The AI-First Graphics Editor - with Suhail Doshi of Playground AI

**Alessio** (0:06)
Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, Partner and CTO and Resident at Decibel Partners, and I'm joined by my co-host, Swix, founder of SmallAI.

**Swyx** (0:15)
Hey, and today in the studio, we have Suhail Doshi. Welcome.

**Suhail Doshi** (0:18)
Yeah, thanks. Thanks for having me.

**Swyx** (0:20)
So among many things, you're a CEO and co-founder of Mixpanel, and I think about three years ago, you left to start Mighty.
And more recently, I think about a year ago, transitioned into Playground, and you've just announced your new round. How do you like to be introduced beyond that?

**Suhail Doshi** (0:34)
I just, you know, founder of Playground is fine. Yeah, prior co-founder and CEO of Mixpanel.

**Swyx** (0:39)
Yeah, awesome. I just like to touch on Mixpanel a little bit, because it's obviously like one of the more sort of successful analytics companies we previously had amplitude on. And I'm curious if like you had any sort of reflections on like the interaction of like that amount of data that people would want to use for AI. Like, I don't know if there's still a part of you that stays in touch with that world.

**Suhail Doshi** (0:59)
Yeah. I mean, you know, the short version is, is that maybe back in like 2015 or 16, I don't really remember exactly because it was a while ago. We had an ML team at Mixpanel. And I think this is like when maybe deep learning or something like really just started getting kind of exciting.
And we were thinking that maybe we, you know, given that we had such vast amounts of data, perhaps we could predict things. So we built, you know, two or three different features. I think we built a feature where we could predict whether users would churn from your product. We made a feature that could predict whether users would convert.
We built a feature that could do anomaly detection. Like if something occurred in your product, that was just very surprising. Maybe a spike in traffic in a particular region. Can we tell you that that happened? Because it's really hard to like know everything that's going on with your data. Can we tell you something surprising about your data? And we tried all of these various features.
Most of it boiled down to just like, you know, using logistic regression. And it never quite seemed very groundbreaking in the end. And so I think, you know, we had a four or five person ML team.
And I think we never expanded it from there. And I did all these Fast AI courses trying to learn about ML. And that was the first time you did Fast AI. Yeah, that was the first time I did Fast AI. Yeah, I think I've done it now three times, maybe.

**Swyx** (2:12)
I didn't know it was the third.

**Suhail Doshi** (2:13)
No, no, just me reviewing it is maybe three times.

**Swyx** (2:15)
But yeah, you mentioned prediction. But honestly, like, it's also just about the feedback, right? The quality of feedback from users. I think it's useful for anyone building AI applications.

**Suhail Doshi** (2:25)
Yeah.

**Swyx** (2:25)
Self-evident.

**Suhail Doshi** (2:26)
Yeah, I think I haven't spent a lot of time thinking about Mixpanel because it's been a long time. But sometimes I'm like, oh, I wonder what we could do now. And then I kind of like move on to whatever I'm working on. But things have changed significantly since.

**Swyx** (2:38)
And then maybe you'll touch on Mighty a little bit. Mighty is very, very bold.
My framing of it was you will run our browsers for us because everyone has too many tabs open. I have too many tabs open and slowing down your machines. Maybe you can do it better for us in a centralized data center.

**Suhail Doshi** (2:51)
Yeah, we were first trying to make a browser that we would stream from a data center to your computer at extremely low latency. But the real objective wasn't trying to make a browser or anything like that. The real objective was to try to make a new kind of computer.
The thought was just that we have these computers in front of us today, and we upgrade them or they run out of RAM, or they don't have enough RAM or not enough disk, or there's some limitation with our computers. Perhaps data locality is a problem. Why do I need to think about upgrading my computer ever?
Actually, it seems like a lot of applications are just now in the browser. It's like how many real desktop applications do we use relative to the number of applications we use in the browsers? There's just this realization that actually the browser was effectively becoming more or less our operating system over time, and so then that's why we decided to go maybe we can stream the browser. Fortunately, the idea did not work for a couple of different reasons, but the objective is to try to make a true new computer.