Welcome to the podcast. I'm your host, Jaden Schaefer. Today on the show, I want to talk about

FAL AI. They've just recently raised $140 million in a series D, and they've just come out with a brand new model. This was kind of their year-end surprise. What's incredible here is it's built on top of Flux 2, which is an open source model from Black Forest Labs. I've talked about them a lot. They were kind of the original image model that powered Grok over on X. What FAL has done that is making, you know, it's surprising

a lot of people is that they've been able to build something on top of that model to create images

that are 10 times cheaper and 6 times more efficient. I want to cover this today because I think this

is the direction that all of the AI companies, all the large AI hyperscalers, everyone is going to be moving in the direction of. And so I want to break down what they're doing, how they're doing, and what some of the innovation is. Before we get into that, I wanted to say a big thank you to today's sponsor, which is delve.com. If compliance is something that's slowing down your deals,

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delve. All right, let's talk about what's going on with Fal. So I think the, you know, obviously

they've just raised a whole bunch of money, $140 million. What's interesting here, they've just

unveiled a flex to-2 dev turbo.

This is a faster, cheaper, and more efficient version of the open weight model, which

was originally released by Black Forest Labs.

So this new model, it's already outperforming a lot of large competitors on public benchmarks.

It's available on Hugging Faces today, although there is a really

important caveat. It is distributed under a custom non-commercial license, which was originally

created by Bacforcedurelab. So Flex 2 Turbo is not a full standalone image model. I'll just

definitely put that out there in a traditional sense. Instead, it's what's called a Lora

adapter. This is a lightweight optimization layer that essentially attaches to the original

flex 2 base model, and then when they attach it, it dramatically improves the performance. So the

result is that you get these really high quality image generations, and they're all delivered

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