Taste is your moat — with Dylan Field, FigmaLetting designers build with Figma Make, how Figma can be the context repository for aesthetic in the age of vibe coding, and why design is your only differentiator now
 There’s nothing as exciting and scary as a blank page. Anything is possible, but where to begin? In the age of AI, the kickstarting process is more and more being delegated to a prompt. “Design an app to do X” or “Write me a blog post on Y”. This is the root of all slop. In our Greg Brockman episode we talked about the tendency that LLMs have to create purple and blue gradients due to their training data. Today’s guest is the man leading the company that might help us fix this: @zoink aka Dylan Field, CEO of Figma. Figma as a taste repositoryWe have often talked about the software engineering triad of specs, test, and code. After my discussion with Dylan, I have adapted this to the design world through levels of composability instead: the full design, the individual components, and the underlying design system. Each of the three has a corresponding product slice:
MCPs as the design <> code handoff“Figma to Code” was its own category of early stage startups for a bit. Once vibe coding started to rise, there was also a lot of chatter about design being the new bottleneck. Figma is now the Figma to Code company, and they are leaning into that. I have three MCP servers always on: the Figma server, Sentry, and Linear. Once you have done the work above, you can just ask your coding agent to implement the design changes.
We also had a great discussion about design and the future of software, what he learned from the Thiel Fellowship, and how he first got AI-pilled. Enjoy! Show NotesTimestamps
TranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and so happy to be at the Figma office today with Dylan Field. Welcome. Dylan: Thank you. Thanks for having me on the podcast and welcome to the Figma office. Alessio: 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 way 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, 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 vibe coding 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? Becoming AI-PilledDylan [00:00:56]: 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 gave more into design and the story in 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 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 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 even incorporated. Like, how do you turn a 2D image into a 3D scene? So 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 100%. 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... 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 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 reality. And so 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 reality. 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 supporting 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 profitable. But also help them explore more of the options 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 do generation and do it in a way that’s consistent with everything else that’s in your design system, the patterns you’re already using? Alessio [00:04:22]: 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 do it? Understand your intent. So you have a background in obviously math and CS and now you run Figma. So you have this kind of like duality of like aesthetics and code. Were you first AI-pilled by the image generation, kind of like more creative things? I think early on in the podcast, most people would say Midjourney was their favorite AI product. And another half of people would say GitHub Copilot. What was your first product that you fell in love with with AI? Dylan [00:04:56]: Not a product, but my first like AI-pilled moment was, I think it was like 2014. 14 or so, maybe a little bit earlier. I was a Teal Fellow. And in my class was a number of amazing people, but one of which was Chris Ola. And Chris and I would be going to these retreats together for the Teal Fellowship every, you know, three to six months. And I remember one of them, Chris had been working on some cool like Haskell 3D generation stuff. And it was all a bit like out there and not clear how it would be. And I remember him sitting down with me. We were at like a wooden table outside in some like Santa Cruz, you know, nature setting. And he’s on the Wi-Fi, which is super slow, connecting to AWS. And he’s like, look at this. I can go on AWS and I can spin this up and I can train this like tiny neural net to classify. And I was like, oh, my God. I’m like, Chris, this is like a solved computer vision problem. Like, why are you excited? And he’s like, no, you don’t get it. It’s a neural net. And there’s like hyperparameters I can tweak. And I think I can actually make, you know, another neural net to like figure out how to tweak the hyperparameters. And I’m like, oh, that’s all great. But like, this is a solved problem. And I lacked the vision at that point to see where it was going. And but it started to get me to pay more attention. And then watching his work when he was at Google. Some of the great blog posts he was doing, as well as starting to listen in on more of people around me and the conversations that were happening around AI and machine learning, got me more and more excited about where this might go. But I don’t think I truly internalized scaling laws for quite a while longer and what that could mean. But I think GPT-3 was probably the first time that I was like, wow, the delts between this and past models is so great. Something. Exponential is definitely happening here. It’s not just like hype. And then, you know, plenty of conversations around that time with other AI figures that we both know well definitely started to make me think, okay, there’s something very important to focus on here. But I think it’s very different to be in a context of, you know, more deterministic software building than AI research. There are completely different motions of how you kind of run those teams. And so it definitely took us a lot longer than, you know, starting at, okay, GPT-3, amazing, to get to the point where we’re starting to ramp up and push the boundaries of what might be possible at Figma. Figma MakeAlessio [00:07:44]: No, that’s great. And yeah, I would say Figma Make is one of the maybe most impressive releases I’ve seen this year. I was playing around with it the last few days. I built a Figma clone in Figma Make, so you guys are cooked. You let us realize a bit. Exactly. Dylan [00:07:58]: Or are you so back? Alessio [00:07:59]: I don’t know. I know. I don’t know. It’s so hard to keep up. It depends on the day. Yeah, exactly. Tomorrow that might change. But to me, there’s this interesting triad in software engineering, which is like you have the tests, you have the spec, and you have the code. Yeah. And you usually, if you have two of the three, you can generate the third. I’m curious how you think about the Figma model, so to speak, the almost Figma data model. You have Figma Design, which is like where the visual work happens. Then you have Figma Make, which is basically, in my mind, the bridge between the design and the code. And then you have the Figma MCP, which is like, how do you bring that into code in a way that it’s not even UI-driven? It’s just like the model is kind of doing the work for you. Does it feel like it’s changing, in a way, the tools that you need to build? And how do you think about, yeah, you mentioned using AI for editing the design and whatnot. Do you feel like natural language is becoming more and more the interface, even in design, that the work