Gaurav Misra
speaker
155 appearances
1 recordings
1 series
first heard Jan 2025
last heard Jan 2025
Gaurav Misra’s voice in public audio — every appearance, attributed to the second.
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And people might even be willing to pay more for that type of guarantee or just like the reps that it's licensed. And then I think besides that, it really just comes down to like how much of the use case we'll be able to cover. And that's the big question. Okay, we're at 5% today, but is the limit 100%? Is it 75%? Is it 50%? Where does this stop?
My guess is we can go all the way to 100% or at least very close to just because it's a solved problem. We know that this is solvable. And I think if we can get there, I think a lot is going to change about how video workloads work in the world.
I actually think that that applies to the text side too. Even on text, we already have created... Essentially, what is a tool for intelligence? It's like intelligence in a box. Intelligence you can just apply onto something to solve a bounded problem. So whether that's coding now, think of it in the coding context. I think as Dwight was saying, engineers are smart people.
Does that mean we need AGI to solve coding? Not necessarily, because... Essentially, what it's doing really is just translating. Think of how computers evolved over time. We used to literally do the punch card thing. Then we were writing assembly language. Who knows that anymore? Then we were doing C++, right? Exactly.
Yeah. Then we were writing C++. And then there's these higher level languages like Python coming to the modern era.
perfect yeah and then we're kind of just saying like hey the new programming language is english that's not a crazy job it's actually a very bounded problem it's a problem of like inventing a new programming language essentially like a programming language that is even more understandable to people because they already know it it's a language that we already know intelligence is a special case
Exactly. Like the general intelligence idea of, oh, we're like creating consciousness. Oh, it's like a thing that's going to exist, go around, do things, like have its own thoughts and have its own like dreams and hopes and stuff. And maybe it'll start a company at some point.
That's a whole different mission than like solving intelligence in a box, which essentially already exists and it's getting better and better.
The way we think about it for our business specifically is that there is a bounded cost that actually solves this problem. That bounded cost is probably in the hundreds of millions of dollars, but it actually gets us to a solution. It gets us to something that, hey, this is actually reasonably good at generating anything that a CGI studio might be able to do.
And that is the level that we need to be at. Now, will that evolve? Yes, it will need to fine-tune it. But fine-tuning is... generally cheap. It's actually not even close to as expensive as like training a foundation model from scratch. And yeah, new data will come in, which we already have a flywheel we're building for. And it's going to be massive amounts of data.
We're going to be continuously training the model and making it aware of what's happening today and what things people might want to generate today. But that's just incremental fine tuning. It's going to be a low cost that's underlying the business. On top of that, inference costs are going down. So I think it's going to start looking more and more like a traditional software business.
I think what's going to happen is initially with these Tesla models existing, whoever truly solves this problem will have a moat for a while, as long as they are ahead. I think for us, we're also trying to build that data moat simultaneously so that we are permanently ahead. And then once enough data is out there, enough people have
raised enough money and have tried the exact same playbook and built these models. And this could be many, many, many years in the future. It's going to become a software race, building the workflows, building all the traditional stuff that we know about pricing and packaging, like all this stuff is going to become really important. We've seen it all.
People are going to do APIs, they're going to do like B2B consumer, all this stuff. There's going to be all these use cases. And I think that's where the real competition will happen. And there's going to be winners in that. I think our theory and strategy on this is the winners are going to be really determined by who has the best model that's consistently outperforming everybody else.
All that comes down to like data acquisition, flywheel, essentially, and the ability to constantly improve the model. I do think this won't be the end, though. I think new problems will get unlocked. And we already have line of sight into that, what those other problems look like. And those problems will have their own foundation models and their own data to be collected.
And essentially, you could imagine a series of foundation models that are solving like a family of problems across a whole set of a workflow that's broad across like video and maybe even other types of media, different types of use cases like film, TV, whatever you want, basically. Maybe it's dubbing, maybe it's... Hands. Yeah, hands, post-production, like, I don't know, right?
Lots of different possible use cases. So as always, that will happen. No doubt about that. You actually can see that these models will reach a point of maturity.
I mean, I think if we actually achieve that within a reasonable timeframe, I think that would be just the beginning. Because I think you could go so much beyond that. I think these industries are massive. Like you could imagine a social network based on something like this. You could imagine film and TV and stuff being dominated by these types of technologies.
You could imagine education being completely transformed. The list is essentially like endless. This would be the starting point of a potential complete transformation across like multiple industries. So I think today we're really excited about accomplishing this particular mission, but I think the possibilities beyond that are practically endless.
I mean, I think Snap had, as any company, a lot of good things and some bad things. I think the great things that I was able to get from Snap was the ability to work with a lot of great people. I think Snap was in a tough spot in many ways. They were in one of the most competitive possible businesses you can exist.
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