Taste is your Moat (Dylan Field of Figma)

Well, I think especially in the context of teams where 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 fast fashion interpretation. I just think that where we're currently at with the models, and of course we're on this trajectory whether it's an S-curve or an exponential, I don't know. Maybe you've got a point of view, but I was kind of like, okay, I'm excited for the ride. As long as I'm here today, 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. But wherever you're at, I don't think the world is in a place today where 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. It's rare that you have 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? 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 actually this applies to other people, the problem that you solved. Now you have to have something that is probably consistent enough to actually share with others so they can learn it and it gets adopted. So I don't know. I think yes, more people will create software. That's awesome. But also, I'm not sure that software will just be disposable. If you look at the way people work, whether with cloud code, cursor, or warp, 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. Maybe it goes beyond intern level, but still I'm not saying go build Figma and an agent is just going to 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 have better capabilities. That's a long ways out. Maybe that's a high bar, but you look at the actual workflows that happen with very big SaaS applications. Let's consider Workday or Salesforce. A lot of CIOs would love to say, yeah, I've vibe coded Workday and saved my company all this money, but 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 you can run into as you think about your HCM and the platform of data you can tap into and then build out from there into different workflows. They've done that over, in Workday's case, decades, and in Rippling's case, not quite a decade, but also a lot of prior knowledge about what needs there are in the market, very intentionally built. So I'm skeptical that without that knowledge of the workflows people will encounter, people will actually make something that can scale. I think you run into the same problems you've run into all along, and then it's a loop. Maybe that loop goes a little faster, but it's not just going to replace. I think that's the bull case for software still being helpful post-AGI, whatever that means.

Yeah, and it's interesting. 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 have a bad shaky foundation for the rest of your experience. Then there's, okay, what's the next query? I'd probably be more bullish about AI predicting your next query or a follow-up than I am about 100% rate on the query being constructed correctly. And I think if you were to show people some prompts around here are example next queries you might want to run, that might spark other ideas they have for follow-up questions that further their analysis. But then there's also how do you display the data and the visualization itself? There are 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 as you 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? 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. And how do you communicate the data inherent in biology to a layman person who hasn't studied biology? Again, people have tried. It's a very hard problem. Maybe breaking into sub problems, but still there's so much to push on there.

And we can think of many other ways too. I think that's maybe a bit of a shaky assumption. And then also I think that people, it's often the case that you have some space where a thousand people are starting a thousand companies in, and everyone's going, 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 listening to this podcast is going to have some ego insight in some space that both of us think is really dumb to work in, but then they'll be the next trillion dollar company. So whoever that is, let me know.

Let us both know. But yeah, I think there is some amount of memetics around people seeing other people doing something and they follow on. 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. And this is just cliche advice at this point, but I think there's something deep to internalize there about the contrarian nature. Going more Thiel language now, but I think he's basically right about this. If you're investing in something, unless you're just going after momentum, which a lot of people do, otherwise you need to have some point of view that a lot of people would just blanket disagree with. It should be scary to you if you're investing in something and you tell your friend about it and say, here's my point of view on why this is really cool, and your friend is like, oh yeah, I totally agree, makes complete sense. That should be a warning sign if you survey people and they're all saying the same thing about that.

I think whether it be that interaction with Chris Ella where I look back and go, man, I maybe dismissed that one too soon, and then learned over time thankfully. Or another example is in 2013 there was a Bitcoin hype cycle, Bitcoin went to $1,000 or something and half the Thiel Fellows at the time were really excited about Bitcoin. I'm just like, these idiots, how do you short this thing? That was my default reaction. And I think the overall meta lesson I've learned over not just the Thiel Fellowship but just being around tech for a while now, because I was paying attention even as a kid, was in some commercials for Microsoft Tiny Toys for example, and then I started tracking the dot-com bubble and wondering why I wasn't getting residual checks anymore. I'm like, I'm going to read the newspaper. Then working in high school at O'Reilly Media was a great point to get exposure to some of the starts of cycles. The Thiel Fellowship, and the meta lesson I think 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 start place to start from is like, what could this be? How big could this be? How important could this be for society? Let yourself imagine and dream, and then go and think about all the ways it's not going to work so you can mitigate each one. But start with the dream. I think if you start there, it's just a better default position to go from.