is going to be done? Or, yeah, what are the pieces in your mind? Language as the Interface for DesignDylan [00:08:57]: Yeah, lots to unpack there. Sure. Sure. Sure. Is natural language the interface? Yes, right now. I’ve said it before, but I really believe it. I think we’ll look back on this era as like the MS-DOS era of AI. And the prompting in natural language that everyone’s doing today, I think, is just sort of like the start of how we’re going to create interfaces to explore Latent Space. So I’m just like, cannot wait for an explosion of creativity there. Because I think when I think of these models, it’s like they’re almost like an N-dimensional concept. There’s this little compass that lets you explore this wild, unknown fog of war in Latent Space. And you can kind of push the models in different directions through natural language. But if you have a more constrained end there and you’re able to dimensionality reduce a bit so you can push different ways, there should be other interfaces available than text. These might be more intuitive, but they also might be more fun to explore. And I think sometimes constraints unwind. So I’m excited for that. But right now, yes, natural language is where we’re at. And while I’m excited to push that forward, meet people where they are, I think, is usually a good model for product development before you get to the point where you’ve really refined. Going back to your triad, I think maybe we can start with the spec. Like, I think the notion of a spec is evolving so much right now. And what should be in a PRD? Versus what should be in design versus what should be in code. That is, I think, much more blurry than it used to be. It used to be that we had, obviously, this, like, very kind of waterfall-y process of, oh, yeah, we’re going to go gather some requirements and then we’re going to go, you know, make a big doc. And then we’re going to go make some designs and we’ll code it up when we feel it’s ready. Maybe you go repeat a few times, but it was a process. And I think. With Figma, you can absolutely follow that process. But also, we recognize that roles are blurring, stages are blurring. And as all that blurs, how do you actually support different ways of working? You might want to make a prototype as part of or in place of, you know, a PRD. You might actually want to focus more on the design as a high-fidelity descriptor of what this could mean if the cost to make design and to create designs is low. And I think that the more that you can kind of expand that option space for people and bring them into a surface to align design and visual fidelity might be the place where you actually can align best. And there’s also the question of, okay, how far can a spec get you? And why is a spec different than code? So if code is the complete spec in terms of it is the most determined, clear way to show up in a design, then it’s going to be the most determined way to show up in a design. So intent of what should happen in every edge case, well, how much of that can be inferred? I think that’s an open question, but one that we’ll all be thinking a lot about soon. And if you think about sort of the value stack overall, it feels to me that the better cogeneration gets, the more design matters. And the more that actually the human pushing on design matters, too. Because even if you have a good starting spot from your design system, from... You know, AI generation, whether it be code or image, you, I think, need to push design forward, not just as an individual screen, but as a system in order to actually compete, differentiate, and win. It’s been our thesis for a long time, design is a differentiator. But I think it’s even more true in this world where we’re at now, where the rate of software creation is going exponential and maybe even vertical. And in that world, you have more software, there’s more competition, so what wins? Well, it’s brand, it’s point of view, it’s taste, it’s craft, it’s design. And I think that’s, if that’s the world we’re headed for, which I’m very confident it is, then it’s not enough just to use AI to generate an output. But I think you have to push further than that and really get in the detail into the craft. And to utilize an AI to explore the option space faster, so you can go as deep as possible in the direction you choose. Source of truth between design and codeAlessio [00:13:37]: Yeah, you know, I only have the pro plan, so I don’t have Code Connect. But I’m curious how you think about that. Because Code Connect, the whole idea, in my mind, was like, hey, instead of having to make sure that the code stays in sync with the design, we kind of build this bridge between the two. But now, if you have the design, you can, in theory, every time regenerate the component anyway. So why add, you know, maybe this additional layer? That before it was there, it’s not needed anymore. To me, that’s the most interesting thing. I was like, what’s going to end up being the source of truth? And what are, like, the two-way bridges? So, for example, right now in FigmaMate, I use the MCP to bring that code into Cursor, my actual code base. But there’s no way yet, I’m sure you’ll do it, for the MCP to write back into the design and say, we actually ended up implementing it this way. I’m curious where you feel like the center of gravity is going to be. Like, obviously, you’re biased in a way. But as an engineer, you know, I’m curious your thoughts. Yeah. Dylan [00:14:30]: Well, first of all, just kind of explain CodeConnect a bit more. So, to expand on what you already said, for, I think there’s different situations that you might find yourself in. So, you might be going to zero to one, making a prototype of something that’s rather disposable. You might be actually working on a personal project where you’re not making something that’s disposable. You’re building on something that’s existing, but the code base is small. It’s, like, pretty clear what’s going on to you. And there’s not a lot of patterns that exist. Or you might be in a pretty large code base where there’s a lot of existing patterns, a lot of code. And you’re trying to fit those patterns. So, especially in that last example, I think as you get to these larger code bases and larger sort of settings inside of companies, it’s very important to be consistent with existing patterns in the code. But also, it’s important to create a design system where, you’re able to create consistency at scale for designers and make it so that people are more efficient. So, they’re not always recreating different buttons, et cetera. There’s the world of Figma and design, and there’s the world of code. And there are advantages to having a source of truth in Figma and a source of truth in code. So, in some cases, we look at libraries that customers make, and they are one-to-one. The design components perfectly reflect the code components. So, the components in the code base. In other cases, you’re working on the thing that’s next in Figma, and that’s not yet all built out in code. And both cases are important. In the case where you are one-to-one, and there’s more of that bijection between components in Figma, components in your code base, you want to define a formal mapping. So, that way, you’re able to give context via MCP, make it so that developers are easily able... to implement a design on the front-end. And CodeConnect serves that goal. We’re doing a lot of investment to make it easier to set up, because right now, it’s too much of a pain. But also trying to get further in terms of how many people can use CodeConnect. And in terms of where the source of truth lies, I think there’s a variety of