A Technical History of Generative Media
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What is the background and early history of Fal.ai and its founders?
Hey everyone, welcome to the Laden Space Podcast. This is Salesio, founder of Kernel Labs, and I'm joined by Swix, founder of Small AI. Hello,
hello. Today we're so excited to be in the studio with Gogan and Batuhan of Pho.
How did Fal.ai pivot from dbt pipelines to generative‑media inference?
Uh welcome. Yeah, thanks for having us. Long time listener, first time calling. Gorkham, you and I actually go back a long way, uh, to when it was still features and labels and you were just coming out of Amazon. I don't I don't even remember the pitch. I only said I should look at my own notes, but you were optimizing run times.
What technical innovations (CUDA kernels, inference engine) boost model performance?
Yeah, it was first like we we were building a future store and then we took a step back and then we decided to build a Python runtime in the cloud and that evolved into uh an inference system that evolved into what fall is today, which is a generative media platform. So we we optimize inference for image and video models and audio models, but we do a lot more. We try to on the whole generative media space for for developers basically.
Yeah,
amazing.
Why is latency so critical for user engagement with image and video generation?
And we can talk about that that journey. I wanted to also introduce Batuhan. We're newer to each other but you've you've come to uh some of my meetups before.
How have open‑source model releases (Stable Diffusion, Flux, SORA) shaped Fal.ai’s growth?
You're head of engineering. Yeah, I I lead
engineering here at Fall. You know. Glad glad to be here.
And what what's your journey?
I met Burkai in 2021, uh when they j were just starting a company and like just before the CD rant, you know, Burkai and Gyerkem we met online. We're all both Turkish. So I think that's that was a connection. We just met and then they said, Oh, why don't you join us? And like I was one of the core developers of Python language. So I I had like really, you know, really good experience with developer tools around the Python language.
What is the future of generative video models and VO‑3’s role in it?
So I started coming here to build the Python cloud, which evolved into this like inference engine. and the generative media cloud that we are building today.
And now you spend time less time with Python and more time with I don't know uh CUDA. Custom kernels and exactly. Custom kernels. Exactly.
Yeah, yeah. Yeah, I remember the DBT foul when the modern data stack was hot. Can you guys maybe just give a quick sense of the scale of foul? So you just raised a a hundred twenty five million dollar Chain. Uh we can talk about how I passed on one of your early rounds. Uh we can go through through that. How many of the developers, how many models do you serve, and maybe any other cool numbers?
Yeah, we have around two million developers on the platform and like we for for the longest time we required GitHub login. It recently changed, but so I'm assuming everyone who has a GitH account as a as a developer, um we have around three hundred fifty models in the platform.
How are advertising and startup marketing driving revenue for generative media?
These are mostly image video and audio models. It used to be only image and then we added audio. and uh the the space evolved into video as well. And yeah, that's that's pretty much the scale. We just closed announced R3C round and we we've been growing a lot in the past year and and it still continues.
Yeah, you had a very nice C party. Um And you guys are over a hundred million on revenue, right? Just this is not you know, just developers kinda kicking the tires. That's correct. Yeah. That's great. When you say three hundred and fifty models, I think what percentage of all the models that you could serve is then? Because uh you know, especially in
An infinite amount of fine tunes, post-trained versions of these models.
What hiring strategies and culture does Fal.ai use to stay ahead in AI infrastructure?
We are trying to serve the models that fix a gap, you know, that fill out gap in the stack. So we don't add a model that's like significantly worse in any aspect compared to other models that we have. We are trying to bring unique models that solve a customer's needs. So that's like these are three hundred and fifty models, you know. There's like twenty, thirty text image models, but like one of them excels in logo generation, another one excels. So like with every model has a unique personality, but if a model is like significantly worse in all aspects, we don't add that to the platform. So there's like infinite amount of models that we can add.
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Chapters
8 chapters
1
What is the background and early history of Fal.ai and its founders?
0:03–0:15
2
How did Fal.ai pivot from dbt pipelines to generative‑media inference?
0:15–0:34
3
What technical innovations (CUDA kernels, inference engine) boost model performance?
0:34–1:02
4
Why is latency so critical for user engagement with image and video generation?
1:02–1:08
5
How have open‑source model releases (Stable Diffusion, Flux, SORA) shaped Fal.ai’s growth?
1:08–1:36
6
What is the future of generative video models and VO‑3’s role in it?
1:36–2:29
7
How are advertising and startup marketing driving revenue for generative media?
2:29–3:11
8
What hiring strategies and culture does Fal.ai use to stay ahead in AI infrastructure?
3:11–1:00:57
Speakers
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