Transcript

And then I got to GitHub and I found that the company had some kind of stage fright because, like, it was such a beloved product, it was so well designed. And it was, it was many of the people who had originally created GitHub were gone. And so the inheritors of it were worried about desecrating the legacy and like, we're a little bit nervous to ship anything and had to be kind of perfect when it shipped. And so it's like, OK, break the stage fright. Like we're going to just throw a lot of pots and hopefully, like, we get good at this eventually. And then the other thing I did was I talked to users. Chip stuff, ship stuff. Yeah. I mean, the main thing is like always about learning. And so how quickly can you figure out if your idea is? Good. And how to change it to be better. And so yeah, it's the cycle time. Like if you can insert a temperature probe into a product team or an engineering team and only get one number out that and like determine whether it's healthy or not. I think the number you want is how long it takes to go from an idea to something that's shipped to users to like. Observing the feedback from how they do or don't use it, like having an improved idea and and the faster that is, the faster you can learn. Now of course it what's a good target for the duration of that loop. Well, I mean, like early stage for early stage product where you're really not sure, you know, like it's really nice if you can do that in one day, which is, which is true sometimes, you know, you, you mean Stripe was famously amazing at this. You, you and John would sit down with people who were installing Stripe in their business and like immediately learn what the problems were and fix them. That's the Stripe was tiny at that point. Can you do something like that for an organ? I mean, GitHub was already at enormous sprawling scale. Can you get that loop down to? I think so, yeah. I mean, there's things that should be slow moving, maybe like your database, although maybe that should have been faster moving. And then there's things that need to be fast moving, which is like you're trying to figure out what you're always solving for, the intersection of two sets, which is what is something we can build that doesn't exist that will work, And what is something that people really want to use, like every day that they don't know that they want to use? And you have all these unknowns going into it. You have your own hypothesis, your intuitions. Based on your own usage. And so you start with those and then you, and then you sort of iterate and loop and figure it out. And so that tightening that loop is like, I think very, very important. And yes, you can do it in big companies. I mean, we're definitely doing it right now at Meta, You know, we, we ship. Is it a culture change? It's a huge culture change.