ways it will probably play out in parallel. I think that it’s okay if, for example, in some times, that code is the source of truth. And you’ll see us do a lot of work to make it so that you’re able to bring your code-based design system into something like Figma Make or Figma Design. But also, if you’re wanting to rapidly iterate and be able to express and try out different visual explorations, and if you think visually, if you are someone who’s not necessarily maximal comfort with code, I think a visual surface is... very important as a place to explore. And I think there are different modes of thinking, and it might be the case, I think it’s likely the case, that the visual sort of metaphor is easier for a wider set of the population to grok than to go into code. I also think that it’s going to be something that, as we move forward in time, with more agents writing more parts of your codebase, I think that it’s going to be something that, as we move forward in time, with more agents writing more parts of your codebase, you will also be less familiar with the code. And so then you might want a different abstraction where you’re able to work on things and basically plan out what your app should be, what your software should be, and Figma can provide that. Figma as a Context RepositoryAlessio [00:18:15]: Yeah, I almost think of Figma as like the context repository for aesthetics. To me, it’s almost as an engineer, right? The design product is not even that helpful, that useful in a way, as long as you can generate the components and I can do small tweaks. And I think one of the big tailwinds that Figma has is, pardon the pun, but tailwind CSS bringing a lot of this more classes, name classes as a way to define style, and the way the Figma variables and the way your system is set up. For me, once I saw Figma me, I’m like, okay, now I get it. Before, when I had the blank Figma canvas, it’s like, I’m not talented enough to start from here, but if you can build an initial thing through AI, then I’m good enough to tweak it and then have that be now the bridge. And then I can take that in Clock Code, I can take that in Cursor, or maybe I’ll just say Figma make forever and the prompts just go there. Dylan [00:19:11]: Yeah, I think that what you’re saying is super important as a point. The blank canvas problem is real. We’re always trying to figure out for Figma design, how do you make it less intimidating for someone to come in? How do you make this more approachable? And there’s always tension between the power users of Figma, Figma design, who would like every single power feature that you can imagine. Why can’t you make a feature for everything in the CSS spec? And of course, we can over time. On the other side of it is, okay, you’ve got someone coming in for the first time, technical, non-technical, whatever. Are they intimidated? Do they feel invited to go create something? And the first, regardless of our UI, the first thing that can block people is that blank canvas. So getting people from the place of, you know, I have an intention to actually putting something on that canvas is so important. And I think once you start getting people in that loop, then it’s less intimidating. You have more that you want to explore. One of the things I’m excited about that we just actually shipped today is a way to copy designs from make into Figma design. And if you think about Figma make, as a just easier entry point for Figma design, you know, it’s like the flight simulator to the airplane cockpit or something, then perhaps you’re able to make it so that that’s an early entry point you go through. And then you actually can do more than just tweaking components, but actually visually manipulate a design. And you might find that you actually can go faster that way than if you’re doing it in code. Alessio [00:20:51]: Yeah, I think, I don’t know what your model internally is between of diffs, but to me it’s like it’s easy to communicate what a diff in code is, but it’s kind of hard to communicate in design in a way that I can then put into an LLM to like apply. So I’m curious how you think about that. It’s like there’s obviously the design is more than the sum of the components, right? It’s like if you just like any piece that is in Figma individually doesn’t look like anything, then when you put them together, it looks great. How do you see the way people communicate design? Also change now that more of it needs to become language because of the interfaces. Understanding and Representing Design Diffs through AIDylan [00:21:30]: Yeah, I think that I might push back a bit on the more of it becomes language part, but maybe we can explore that later. Depends on exactly what you mean by that. But in terms of the way that we represent diffs, you can go to version history in Figma and see sort of composites of diffs. You can make a new version at any point. And of course, internally, there is the journal of every single edit. And I think that’s really cool. I think there is opportunity there, to your point, around how do you basically use not just what’s in Figma design as a source of truth, but also the tool calls that made via MCP and when they are made to understand what is the context that’s changed since I last made a call. So that’s an opportunity that I think is a smart one to point out. But also, I think it’s just interesting to think about the journal data and what could be possible in terms of thinking about what you might do next, how to help you be more efficient and help you explore more ideas. Alessio [00:22:32]: Yeah, my natural language part is if I give an LLM a get a progress, it can understand from the code what type of changes I’ve made. I’m trying to figure out, and this is just because I’m not a Figma power user, if I gave an LLM a Figma journal or diff, could it understand aesthetically what has changed to then update maybe other docs or parts of my system? Dylan [00:22:58]: Yeah, right now that’s not there. I think it’s a really good idea. And I think it also mirrors other ideas that are going to be important too. For example, if you pull in Figma context into your IDE or your agent, whatever format it takes, let’s say that the design is not perfectly implemented, because currently that’s where we’re at, is a lot of times it’s, wow, this is a great start, but there’s more work to do. Well, what’s the delta? How do I get to the point where it’s perfectly implemented? And that requires some back and forth too. Yeah. Alessio [00:23:32]: When I did the Figma to Cursor, the only thing that got wrong was the border color. Everything else was perfect. Dylan [00:23:39]: I’m glad to hear that. That’s awesome. I was very impressed. And to be clear, some of the time it does just work. Exactly. And it’s magical. It’s magical. And by the way, I’ll point out, that’s not like a comment on, oh, front-end engineering is dead. There’s so much more to front-end engineering than just that translation step. That’s kind of like the most mechanical part. And actually thinking through all the states, all the intended behavior, and how to make the design truly come to life, that is a lot of the interesting work. And I’m excited for how interfaces will get much more rich and much more interesting as we make it so that they’re easier to go from design to code in that first place. Figma’s Role in Shaping Visual AestheticsAlessio [00:24:20]: Because that’s just the first state. Right. Yeah, when we had Greg Brockman, we talked about these purple and blue gradients kind of taking over the web because of all this training data. Do you feel a sense of responsibility in a way in Figma Make also to set the new