The Exchanges

Q machine learning and AI. And you were almost like too early, right? It's like maybe seven years ago, the models weren't quite there. How should people think about revalidating like expectations of this technology? You know, I think even today people will People tell you, oh, models are not really good at X because they were not good 12 months ago, but they're good today. What's your process for that?

A Heuristics for thinking about that or how is, yeah, I think the way I look at it now is pretty, has evolved a lot since when I started. I mean, I think everybody intuitively starts with like, all right, let's try to predict the future or imagine like what's this great end state we're going to get to. And the tricky thing is like often those prognostications are right, but they're right in terms of direction, but not when. For example, You know, even in the early days of the internet, nineties, when things were even, like, tech space and, you know, even before, like, the browser or things like that, people were like, oh man, you're gonna have, you know, you're gonna be able to order food, you get, like, a Snickers delivered to your house, you're gonna be able to watch any movie ever created, and they were right, but they were like, you know, it took, you know, 20 years for that to actually happen, and before you got to DoorDash, you had to get, you started with, like, Webvan and Cosmo, and before you get to Spotify, you had to do, like, Napster and Kazaa and LimeWire, And like a bunch of like broken Britney Spears MP three's and malware. So I think the big lesson is, um, being early is the same as being wrong. Being late is the same as being wrong. So really how do you calibrate timing? And then I think with AI, it's the same thing that people are like, oh, it's gonna completely…

AI assessment note: “often those prognostications are right, but they're right in terms of direction, but not when”

Q Before we jump into Dash, let's talk about the Drew Haas and AI engineering. So IDE, let's write that down. What IDE do you use? Do you use Cursor, VS Code? Do you use any coding assistant, like, with chat? Is it just autocomplete?

A Yeah, yeah, uh, both. So I use VS Code as, like, my daily driver, although I'm, like, super excited about things like Cursor or the AI agents. I have my own, like, stack underneath that. I mean, some off-the-shelf parts, some pretty custom. So I use the continue.dev just like AI chat. Uh, UI basically as just the UI layer, but I also proxy the request. I don't, I proxy request my own backend, which is sort of like a router. You can use any backend. I mean, Sonnet three five is probably the best all around, but then these things are like pretty limited if you don't give them the right context. And so part of what the proxy does is like, there's a separate thing where I can say like include all these files by default with the request. Then it becomes a lot easier and like without like cutting and pasting. And I'm building, we'll say, like, prototype toy apps. There's, like, a front-end React thing and a Python back-end thing, and so it can do these, like, end-to-end diffs, basically. And then I also, like, love being able to host everything locally or do it offline, so I have my own, when I'm on a plane or something or where, like, you don't have access or the Internet's not reliable, I actually bring a gaming laptop on the plane with me. It's, like, a little, like, blue briefcase-looking thing, and then I, like, literally hook up a GPU, like, into one of the outlets, and then I …

AI assessment note: “I use VS Code as, like, my daily driver... I use the continue.dev”

Q their AI feature? So like vice versa, you know, if I can connect my Dropbook storage to this other AI assistant. How do you kind of think about that, about, you know, not being able to capture all the value and how open people will stay? I think today things are still pretty open, but I'm curious if you think Things will get more close or, like, more open later.

A Yeah. Well, I think you have to get the value exchange right, and I think you have to be, like, a trustworthy partner, or, like, no one's gonna partner with you if they think you're gonna eat their lunch, right, or they'll, if you're gonna disintermediate them, and, like, all the companies are quite sophisticated with how they think about that, so we try to, like, we know that's gonna be the reality, so we're actually not trying to eat anyone's, like, Google Drive's lunch or anything. Actually, we'll, like, integrate with Google Drive, we'll integrate with OneDrive, Really any of the content platforms, even if they compete with file syncing. So that, that's actually a big strategic shift. We're not really reliant on being like the store of record. And there are pros and cons to this decision. But if you think about it, we're basically like providing all these apps more engagement. We're like helping users do what they're really trying to do, which is to get, you know, that Google Doc or whatever. And we're not trying to be like, oh, by the way, use this other thing. This is all part of our like brand and reputation. It's like, no, we, we give people freedom to use whatever tools or operating system they want. We're not taking anything away from our partners. We're actually like making it, making that thing more useful or routing people to those things. I mean, on the margin, th…

AI assessment note: “no one's gonna partner with you if they think you're gonna eat their lunch”

Q your own data centers. Where are we in the AI way for that? I don't think people really care today to bring the models in house. Like, do you think people will care in the future? Like, especially as you have more small models, like you want to control more of the economics or are the tokens so subsidized that like, it just doesn't matter. It's more like a principle.

