in Video (00:39:25) Training Models on Human Data (00:41:15) Competitive Landscape and Copycats (00:44:01) The Role of Research Talent (00:46:25) Pricing AI Software (00:51:51) Investor Perspectives on AI (01:02:44) Lessons from Snap (01:07:04) The Kindest Thing Anyone Has Done for Dwight & Gaurav
Chapter 1: What is the main topic of this episode?
Something I speak about frequently on Invest Like the Best is the idea of life's work. A more fun way to think about it is that I'm looking for maniacs on a mission. This is the basis for our investment firm, Positive Sum, and it's the reason why I'm so enthusiastic about our presenting sponsor, Ramp.
Not only are the founders, Kareem and Eric, life's work-level founders, certainly maniacs on a mission, they have created a product that is effectively an unlock for founders and finance team to do more of their life's work by streamlining financial operations, saving everyone their most precious resource, time. Ramp has built a command and control system for corporate cards and expense management.
You can issue cards, manage approvals, make vendor payments of all kinds, and even automate closing your books all in one place. Speaking from my own experience using Ramp for my business, the product is wildly intuitive, simplistic, and makes life so much easier that you'll feel bad for any company who hasn't yet made the switch.
The Ramp team is relentless, and the product continues to evolve to save you time that you would never have dreamed of getting back. To me, there is nothing more interesting than technologies that reduce friction for other entrepreneurs to be able to build the thing that they want to. So much attention has gone to cloud computing, APIs, and other ways of making life easy for founders.
What Ramp has done and is doing is build yet another set of tools in this category. To get started, go to ramp.com. Cards issued by Celtic Bank and Sutton Bank, member FDIC. Terms and conditions apply. As an investor, staying ahead of the game means having the right tools, and I want to share one that's become indispensable in my team's own research, AlphaSense.
It's the market intelligence platform trusted by 75% of the world's top hedge funds and 85% of the S&P 100 to make smarter, faster investment decisions. What sets AlphaSense apart is not just its AI-driven access to over 400 million premium sources like company filings, broker research, news, and trade journals, but also its unmatched private market insights.
With the recent acquisition of Tegas, AlphaSense now holds the world's premier library of over 150,000 proprietary expert transcripts from 24,000 public and private companies. Here's the kicker. 75% of all private market expert transcripts are on AlphaSense and 50% of VC firms on the Midas list conduct their expert calls through the platform.
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Chapter 2: How does AI impact content creation?
That's the kind of insight that helps you uncover opportunities, navigate complexity, and make high conviction decisions with speed and confidence. Ready to see what they can do for your investment research? Visit alphasense.com slash invest to get started. Trust me, it's a tool you won't want to work without.
Ridgeline gets me so excited because every investment professional knows the core challenge that they solve. You love the core work of investing, but operational complexities eat up valuable time and energy. That's where Ridgeline comes in. Ridgeline is an all-in-one operating system designed specifically for investment managers, and their momentum has been incredible.
Chapter 3: What are the challenges in video data and AI?
With about $350 billion now committed to the platform and a 60% increase in customers since just October, firms are flocking to Ridgeline for good reason. They've been leading the investment management tech industry in AI for over a year with 100% of their users opting into their AI capabilities, putting them light years ahead of other vendors thanks to their single source of data.
And they recently released the industry's first AI agents, digital coworkers that can operate independently. Their customers are already using this highly innovative technology and calling it mind-blowing. You don't have to put up with the juggling multiple legacy systems and spending endless quarter ends compiling reports.
Ridgeline has created a comprehensive cloud platform that handles everything in real time, from trading and portfolio management to compliance and client reporting. It's worth reaching out to Ridgeline to see what the experience can be like with a single platform. Visit RidgelineApps.com to schedule a demo. Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best.
Chapter 4: How do we build a sustainable AI business?
This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. Invest Like the Best is part of the Colossus family of podcasts, and you can access all our podcasts, including edited transcripts, show notes, and other resources to keep learning at joincolossus.com.
To learn more, visit psum.vc.
My guests today are Dwight Churchill and Gaurav Misra, 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 and what this means for building sustainable businesses.
We also explore their unique data flywheel, why video generation could reach Hollywood quality within 18 months, and why building advanced AI products doesn't require huge teams. Please enjoy this great discussion with Dwight and Gaurav. And a key side note, the first person you'll hear is Gaurav.
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 OK, great. What are we going to do with it and how can we build enduring generational businesses with this technology at the core? 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. 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, 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.
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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Chapter 5: What is the future of AI in video generation?
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.
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.
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.
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.
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.
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.
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?
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. I love the point that if you give a Hollywood studio or WADA 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 that could be achieved? I think it's pretty soon, honestly.
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Chapter 6: How does the pricing of AI software work?
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.
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.
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.
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.
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.
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Chapter 7: What insights are there on investor perspectives on AI?
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.
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.
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.
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.
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.
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.
If you think about the literal process of training, maybe you can explain it to people that are curious about how this actually works. So 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 many people like Asura focusing on what we'll call B-roll background video or just like a landscape of video. And your focus has been on A-roll, like a 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.
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 the literal actual training process of what's happening?
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Chapter 8: What are the lessons learned from Snap?
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.
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.
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.
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 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. 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.
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.
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.
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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