standard for what things should look like? And how much do you think about making that explicit for the user right on? It’s like, hey, I’m actually not going to... You need to make some aesthetic choices early on. Yeah. Dylan [00:24:45]: I feel very strongly that across the Figma platform, we should do our best to find ways to help people explore more of the space of aesthetic rather than impose a personal viewpoint on aesthetic. That’s a hard problem. But if we can accomplish that, I get very excited. Because if you can actually open that space up more and figure out how it applies to software, to product design, then not only could we... We’d be in a place where we help you generate high-quality visual output, whether you’re a trained designer or not, but also we could get to the place where you could be nudged into or nudge yourself into different directions that are underexplored relative to the design community and design history. As you know, it’s like there’s an ability to interpolate between different styles, different ideas, and AI can help you do that. But also the designer can then take that so much further. Yeah, I think regurgitating the median website is like, you know, maybe that’s where a lot of us are today. But where we need to get to is one is a place of really pulling out new styles. And I think overall, the other thing I’d say is you look back at the Flash era of the web. You know, we both grew up through that. And it was maybe not always high quality, but dynamic. Exciting. Fun. You know, this was an era where experimentation was happening. And then we, you know, at some point it was like, okay, Steve Jobs gets up on stage and goes, Flash is dead. You know, here’s my new world. And, you know, brief skeuomorphic phase, then Swiss Minimalism. And we’ve all been Swiss Minimalism for quite a long time now. And I just think that, again, going back to, okay, more software created than ever. What needs to happen? Well, designers need to push us forward. And that’s going to mean an exploration, an explosion of creativity and so many different visual styles that will be explored. So much more dynamism in interfaces and new patterns emerging. Especially as you start thinking about what are all the screen targets we’re going to have. Those are going to explode too. And states, you know, all the different surfaces that will be created. And designers will have to think through systematically. That’s a big challenge. A big opportunity as well. Yeah. Alessio [00:27:13]: I started, you know, Kernel Labs a month and a half ago. And James, our designer, I’ve been working with him on our initial landing page and our product design. And he’s almost like my Latent Space shepherd. You know, it’s like, even if I had, even if Figma was AGI, I still wouldn’t necessarily know what to ask for. You know, I think that’s like really what people a lot of time get wrong, which is like software. It’s like the same thing. It’s like you could give a software AGI to anybody and like doesn’t mean they could build great software. Correct. You know, I think to me, that’s the most exciting thing. But what I really like is then the ability now that I have to like reuse this across surfaces in a way that wasn’t possible before. You know, because I can create ideally in Figma, not my design system, but now you have make to generate all new types of products. I have the NCP. You also have, you know, slides. You have like different products on the other end of the spectrum. And again, going back to being like the context repository of aesthetics. I know that as long as I have, you know, the context from Figma. Whatever the AGI or whatever I’m talking to is going to generate, it’s going to have some sort of like rooting and like what I think looks good. Yes. I think some people will have it personally. I think some people should have some sort of like personal Figma almost. Where it’s like, you know, when you’re generating images on ChatGPT or Midjourney, you should have some sort of like aesthetics to draw from. How do you think about that evolution? Because you’re going from a world where like only designers work on your product and now it becomes a core part of like a lot more constituents. Yeah. Dylan [00:28:39]: I think in a world where design is the way you win, it’s only natural that we need to get more people involved in the design process. That is not going to diminish the role of designers. In fact, I think it expands the role of designers because then you have to shepherd people through the design process and help them go from, okay. I mean, it’s kind of a journey you go on, right? Like not even being aware of design. Yeah. You know, kind of like blindly going through the world to, oh man, aesthetics, they matter. To, okay, can we make it pop? Can we make it cool? And then people start to actually think about it. Well, wait a second. Like I’m looking at one screen. What’s the actual experience here? What is the entire flow? And then it’s, okay, well, let’s take mental models of how we can think about this experience and consider it in different ways. What are the potential different paths? Metaphors and experiences that we can create here? And what are the abstractions that matter? And then from there, it’s like, okay, wait a second. Well, this all exists in the context of like our brand, the greater culture of the moment and business constraints and all sorts of other things you might be optimizing for. And I think more people coming to the design process. That can help add in context as well. And there’s no reason why someone who’s outside of design or doesn’t call themselves a designer, whatever they identify as engineer, product manager, CEO, whatever. Everyone should be able to come in and say, okay, here’s an idea. And the idea hopefully could be parsable in high fidelity to the standards of at least the design system and consistent. Yeah. So it’s not distracting because an idea should be evaluated on some merits. But then I think from there, the actual exploration and making that great, that is a hard design task still. Alessio [00:30:47]: So how do we lower the floor for everyone coming in, but also raise the ceiling, make it so designers can do even more and produce even greater work? I think like, I mean, obviously, you know, it’s cliche, like everything is changing, right? But I think there’s just like a fundamental shift in both. So how people perceive software. I think, you know, Sam tweeted at the moment, tweeted about the fast fashion era of SaaS. But there’s also like a negative connotation to fast fashion. But I feel like in software, it’s like, man, if you can get the software that you need at any moment, that’s not a cheap thing. That’s like an expensive thing that is now being made cheap. And it’s like still high value. And I’m curious like how different products are going to drive that. Even though it feels like, hey, I just created this for you. Very quickly. But like there was so much that went into that, you know, that is like maybe sometimes undervalued, you know? So I’m curious, like if there’s something that you think about where, okay, in a way people should come to Figma and do a lot of work. But maybe in a way we can kind of help you from like all the work you’ve done in the past, kind of like come to the right result much faster in the future. Yeah. Fast Fashion in SoftwareDylan [00:31:56]: Well, I think especially in the context of teams where there’s, you know, they’ve done a lot of work in Figma. Of course, there are patterns that with that team’s consent, you can tap into and figure out how to help improve outputs. But I do have a little skepticism about the like