Dylan Field

speaker

577 appearances 1 recordings 1 series first heard Oct 2025 last heard 2 Oct

Dylan Field’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Oct 2025 with 1.

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Latent Space: The AI Engineer Podcast · Taste is your Moat (Dylan Field of Figma) · 2 Oct 2025 podcast

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%.

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