Julie Zhuo on building Sundial — the 'Cursor for data analysis' — and lessons from Meta's data-driven culture

grows your company to the next level. We have our next guest already in the reream waiting room. We will bring her in now. How are you? Welcome. Hi. Nice to be here. Great. Great to meet you, Julie. What's happening? Great to Yeah.

Would you mind kicking us off with a little bit of an introduction on yourself, uh, your company and maybe some of your background as well? Sure. So I am one of the founders of Sundial. We are trying to build the cursor for data analysis.

So we're trying to automate a lot of what data scientists will do um so that we can help companies make better decisions with data. We work with great companies like OpenAI character captions. Before that I uh led design and research for the Facebook product. I grew up at Meta back then on Facebook.

Uh could you explain for the audience what Facebook is? They've been living under a data center. No, I'm kidding. Sorry. So, you know, if if you if you guys become friends and your relationship gets to a certain level and you want to, you know, let the world know can amazing broadcast. Amazing.

Well, I think it it feels like the absolute perfect background to build Sundial because when I think when when I think people think about optimizing digital products and making decisions with massive amounts of data, people think Meta and they think Facebook, right? Totally. Yeah.

I mean, you say that because you probably know more than most people, but I do think what makes Meta so good is that it was so freaking operationally rigorous. It was so good at actually capturing and modeling what made it a great business.

Um, and and being like, you know, just so on top of every little thing so that we could optimize, iterate, experiment, test, and eventually, you know, get to that scale. Yeah.

And and did you guys, I imagine, had to rely on a bunch of internal tooling that you built in order to do that that was proprietary or or what did that look like? Absolutely. Yeah. Everything was just homegrown because like actually the whole concept I would say that Meta probably introduced this idea of growth.

I mean now everybody's got growth teams but back then that was like a new thing and it was only created you know with people like Chimath and Alex Schultz and Naomi because Facebook was growing like crazy and then one day growth kind of stalled and you know every I think this happens to every successful company right in the beginning you're like we're we're we're great our intuition is on we're solid like everything hits and then one day it stops hitting and then everyone's like oh my god what happened and there's a lot of questions you know board board members are calling and then it becomes like well we have to figure it out and the only way you can do that is with good observability.

Yeah. Can you take us through like a concrete example of uh like one of the like a case study of like design at meta and and and how um like some of the decision-m linked to some of the data analysis you did? Like do you have anything that you can share?

I I know it's been a while but some of the stuff's probably private but some of it you can probably share. Yeah. Um I'm trying to figure out um what's an example I can pull out. Well, I will say that like a lot of the process so I I think we kind of segment things into two worlds, right?

The first is like look in order to build something new, you do need that spark of inspiration and it usually comes from intuition. Like you need to kind of have like a vision and so often that would be like the spark of the idea.

But then as soon as you realize that you have like a hypothesis you know like I think what people want is you know better ways like if we give people more custom ways of sharing on Facebook for example they will share more that's an example of like a particular hypothesis that somebody would have and usually that comes with like an idea you're like if I build you know a really awesome text editor or I give them a way to record video or you know I make it super easy to like pull in a quote Right?

Then people will go and share more. But the thing that you then need to do is to figure out well how quickly can you test whether that hypothesis is true? Like what's the what's the kind of smallest thing that you can build that gives you either more or less assurance that this thing is is the thing to bet on.

And so we would try and figure out what's like the smallest framing of that experiment.

Um and then if it seems like you know okay it's inclusive or we actually think it might be promising then you just double down right then you like add more you know you keep building and I think that um that that's been like you know very this is like a super early example but I think like hackathon project like video was like a hackathon project that somebody did um and then very early on like video came out after the hackathon and like just very quickly you can see um what what will you know like that that people are actually using.

In fact, maybe a better example now that I'm thinking about it is actually feed. So, feed is one of those very early examples where people hated it. So, newsfeed came out. Before that, the way that people went to profiles is like, you know, I would go to your profile, John. I would go to your profile, Jordy.

I'd see if anything changed. But there was no system that automatically told me if you updated something. And so we in launch feed, this was like back in 2006, and there were like huge protests. In fact, like a million people within a week joined this Facebook group called I effing hate Facebook newsfeed.

Um because people thought it was like it was creepy. It was like, you know, um too much surveillance.

But the reason I think Mark and the team like we kept on it is the fact that like through the numbers like through the data you could see that people were spending a lot of time on newsfeed and in fact the reason they knew to join this group I effing hate newsfeed is because of newsfeed like it was they were like you know so it just was like very obvious to us that this was working despite what people were saying about it.

Yeah. Uh can you talk about preference falsification more broadly? Was that like a hard one lesson that carried through? It feels like Meta has really um like absorbed that lesson and it continues to inform the company culture. Have you carried that idea of preference falsification forward into your career?

Uh how do you think about that disconnect between what people say and then what they actually do? I think it just like you have to gather the data.

You just have to look at you know like you can say okay look this is what I see I see it like data their data is that people tell us they don't like it or they tell us they're feeling one way or another right so I think about qualitative feedback as data customer surveys are you know or data like anything is data but also behavior like what do people actually do what do they click on where are they spending their time and attention and the more we can have all of that in one place the better like a a better more realistic and accurate view of reality we have.

When should a company sign up for Sundial? Because I I I think there's a there's it's almost a meme in Silicon Valley.

If you if you launch a product early and you're obsessing maybe too much over the data, you could you could go end up actually going down the wrong path and and there's points in which uh maybe you disagree with this where the actual just like founders intuition is what what's going to get you to that um sort of uh takeoff with a product.

But what's your read? I agree with that. I do think that it's there's such a thing as like obsessing too early. Like when you don't have product market fit, you probably have to take bigger swings. You're probably not in the business of trying to optimize.

But I think if you feel like you have product market fit, you're clearly scaling your team. Like you're like, "Okay, I probably need a growth person. I probably need a data. " Like the reason you want a growth or data person is you want to form more fuel on the fire.

Like you want to understand what works and what doesn't. And so what all of these roles are trying to do is start a series of experimentation and just like again get to better uh better visibility, right?

Better observability for like what we all want to do is just build a mental model like this is how the business works and if you tweak this thing more money goes up and you tweak this more users, right?

We want to build a very real and um influencable model and that is what like all of that work of trying to build uh and and and do analysis is is there to help us do. Did you overlap with Laura Dana at uh Meta? Can you tell me a little bit about her? Well, yeah.

What everyone she was in the Wall Street Journal earlier this week. Um what what what's made her uh you know what's the secret to her impact? So Laura Dada joined as um a design lead and I think she took over a messenger.

So, Facebook Messenger and I think the team was pretty small then, maybe about eight designers and she helped scale that up to like 200 designers and eventually she actually become the product group lead.

So, the product group lead is kind of like the GM role and in fact it's there's not that many examples of design leaders who become GMs, you know, like engineers can become GMs like um I think Adam Oseri, head of Instagram is one. I think Lordana is probably the other that I can recall.

Um, so she ended up uh actually leading all of Messenger. I think recently she went to go work on AI and I just saw the news that she is joining as Figma's new uh chief design officer, which is fantastic news. I absolutely adore Laura. I think she is fabulous. Yeah, we'll have to get her on the show.

That would be uh it'd be it'd be fun to dig into. Um anyway, Jordy, anything else? I have a bunch more questions, but I don't think we have time. Yeah, um and uh yeah, congrats on on all the progress so far. This is fantastic. We'll talk to you soon. All right, talk to you guys soon. Bye. Bye. Cheers.

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