fast fashion interpretation. I just think that there’s where we’re currently at at least with the models. And, you know, of course, we’re on this trajectory, whether we’re on an S-curve or an exponential or it’s an S-curve that will turn into an exponential. I don’t know. Maybe you’ve got a point of view. Sure. But I just kind of like, okay, I’m excited for the ride. And as long as… I’m here today. Well, my mental model and strategy is just like your strategy should always be okay. Assume AI models get better and make sure that makes Figma better. As long as I believe that’s true, I’m happy. If not, change strategy. That’s the algorithm, you know. But wherever you’re at, I think that in terms of that interpretation of where models are going, I don’t think the world is in a place today where those models are going. I think the fast fashion era is here. And I also think that so much of designing software is doing it in a way where many people can use it. Like, it’s rare, I think, that you’ve got an individual piece of software that is truly just for you. I think it’s awesome that more people are exploring their ideas and creating software and tools for them. But then the next step, if they want to go further, is, okay, how do I make this good for other people, too? And in a setting where people are trying to learn software, most people learn it from other people. So now you’re in the same place you were before. You had a piece of software. You made it just for you. You decided it actually supplies other people the problem that you solved. Now you’ve got to have something that is probably consistent enough to actually share with others so they can learn it. And it gets adopted. And so I don’t know. I think it’s like, yes, more people will create software. That’s awesome. But also, I’m not sure that software will just be, like, disposable. And if you look at the way that people work, whether it be with Cloud Code or Cursor or Warp or whatever, so much right now is, like you said, you need to have some expertise about how software is built. That lets you discretize the task, just like you would to an intern. And, you know, maybe it goes beyond intern level. But still, I’m not saying, okay, go build Figma. And you, agent, are just going to go figure out all the complexities of Figma. I think that’s just not something I see happening in any near-term future. Even as longer-running agents start to occur and we’ve got better capabilities, that’s a long ways out. Now, maybe that’s a high bar. But you look at the actual workflows that happen with, you know, the very big SaaS applications. Let’s consider Workday, for example, or Salesforce. A lot of CIOs would, you know, love to go, okay, yeah, I’ve vibe-coded Workday. And I just saved my company all this money or whatever. But, okay, you actually peek under the hood of a Workday or a Rippling. These are very complex pieces of software that have accounted for every edge case that you can run into as you’re thinking about your HRIS. And the platform of data that you can tap into. Yeah. And then go build out from there into different workflows. And they’ve done that through, you know, over, in Workday’s case, decades. In Rippling’s case, you know, not quite a decade, but also a lot of prior knowledge about what needs there are in the market. And very intentionally built. So I’m skeptical that, like, without that knowledge of the workflows that people will encounter, people will actually make something that can scale. I think you run into the same problems that you’ve run into all along. And then it’s a loop. Maybe that loop goes a little faster. But it’s not, like, just going to replace. Limitations of Prompt-Based Software CreationAlessio [00:36:04]: I think that’s the bull case for software still being helpful post-AGI, whatever that means. Because in a way, you still need to prompt the AGI. And I think you’re going to end up having these interfaces that, again, just like you’re going to help people explore the Latent Space in design, there’s going to be a way for interfaces to, like, compress the way that information gets passed through these systems. I think in data analysis, you’re kind of seeing a lot of the applications kind of going away in a way because the models are, like, so good at it and it’s so natural to do on a conversational level. But again, sometimes it’s like, well, how do you give it the right data? How do you ask for the right type of charts? How do you ask for, like, the right follow-up questions? I think there’s, like, a good question of at what point is a software a piece of software, at which point it’s just like, again, a Latent Space guide that just helps you project your interest into the model. Yeah. Dylan [00:36:56]: And it’s interesting. It’s like if you look at data analysis, maybe break it up into a few things that have to go right, you need to have, first of all, trust that the right queries are being written in the correct way. And that’s a lot of trust because if you get that wrong, you know, you have a bad shaky foundation for the rest of your experience. And there’s, okay, what’s the next query? I’d probably be more bullish about AI predicting the next query or a follow-up than I am about, like, you know, 100% rate on the query being constructed correctly. I think if you were to show people some prompts around here, example, next queries you might want to run, that might spark other ideas they have for follow-up questions that further their analysis. But there’s also, like, how do you display the data and the visualization itself? Yeah, there’s canonical visualizations we’re all used to. But I think visualization is fascinating. And we’ve only scraped the surface in terms of how we can visualize data. I think that especially to get to larger data sets, more complexity, and you’re really trying to communicate data to people, that is one of the most interesting design problems out there. Like, how do you communicate how much money in the budget of the federal government is spent where? People have tried so many times. I’ve never seen something that is clear and actually gives you any sense of scale that you can relate to, you know. And how do you communicate? How do you communicate the data inherent in biology to a layman person who hasn’t studied biology? Like, again, people have tried. It’s a very hard problem. And maybe you break into sub-problems, but still, there’s so much to push on there. Yeah. Alessio [00:38:42]: Yeah, exactly. There’s almost, like, the two ways. So, like, me, how do I communicate to the model what I need? And then there’s also the other side, which is, like, the models have, like, so much imbued into them that we need to get out of it. That we don’t quite know how to do. And, like, one part is, like, information communication. You know, one part is, like, once I prompt it, how do I get the response in a way that I really parse? I think, yeah, that just, like, I don’t know. It’s been breaking my brain for the last few months just thinking about what will feel like software, what will feel like a conversation. And, like, I know that, obviously, OpenAI, you know, has, like, this big goal of, like, kind of being your companion and, like, you have the voice mode and whatnot. But at some point, you just need something that is beyond that. You need something beyond component rendered inside a chat interface, you know? And it’s hard to figure out, especially when you think about, I know you do a lot of angel investing. So, I’m also curious about how you think about startups and, like, what kind of products are, like, now possible that maybe weren’t before? Like, what products people should