A Yeah. Yeah. I mean, I think there's another one. Where, like, thinking about the future is a lot easier, or if you start with the past. Um, so, I mean, there's definitely this, like, big surge in demand as, like, there's sort of this FOMO-driven bubble of, like, all of big tech taking their earnings and shipping them to Jensen for a couple years. And then you're like, alright, well, first of all, we've seen this kind of thing before, and, you know, in the late nineties with, like, fiber, you know, this huge race to, like, own the internet. The only information superhighway literally, and then way overbuilt. And then there was this like crash. I don't know to what extent, like maybe it is really different this time, or, you know, maybe if we create AGI that will sort of solve the rest of the, or we'll just have a different set of things to worry about. But you know, the simplest way I think about it is like, this is sort of a rent, not buy phase. Cause you know, I wouldn't want to be, we were still so early in the maturity. I, you know, I wouldn't want to be buying like pallets of over, like of Two eighty-sixes at a five X markup when like the three 86 and four 86 and Pentium and everything are like clearly coming there around the corner. And again, because of open source, there's just been a lot more competition at every layer in the stack, and so product developers are basical…

AI assessment note: “the simplest way I think about it is like, this is sort of a rent, not buy phase.”

Q Dropbox and he's like, you know, this is just a feature. It's not a product. And then you build like a ten billion dollar feature. Uh, how in the age of AI, how do you think about, you know, maybe things that used to be a product or now features because the AI on top of it, it's like the product, like what's your mental model to think about it?

A Yeah. So I don't think there's really like a bright line. I don't know if like I use the word features and products in my, Mental model that much of how I break it down. Cause this is kind of a, it's a good question. I mean, I don't not think about features or not think about products, but it does start from that place of like, all right, we have all these new colors we can paint with and all right, what are these higher order needs that are sort of evergreen, right? So people will always have stuff at work. They're always needed to be able to find it or all the verbs I just mentioned. It's like, okay, How can we make like a better painting and how can we, and then how can we use some of these new colors? And then, yeah, it's like pretty clear that after the large models, the way you find stuff, organize stuff, share stuff, it's going to be completely different after COVID. It's going to be completely different. So that's the starting point, but I think it is also important to, you know, you have to do more than just work back from the customer and like what they're trying to do. Like you have to think about, and you know, we've, we've learned a lot, a lot of this the hard way sometimes. Okay, you might start with a customer, you might start with a job to build, and then you're like, alright, what's the solution to their problem? Or like, can we build the best product that solv…

AI assessment note: “I don't know if like I use the word features and products in my, Mental model”

Q know you're a big Pats fan. Obviously, that's a great example of building a dynasty on, like, some building blocks and bringing people in the system. When you're building a company, like, how much slack do you cut people on, hey, you're gonna learn this, versus, like, how do you measure, like, the learning rate of the people you hire, and, like, how do you think about picking and choosing?

A Great question. It's hard. Um, what you want is a balance, right? And we've had a lot of success with great leaders who I actually grew up with a company, started as an, you know, IC engineer or something, then made their way to whatever level. Our exec team is populated with a lot of those folks, but, but you, but there's also a lot of benefit to experience and having seen different environments and kind of been there, done that. And there's a lot of drawbacks to kind of learning by trial and error only. Um, and then even your high potential people like can go up the learning curve faster if they have like some experience to learn from. Now, like experience isn't a panacea either. Like you can, you know, Have various organ rejection, or misfit, or like, overfitting from their past experience, or cultural mismatches, or, you know, you name it. I've seen it all. I've kind of gotten all the mistake merit badges on that. But I think it's like constructing a team where there's a good balance, like, okay, for the high potential folks who are sort of in the biggest jobs of their lives, can, do they either have someone that they're, is managing them, that they can learn from, you know, as a CEO, part of your job, or as a manager, like, you have to, like, surround, or help support them. So getting the mentors or getting first time execs, like mentors who have been there, done that, or,…

AI assessment note: “what you want is a balance, right? And we've had a lot of success”