I do think the X algorithm seems to have changed a little bit recently. Maybe Nikita's in there tweeting things. 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. Remember when we were all worried about everyone becoming a zombie playing Farmville all day? Well, here we are. Some people still play Farmville, but I don't know anyone that does that all day long. Most of us kind of forgot about the social gaming era.

Oh, I try to just search for Figma a lot and see what people are saying. But also I've trained my algo feed to show me a lot of stuff that's relevant to Figma. There were ways, I don't know if they still are as powerful signals with whatever algorithm shifts have happened, but you kind of find out what signals matter. Ironically, 'not interested in this post' seems to not do anything. 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 I wasn't always sharing the link, but I'd copy it whenever I saw something I wanted a signal boost to my feed. The more you learn about the algo feed, the better you can train it, the better you can make it useful. And then I think feedback across any surface, not just social media, but support, sales conversations, conversations with our community, gathering people together, talking with folks, research both qualitative and quantitative, these are extremely useful signals for our team. I think of intuition as a hypothesis generator, then you have to test the hypothesis. 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. I do the latter as well, but the former is my preferred method. I'd much rather champion user feedback or a user bug report or a feature request and then dive in with that person than just have it come from me. The other thing is I'm always looking for those visionary users who are a step ahead of everybody else. They know intuitively what is needed. When you can find them and separate them out, that signal is amazing to get. I remember early days of Figma, there was this one user test that I literally brought a bottle of wine to because Figma was so slow at that point. To complete the user test, I knew it would take hours. It was a tough one to administer. We went through the bottle of wine during the user test. The person we were doing the user test with, a guy named Pyam, an amazing designer then working at Coursera, the next day he followed up with a super long doc, 8 to 10 pages, that laid out basically a lot of what ended up being our roadmap. Not that we literally followed the doc, but I look back and compare and contrast. He's an example of someone who was a visionary user. For a person like that, there are a lot of people that will give you more local feedback, but some people can see the big vision too. That's always really exciting because it's validation for you and the team about where you should go, but also a source of new ideas and insights.

Uh, kickback. Okay.

Early stage recruiting is so hard. So first of all, just don't give up. My first piece of advice. Second piece of advice is just think long term. There are folks that I talked with in the first year or two of Figma and they didn't join until 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 eventually it turns into something that could be they join the company or actually just they're 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. Only the true believers are going to get on board. And I'm a fan of just not selling too hard. 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 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: I told John Doerr one day that I was having a lot of problems with recruiting, I wasn't very good at it. He said, 'Do you wake up in the morning and is the first thing you think about recruiting?' I said, 'Well, no, I'm thinking about coffee.' He said, 'Okay, well then, mid-morning, are you thinking about recruiting?' I said, 'No, I'm probably thinking about what snack I'm going to have.' He said, 'Okay, lunch, are you thinking about recruiting?' I said, 'No, I'm probably thinking about what emails I have to do.' He said, 'Last thing at night, you're about to go to bed, are you thinking about recruiting?' I said, 'No, definitely not, I'm tired.' He said, 'Well, if you're thinking about recruiting in all these moments where you have a second to pause and you're actioning on it, then it'll fix itself.' The way that can manifest as a process is you just basically make a spreadsheet of your funnel and obsessively look at it all the time and go, okay, how do I make sure this funnel keeps going, just like you would with sales. If you're a salesperson, you have to continue to feed the funnel, you have to move people through it. If you're not doing that, you're not recruiting. So yeah, you have to be very disciplined, which is something I have to push myself to. I like to be in the cloud.