stop pursuing because you think will be a part of the models? Interfaces Beyond ChatDylan [00:39:43]: First of all, I’m hesitant to give advice on that because, first of all, I think that the idea that all software will or lots of software will exist. In a session with an LLM or any model, I think that’s a little overblown. There’s so many different ways this could work out, whether it be, you know, kind of a back and forth of the model as an origin of a request. And then you go elsewhere to the models embedded in software. But you still have a destination you go to first. It’s not the model. And we can think of many other ways, too. I think that that’s maybe a bit of a shaky assumption. And then also, I think that people, it’s often the case that you’ve got some space that, like, a thousand people are starting a thousand companies in. And everyone’s going, oh, don’t go there. It’s too crowded. But then one person comes up with some really new, clever idea. And it’s a totally different take. And they propel from there. So, I never try to say don’t do something to an entrepreneur because somebody that’s listening to this podcast is going to have some ego. They’re going to have some unique insight in some space that both of us think is really dumb to work in. But then they’ll be the next, like, you know, trillion-dollar company. So, and, you know, of course, like, whoever that is, you know, let me know. Right, yeah. Let us be a part of it. Let us both know. But, yeah, I think there is some amount of memetics around, like, people see other people doing something and they follow along. And you have to have a unique insight if you’re starting a company or working on a product. It usually should be something that’s unpopular. Right. And this is just cliche advice at this point. But, like, I think there’s something deep to internalize there about the kind of contrarian nature. I’m going more tea language now. But I think he’s basically right about this. I mean, as in if you’re investing in something, unless you’re just going after momentum, which a lot of people do. But otherwise, you need to have some point of view that, like, a lot of people would just blanket disagree with. And it should be scary to you if you’re investing in something and you tell your friend about it and you say, here’s my point of view on why this is really cool. And your friend is like, oh, yeah, I totally agree. It makes complete sense. That should be a warning sign if you survey people and they’re all saying the same thing about that. Yeah. Lessons from the Thiel FellowshipAlessio [00:42:12]: What have you learned about yourself during the TO fellowship? What are, like, things that you change about how you approach, yeah, just life, thinking, learning? Dylan [00:42:20]: think whether it be, like, that interaction with Chris where I look back and go, man, I maybe dismissed that one too soon, you know, and then learned over time, thankfully. Or another example is I think in 2013, there was, like, a Bitcoin hype cycle. You know, Bitcoin went to $1,000 or something. And, like, half the TO fellows at the time were really excited about Bitcoin. I’m just like, these idiots, like, how do you short this thing? And, like, that was my default reaction. And I think that the overall meta lesson that I’ve learned over not just the TO fellowship but just being around tech for a while now because I was paying attention even as a kid. I was in some commercials that were, like, for, you know, Microsoft and eToys, for example. And so then I’m starting to track, like, what’s this dot-com bubble? And wait, why is that coming? Why, like, am I not getting residual checks anymore? You know, that became, from a monetary standpoint, I’m like, I’m going to read the newspaper. And then, you know, working in high school at O’Reilly Media was a great point to get exposure to some of the starts of cycles, TO fellowship. And the meta lesson I think that I’ve learned is don’t look for reasons why things are not going to work. That’s important too, but it’s not the place to start from. The place to start from is, like, what could this be? How big could this be? How important could this be for society? And let yourself imagine, dream. And then go and think about all the ways it’s not going to work so you can mitigate each one. But, like, start with the dream. And I think if you start there, it’s just a better default position to go from. Alessio [00:44:05]: Yeah, I’m really worried about the X algorithm and what it has done to optimism because it’s so easy to get likes just being negative about things. Dylan [00:44:14]: I do think the X one seems to have changed a little bit recently. Alessio [00:44:17]: Yeah, maybe Nikita’s in there tweaking things. Dylan [00:44:20]: Yeah, I’m thankful for that. But, yeah, I do think algo feeds reward controversy and being negative is a way to get controversy. But also, I don’t know, I’m default optimistic. I feel like society just builds antibodies to different things over time. I mean, remember when we were all worried about, like, oh, my God, everyone’s going to be a zombie playing Farmville all day? Right. Like, well, here we are, you know. Yeah. Some people still play Farmville, but, like, it’s. Right, mostly. I don’t know anyone that does that, like, you know, all day long. And most of us kind of forgot about, you know, the social gaming era. Using X for Product FeedbackAlessio [00:44:58]: I know. So. When I drive by the Zynga building, I’m always like, I remember, like, in the days. Yeah. How do you use X? Because, I mean, you were famously on IPO Day responding to product feedback on X. What’s your routine for staying on top of that? Dylan [00:45:13]: Oh, I mean, I try to just, like, search for Figma a lot. And see what people are saying. But also, I’ve, like, trained my algo feed to show me a lot of stuff that’s relevant to Figma. Yeah. And there were ways. I don’t know if they still are as powerful signals with whatever algorithm shifts have happened. But it’s, like, you kind of find out what signals matter, you know. Like, ironically. Right. Not interested in this post. Seems to not do anything. You know. A like or a bookmark. I’m not sure how much that matters. But copy link. Turns out that really matters as a signal. Or at least it did. So, you know, I wasn’t always sharing the link, but I’d copy it whenever I saw something I wanted a signal boost in my feed. It’s like, okay, the more you learn as X for the algo feed, the better you can train it, the better you can make it useful. And then, you know, I think feedback across any surface, not just social media, but support, sales conversations, conversations in our community. We gather people together and then just talking to folks. Research both qualitative and quantitative. These are extremely useful signals for our team. And I think of intuition as like a hypothesis generator that you have to test the hypotheses. So, using feedback to be part of that test is important. But also, I’d always rather give feedback to the Figma team by surfacing the voice of a user rather than being like, I have this point of view. Right. You know, I’d do the latter as well. But the former is my preferred method. And so, I’d much rather champion user feedback or a user bug report. Right. So, if I have a feature request and then dive in with that person, then, you know, just have it come from me. And the other thing is I’m always looking for those visionary users who are a step ahead of everybody else. They know just kind of intuitively what is needed. And I think that when you can find them and separate them out, that’s a signal that is just amazing to get. I remember