Taste is your Moat (Dylan Field of Figma)
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What is Figma’s original mission and how does it bridge imagination and reality?
Hey everyone, welcome to the Lid in Space Podcast. This is Alestio, founder of Kernel Labs, and so happy to be at the Figma office today with Dylan Field. Welcome. Thank you. Thanks for having me on the podcast and welcome
to the Figma office.
Yeah, you know, we almost couldn't choose where to do this because there's so many beautiful spaces in it, but we finally settled it 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 um, you know, 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 bycoding 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.
How did Dylan become ‘AI‑pilled’ and what early AI experiences shaped Figma?
And now with Figma Make, you're obviously moving to like a much broader audience. So what was the journey to get there?
Yeah, I mean, I think if you go back far enough, you know, 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 Figma. And We were doing, you know, 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 Scomrock. And thinking through, okay, what might it look like if some of the ideas from the 90s were to resurface? And, you know, those were just kind of like fun, geeky conversations that were pretty abstract because obviously the world was. Wasn't there yet.
And then back at Brown with Evan, my co-founder and 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 uh sort of ML and computational photography approaches to doing image editing and what could be accomplished with that. So, for example, there were Uh papers being written about how do you use internet scale data to complete scenes and make it so that you can basically do the equivalent of like 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 like 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 Been incorporated about stuff like how do you turn a 2D image into a 3D scene? Some more computational photography, you know, plus on blending and some of these early techniques that you kind of get like 85% of the way there to something awesome, but not a hundred percent. And it wasn't until you know we really had deep learning that you 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 it's the vision was stated as 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 an idea in your head to an actual ship product as fast as possible. And that might take the direction of an internal prototype to explore different ideas. It might uh be an internal app that you're using. Uh I've been as far Was uh some work on like random data munging, but I was using make for it, which is kind of fun. And rather than like write a Python script.
And uh 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.
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Chapters
8 chapters
1
What is Figma’s original mission and how does it bridge imagination and reality?
0:04–0:50
2
How did Dylan become ‘AI‑pilled’ and what early AI experiences shaped Figma?
0:50–8:00
3
What is Figma Make and how does it let designers go from idea to shipped product?
8:00–14:04
4
Why is language being treated as the interface for design in Figma?
14:04–23:25
5
How does Figma act as a context repository for aesthetic and design diffs?
23:25–33:05
6
What are the limitations of prompt‑based software creation and the ‘fast‑fashion’ analogy?
33:05–46:01
7
Why is design the key differentiator in today’s software landscape?
46:01–53:59
8
What lessons have been learned about early‑stage recruiting and building the Figma team?
53:59–1:01:32
Speakers
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