Taste is your Moat (Dylan Field of Figma)

**Alessio** (0:04)
Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio from the Kernel Labs, and so happy to be at the Figma office today with Dylan Field. Welcome.

**Dylan Field** (0:12)
Thank you. Thanks for having me on the podcast, and welcome to the Figma office.

**Alessio** (0:15)
Yeah. You know, we almost couldn't choose where to do this because there's so many beautiful spaces in it, but we finally had a with this corner. Super excited to have you on today. I was reading through some of the history of Figma, and your initial mission was to close the gap between imagination and reality. And if I heard that today, I would assume it would be the slogan of one of the VypCoding platforms. And so maybe talk about what was like the first, we should take AI seriously moment, where you were like, okay, imagination to reality in the first phase of Figma was like helping designers bring what they had in their mind into a canvas. And now with Figma Make, you're obviously moving to like a much broader audience. So what was the journey to get there?

**Dylan Field** (0:56)
Yeah, I mean, I think if you go back far enough, AI showed up in different forms for Figma. So I had the chance to be on the data science team at LinkedIn as an intern prior to working at Flipboard and getting more into design and then starting in Figma. And we were doing a lot of more classical machine learning approaches. And I was kind of absorbing that. And there's plenty of discussion about agents back then with my mentor, Pete Scumrock, and thinking through, okay, what might it look like if some of the ideas from the 90s were to resurface? And those were just kind of like fun, geeky conversations that are pretty abstract because obviously the world wasn't there yet. And then back at Brown with Evan, my co-founder and our original CTO, who's no longer at Figma but an absolute legend, I mean, just check out his GitHub if you're not convinced of that. He and I were talking a lot about some of the stuff we're starting to see as sort of ML and computational photography approaches to doing image editing, and what could be accomplished with that. So for example, there were papers being written about, how do you use internet scale data to complete scenes? And make it so you can basically do the equivalent of content-aware fill, but instead of doing it in an algorithmic deterministic way, how do you do that based on the entire internet? And we thought that was a pretty fascinating concept, and there's a professor at Brown who was doing some cool research in this area. We also were getting very excited in the early days of Figma before we even incorporated about stuff like, how do you turn a 2D image into a 3D scene? Some more computational photography, puts on blending, and some of these early techniques that you get 85% of the way there to something awesome, but not 100%. And it wasn't until we really had deep learning that it could get to 100%.
But all of these individual demos that we're able to work on, and by we, I mean mostly Evan, he's the real genius in the equation here. But as we started to explore a bunch of these areas, it just felt like there must be some way to make creation easier. And so that's why the vision was data's idea to reality, and not like idea to X as a subset of reality. Because we thought actually you could do this for a lot of different areas, and I still do. But we're starting with a data product. And fast forwarding to today, Figma Make, for example, we're really trying to make it so that you can go from idea in your head to actual ship product as fast as possible. And that might take the direction of an internal prototype to explore different ideas. It might be an internal app that you're using. I've been, this morning was some work on like random data munging that I was using to make for it, which is kind of fun. And rather than like write a Python script. And it's, I think, very exciting to think about how far you can help people go. And how you can make them both more productive, but also help them explore more of the option space of design with some of these techniques. And then of course, we're also excited about what that means in Figma design as well. How do you prompt to edit, prompt to degeneration, and do it in a way that's consistent with everything else that's in your design system, the patterns you're already using. And how do we actually infer from what's already inside of Figma what you want to do? And really be expansive in the way that we understand your intent.