early days of Figma. There was this one user test that I did. I literally brought a bottle of wine, too, because Figma was so slow yet at that point. I mean, like, to complete the user test, I knew it would take hours. So, it was kind of a tough one to administer. We went through the bottle of wine during the user test. And the person that we were doing the user test with, this guy named Payam, who’s an amazing designer, then was working at Coursera. And the next day, he followed up with, like, this super long doc. I mean, it was eight to 10 pages. And it laid out basically a lot of what ended up being our roadmap. Not like we literally follow the doc, but I look back and compare contrast. And yeah, he’s an example of someone who was a visionary user. And for a person like that, there’s a lot of people that will give you more local feedback. But some people can see the big vision, too. And that’s always really exciting because it’s validation for you and the team about Early-Stage Recruiting at FigmaAlessio [00:48:10]: where you should go, but also a source of new ideas and insights as well. How did you think about hiring and building the team back then? Because I remember I was working at a YC company and we were all on Sketch. Kickback. Okay, cool. And we were all on Sketch, right? And so, I think a lot of people are maybe like, well, why isn’t Figma just going to be like Sketch and blah, blah, blah? Yeah. How did you figure out who are the right people to bring on the mission? Because I think the same thing is happening in AI, which is kind of like the, you know, the meme of what should be built. And then maybe there’s like some more missionary people. What were some things that you think people should take on early stage? Early stage recruiting, especially. Dylan [00:48:46]: Early stage recruiting is so hard. So, first of all, just don’t give up. Right. That’s my first piece of advice. Second piece of advice is just think long term. You know, there are folks that I talked with in the first year or two of Figma, and they didn’t join until like year five, year six. But those relationships, it’s amazing how if you’re consistent and just spending time together with people you like, how they’re going to get along. And then eventually it turns into something that could be they join a company or actually just throw a friend outside the company, but someone that inspires you. And that’s great too. But yeah, I think taking the long view is super important. Of course you need conversion today, you got to hire. And I think the kind reality of being early stage and having a lot of risk is you have a natural filtering function. Right. Like, only the true believers are going to get on board. And I’m a fan of just not selling too hard. It’s like, make sure people understand what’s going to go on and what’s going to happen and where you’re pushing and what you’re going to do. But like, if someone needs to be sold so hard, it’s usually a sign they’re not going to stick around. Yeah, I think you have to have a really good process. Best recruiting advice I got in the early days, yeah, I told John Doerr one day, he said that, you know, I was having a lot of problems with recruiting. I wasn’t very good at it. And he’s like, well, do you wake up in the morning and it’s the first thing you think about recruiting? I’m like, well, no, I’m thinking about like coffee, like, okay, well then, you know, it’s like mid-morning. Like, are you thinking about recruiting? I’m like, no, I’m probably thinking about like what snack I’m going to have. I’m just like, okay, it’s lunch, are you thinking about recruiting? I’m like, no, I’m probably thinking about like what emails I got to do. Okay. It’s like last thing, the last part of the day, you’re about to go to bed. Right. I’m like, am I recruiting? I’m like, no, definitely not. I’m like tired. He’s like, well, just like, if you’re thinking about recruiting and all these moments where you just have a moment, have a second to pause and you’re actually on it, then it’ll fix itself. And the way that I think can manifest as a process is you just basically make a spreadsheet of here’s my funnel and you obsessively look at it all the time and go, okay, like, how do I make sure that this funnel? Uh, keeps going. Just like you would with a sales, if you’re a salesperson, you have to continue to feed the funnel. You have to move people through it. And if you’re not doing that, you’re not recruiting. So yeah, you have to be very disciplined, which is something that, you know, I’m, I have to push myself to, I like to kind of be in the cloud. So Alessio [00:51:30]: I definitely like to have coffee. Um, how has that changed now that you have like, you know, public company that you run obviously would make, I’m sure you have to build a new team to kind of lead that. Yeah. How has that changed? And also like, you know, uh, this is a great call for recruiting, uh, for engineers listening. What are like the type of people that succeed at Figma today? Dylan [00:51:51]: One thing that’s been interesting is that, uh, yes, of course we’ve hired amazing researchers who are pushing the boundaries and thinking, you know, in a way that’s, you know, on its own life cycle in terms of not as tied to, okay, we have an explicit date we’re trying to release on because that’s just not how research works. But what I’ve found is, um, very good. Yeah. I mean, you know, we’ve had engineers who are just more full stack and oriented towards learning new things. They can be quite successful on AI products. There’s some reorientation they might need to do. They might need to, of course, learn new skills, just like designers need to learn new skills. But in general, we’re looking for smart high agency people who have product sense, who care about design, who see the world the way that we do. And, and want to learn skills and keep growing and work on hard problems. I think that’s the filter that’s always been the case for Figma. Alessio [00:52:48]: And, um, if that resonates with people, then yeah, please apply. One question I have from Zach from Warp was how do you position Figma make in like the universe of this like prompt to app, prompt to creation? Like how should people think about how you fit? Do they feel like competitors to you? Do this make just feel like an extension of the design team? What is like the core? What’s the competitor universe for you? Positioning Figma Make in the Prompt-to-App LandscapeDylan [00:53:11]: You know, it’s depends on how you define it, right? Like I think, um, engineers feel much more comfortable in ID than non-engineers. And as we get to more agentic environments, perhaps that’s a different vibe, but still, I think most people feel like, uh, an ID or cod code, if they’re not engineers, they feel like that’s not made for them. Yeah. And I think that with the platform we have and the visual metaphor, the opportunity for free form exploration, ideation on an infinite canvas and being able to try out lots of things and then also see the big picture of what are the different paths I can go down. That is a metaphor that I think works for a lot more people. And in that sense, as we tie, make in even further than just, okay, I can copy a state, input, and Figma design. That’s what we launched today, but there’s so much further we can go. And the further you go, the more you’re able to then, I think, bring more people into this sort of surface. So in that case, you know, it’s, it’s just like, we’re trying to race against ourselves. I think, um, you know, if you evaluate Make as it launched, we raced towards launch, you know, as prompt to code and not a lot around that, not