# fal pivoted from Python infrastructure to image models Markdown copy of https://realalpha.app/s/fal-pivoted-from-python-infrastructure-to-image-models/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy. - Speaker: [Gorkem Yurtseven](https://realalpha.app/builders/gorkem-yurtseven/) · Co-Founder & CTO, fal · Co-founded fal and spent years optimizing its inference engine for generative media models. - Host: [Molly O'Shea](https://realalpha.app/people/molly-oshea/) · Founder, Sourcery · Ex-Upfront Ventures investor; has interviewed Alex Karp and Palmer Luckey. - Show: Sourcery - Episode: [Inside fal’s $1.65B Series E & F Led by Sequoia & Bessemer](https://realalpha.app/v/pPzJ1wE8Lnw/), Nov 3, 2026 - Moment: 13:22 to 17:56 of the episode (5 min), playing on https://realalpha.app/s/fal-pivoted-from-python-infrastructure-to-image-models/; the episode from there: https://realalpha.app/v/pPzJ1wE8Lnw/?t=802 ## Quote > “where the trend still holds is let's **focus on image models** because like there's something interesting happening there as well” > — [Gorkem Yurtseven](https://realalpha.app/builders/gorkem-yurtseven/) at [15:37](https://realalpha.app/v/pPzJ1wE8Lnw/?t=937) · Sourcery ## Takeaways 1. The original thesis was to bring Python workloads into the cloud. 2. They avoided competing in a race to sell language-model tokens cheaply. 3. Image-model revenue growth gave them a reason to pivot. ## About this segment The founders began with Python-focused data infrastructure, then shifted toward image models after ChatGPT and Stable Diffusion changed the market. Early revenue growth helped them commit to generative media before the category was established. From “Inside fal’s $1.65B Series E & F Led by Sequoia & Bessemer”, Nov 3, 2026. [Watch the full episode](https://realalpha.app/v/pPzJ1wE8Lnw/) ## Transcript Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there. [13:35](https://realalpha.app/v/pPzJ1wE8Lnw/?t=815) What was that evolution like for you, and how have you completely embraced this new category? Right, yeah. So we started in compute, but like a pre-AI era, let's call it, right? so we started in 2021 and the whole thesis was like okay there's you know this previous data infrastructure wave led to some amazing companies like snowflake databricks and what do they have in common they own compute you know so in order to build the next generation of like the data infrastructure company we also have to focus on compute that was the initial thesis and we thought the next big thing is going to be Python. So SQL, basically like what Snowflake did and what Databricks did to a certain extent was like enabling SQL to run on, you know, in the cloud and they own the compute and they let you scale the compute elastically. So we thought Python is [14:30](https://realalpha.app/v/pPzJ1wE8Lnw/?t=870) going to be the next thing because AI is the next big thing. So we ended up working with some really awesome teams like trying to bring their data science and machine learning old terms like workloads into the cloud so that was like our initial starting point and then soon after we started um some insane things happened in the market i think from a technology point of view like we had a massive breakthrough with chat gpt and in our space stable diffusion and and dali So we kind of had a choice at that point. [15:06](https://realalpha.app/v/pPzJ1wE8Lnw/?t=906) We can either stick to our data infrastructure initial idea, or we could go into this new world of AI. And there was language models. And at the time, I think, people were very excited about a bunch of open source models. If you remember, Lama came out shortly after, and there was this huge price race to the bottom. and we just thought okay we're like five six people right now we don't have that much money in the bank you know we're pretty early company and you know we can't really like go and you know sell tokens at a negative margin and let's do something more interesting you know the trend still holds uh where the trend still holds is let's focus on image models because like there's something interesting happening there as well there was no sight of video models like no one even talk We talked about it, I think, back then. But we thought that we could take image models and make them run a lot faster in the cloud on the same infrastructure that we built. [16:10](https://realalpha.app/v/pPzJ1wE8Lnw/?t=970) And so we did that. So we had a very technical team. We thought we could do a really good job running these models faster. I don't think we had a very formed thesis on where those models are going to go. So we kept doing it. and that got a lot of attention from like very early customers and we thought okay you know what like with this other business we ran it for a year we didn't make a single dollar with this one our revenue is starting to go like this there's clearly something here so we decided to focus on the image model space there and three to six months later roughly we actually realized that like this trend is probably much bigger and we thought that these models are um like useful for very different things than than what llms are helping with you know we thought it'll help with ads and marketing we thought it's going to be the buyers are going to be completely different [17:09](https://realalpha.app/v/pPzJ1wE8Lnw/?t=1029) so we decided to call it something else to like just be able to tell the story to people so we actually came up with this word generative media and we slapped it on our website and we said follow the generative media company and we always have to explain what it is because I think you know three years ago no one really understood what that meant so yeah that's that's kind of like how we got into it and how we sort of found our our like niche which you know three years down the road it's more well established and it's a whole like new industry so and people want to make feature films out of it. [17:45](https://realalpha.app/v/pPzJ1wE8Lnw/?t=1065) Exactly, yes, yes.