00:19:52 · Molly O'Shea
[00:19:52] S0: There was a tweet that you posted retweeting a graph of number of apps that are being released and then the actual usage of those apps. We actually just, I talked about that with Mark Pincus. He's super product guy. He he definitely pays attention to that. But in that point, you were saying execution is cheap, but design and creativity are actually very, very important and separates the wheat from the chaff. But in terms of that, like, what kind of risks should people be taking? Like, what do you see is kind of, like, overlooked right now?
00:20:27 · Dylan Field, Molly O'Shea
[00:20:27] S1: I think so much is overlooked. I mean, everything from, like, what problem are you even tackling to how do you brand, what positions do you take as a company, how do you connect with people, and what emotions are you trying to bring through your brand and marketing, but also through the application software. And then for the software side, I think, I'm just eager to see not only more interaction patterns tried, but really folks understanding what is the core thing that people need and pushing all the way to get there. It's so much easier to apply patterns that you've already seen. But I think that there's just like this huge unexplored space out there of stuff that, you know, it takes a long time to get right. And that is a risk because you might not actually hit. But if you can actually go all the way and think through the system really well and hold it in your head and hold it in your team's head, which is even harder to communicate it. Like you kind of have to memify it inside your company sometimes, but if you can do that and you can come up with like more novel ways for people to interact with systems, that's where I think, you know, it'll really be amazing. And the example is I'm still shocked that we're in a prompting age. Like, it's been, what, five years since GPT-three now? I think it's 2021, June 2021 GPT-three. [00:22:04] S0: Really?
00:22:04 · Molly O'Shea, Dylan Field
[00:22:04] S0: Really? [00:22:05] S1: I think so. And That's crazy. Yeah. And, like, we're still prompt in a way. There are other ways to explore latent space. Mhmm. I just think that the interfaces for how you interact with models will evolve so much. And to the degree that models are part of software, we will see a ton of exploration there. [00:22:26] S0: When we're talking about brand and and personality and forming trust, AI has really stopped a lot of trust in technology and just kind of like mainstream media. It's like I think it's like literally worse than ICE. So what builds a trusted brand?
00:22:47 · Dylan Field
[00:22:47] S1: Well, AI, first of all, I would say is really bimodal case, right? Because you've got folks that are literally in the case of a 4.0 or Quad, in love with the model. And I don't just mean, like, they really like its outputs. Like, they have affection for the model. I would say that's trust. And then you've got people that are like, this take it could take my job. I'm listening to things people are saying about how it's gonna destroy the world or the economy or whatever and doom. And so that is couldn't be further apart, like the super fans versus the super detractors. But in terms of just generally brand, I think it's consistency over time. And how do you actually show up in a way that people can expect, can model in their heads, and keep living your values, keep showing up the right way, and really doing things with the customer's interest at heart. If you're not doing that, what are you doing? Yeah. So I also go back to a line that Tim O'Reilly would always say. My first internship professionally was, like, at O'Reilly Media, and I loved it there. They were amazing to me, and I'm so grateful to them. Tim O'Reilly would always talk about create more value than you capture. And I think that is extremely important. And when you're perceiving, you know, singletons for AI, folks that, you know, you perceive might want to become monoliths and they're not saying that they're not going to want to do that. There's going to kind of be like a black hole and suck it all up, which I'm sure that that's not going to be what ends up happening. I think that they're all gonna get a lot smarter than that. And I think most don't even wanna do that in the first place. But yeah, the definition of a platform is that you're creating even more value outside of the platform than you are from the platform itself. This is also the Microsoft definition. It's like, what's the GDP of everything created on Windows back when, you know, Windows was the top priority of Microsoft? They wanted that sort of, GDP of stuff built on Windows to be greater than the revenue of their Windows line. And I think that's a really special way to think about ecosystem. And yeah, overall, the more that I think we lean into that kind of mindset as an industry and AI companies are vocal about that and show that, then I think people have more trust.
00:25:34 · Dylan Field, Molly O'Shea
But the doomerism has to stop as well, and it has to stop with good rationale. We have to come up with the best arguments we can for not what can go wrong, but how we're going to go address it and paint an optimistic future vision for humanity. [00:25:52] S0: Right. I've been seeing a shift in sentiment towards productivity, not job loss. And so while, like, I guess it's weird, but kind of the new North Star is to have more agents than employees. Because they'll help equip your employees and you'll run faster, you'll be able to do more as an organization, you can be more on the offensive side, which I think you and Figma have been doing really well. And then, I mean, you could also see this with, like, Figure AI. Like, they now have more humanoid robots at their office than employees. Obviously, they're gonna sell them, but still. [00:26:25] S1: Yeah. It's a little different. [00:26:26] S0: It's a little bit different. But I'm curious your standpoint as the proliferation of AI agents increase, like how does that change the internal dynamics of software and how you're selling and the products you're creating and all that?
00:26:41 · Dylan Field
[00:26:41] S1: I think that there's a bunch of ways this could play out and so we'll have to see. But overall, first, I think it's not even clear how do you define the unit of an agent? Yeah. So that's kind of a difficult one. But yeah, in general it should be the case that there's great services that you offer if folks are trying to do inference and use AI with your software and your platform that should result in like value to the customer and it should be the case that that's driven I think by humans. Overall, I think that humans being the loop will be the way that you steer towards the best outcomes. And I think that all my experience with AI so far have shown that on formal domains, things that are verifiable, you can get to amazing outcomes with enough IQ points. And yet judgment is somehow not purely correlated with just adding IQ points to the model. Design certainly does not seem to be correlated with adding IQ points to the model. These seem to be other capabilities that you have to figure out how to train and judgment I'm really curious about in particular because yeah just intuitively you know the folks that are really good at strategy like I have not even found with humans the common characteristic of how to find them. In design, think, you know, how do you teach a model true empathy for a user? How do you make it so that they can not just, you know, read a transcript of a user interview, but understand the relationships that user has, the inflections in their voice when they're talking about the problems they have and how it matters, and connect that with, like, their world and actually have that empathy for their worldview and where they're coming from to build for them, I I think it's really hard.