a lot of integration with the Figma platform. Then sure. There’s a million other tools and more coming every day that you can evaluate it against. But I don’t know if that’s the right way for us to think about it. I was, um, listening to the Brett episode you did, and he had this one line that’s just stuck with me, which is, he was talking about, uh, you know, do you want to resell coffee beans as roasted coffee beans? Or do you want to like go make the amazing, like special latte, obviously want to make a special latte. Yeah. Everyone does. But like, how can you make something that is, uh, unique in place, the needs of designers and does that in a way where we can really bring advantage to people. And I think there’s so much we can do there. So many different tiers that are coming that I’m just really excited about. Yeah. Digital Scarcity & AIAlessio [00:55:19]: Nice. I know we don’t have too much time left. I want to just talk about some outside of tech. So you always have a CryptoPunk as your Twitter PFP. How do you think about it? It’s actually a chain runner, not a CryptoPunk. See? Okay. Uh, we’ll, we’ll go through NFT education later. How do you think about in the digital world, especially like you have, you’re gonna have this huge divide between scarcity and like, you know, abundance, right? How do you feel about the future of like these digital collectibles and like communities and like, yeah, how that fits into the universe of like, Hey, you can generate anything at any time. You know, Enzo Ferrari used to say a Ferrari can never be readily available to be desired. I’m curious how you think about the distribution of things. People will see on the internet between the super niche tailored, just created for you and like these kind of like iconic cultural properties. Dylan [00:56:09]: The paradox of like digital scarcity is what made me excited about NFTs before they’re called NFTs back in 2017, whatever it was. And I think that, uh, not everyone is like, has that collector gene, but some do. And, um, for those that do, whether they’re, whoever it is, they’re collecting digital items. They’re scarce and people will enjoy getting into whatever collector aspect they want there. But I think in general, I’ve, I’ve found myself wanting to distance a bit from the NFT space. It’s like kind of interesting and actually has some parallels, I think, to AI and I got in it so early and that point it was like this like just niche community on the internet of weird people that thought digital stuff could be scarce and might, you might want to collect it and pay real money. Yeah. But it was not expensive. And so anyone, for example, in the States, like could be part of that. And you really, it was gated more on just like, do you know about it and do you get the idea of it? And is that idea exciting to you or is it repel you? And, um, then I, like over the next three or four years, it went from this like very idealistic would think about what the future of creation and digital items and scarcity could be. So to get rich quick scams and just sort of this overall vibe of like flipping stuff and trying to make money and I don’t know, it just the, the whole meta of it changed. And I, that’s when I peaced out, you know, I, I realized at some point I was like, oh, I get excited about collecting some NFT project because I think that it’s the art is cool. I think that the creators are awesome and there’s some intention behind the work that’s And then, you know, if I talk about it online, like the project might do worse. It might attract people to buy it and folks can apparently, I learned, do scams where they basically pump and dump and create, you know, trading behaviors that are no good. And so I stopped talking about things I’m excited about for the NFT space. The parallel that I think is kind of interesting is, you know, you compare AI to that. And it’s like, there’s been a long era of people that I think are very on mission and thinking about the big picture, the risks, the opportunities, the possibilities. Dylan [00:58:42]: And that’s kind of meeting in this moment, the get rich quick. You know, if you look on YouTube, there’s a lot of people making videos about like, okay, how do you use AI to make business? Alessio [00:58:51]: Passive income. And I’m not trying to dismiss that because great, if you can make money using AI, that’s great for you. And some people certainly will. But I think there’s too much just like, do it because it’s going to make you some money, energy in the space right now. That makes me like a little bit nervous, having been through that NFT cycle and seeing where it ended up. That has been on, you know, I own a card store in San Carlos. So I do like Magic the Gathering. Dylan [00:59:21]: And there’s similar thing happening where like, you know, there’s a lot of speculation just because everything is the gathering, like super cool now. Alessio [00:59:28]: It is. Oh, fuck. Yeah. Well, I’ve been waiting for this moment. Let’s go. We’ll do it. We’ll do a Magic the Gathering event. Dylan [00:59:34]: Draft Night. I’m down. Nice. We used to have it in the early days of Figma. We used to do Draft Nights. Alessio [00:59:39]: Nice. Yeah. What sets were coming out then? Do you remember? Dylan [00:59:42]: I don’t remember. Alessio [00:59:43]: Okay. Yeah. Sorry. Dylan [00:59:44]: There’s a guy named Andrew on our team. And he wowed me so much. With his expansive encyclopedic knowledge of Magic the Gathering that I was like, wait a second, like, can we move you from support to product education? And then he killed it at product education because he has just as encyclopedic knowledge of Figma. And but yeah, it was like basically Magic the Gathering Draft Night that gave me the confidence inside of, oh, wow, like these skills are transferable. So that’s funny. Alessio [01:00:17]: Magic the Gathering, career opportunity. Exactly. You know, I should go around. I’m going to play in the regional championship for the Americas in November. I should just go around and say, okay, you should come work at this. You’re going to the regional championship? Dylan [01:00:27]: Yeah. Alessio [01:00:28]: I see you’re like really hardcore. So I think to me, that’s like the best way to like disconnect because you have to be so focused on the game that like you’re not actually thinking about things. But there’s kind of like obviously the collectible side, but there’s still at the core, like a community, let’s come together at the store, hang out, play games. And I hope that like, that’s what we’ll see more out of AI, which is like enabling more. Me too. Which is like enabling more. More of these like small communities locally to like, you know, have more entertainment and like support themselves in a way that doesn’t have to be, oh, is this going to make money? Like, is this going to be profitable? Dylan [01:01:01]: Yeah, I think the more you can go from a mode of like, I go on social media app of choice and mindlessly flip through my algo feed to I’m going and making things like that is good. We want to move consumption behavior to creation behavior. And yeah, I think that will happen. I just a little nervous about the get rich quick fives. Right. Yeah. Awesome. Alessio [01:01:25]: Dylan, we’ll have you for draft night at the new Kernel Space. Looking forward to it. But thanks so much for the time. Thank you. Thanks for having me. You're currently a free subscriber to Latent.Space. For the full experience, upgrade your subscription.
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Taste is your moat — with Dylan Field, Figma
Thursday, 2 October 2025
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