Lenny Rachitsky: belief

Transcript context

…That really takes a scientific approach to proving that out. Companies like Netflix and Microsoft, there's over and over again 80 plus percent. Some companies say 90% of things fail. And so the closer you get to something that you go bear your head in the sand or go into an attic and build something for six months and ship it, the more likely it is that you are going to ship the 80% wrong stuff. Whereas the more iterative you are, the more likely that you're going to catch it sooner. And failure doesn't have to be a wall, it can be a compass, it can be the thing that leads you to the right thing. And so you always want to as best you can, get stuff in front of customers so that you can get that compass and get that compass activated, know where to go. So that means doing ugly things. I tell my teams all the time, if it's not embarrassing, you've gone too far. Got to be embarrassing. The first thing, that was embarrassing, kicking people out, onboarding, spend all this money and whatever to get them into your center flow, and then the first thing we do, get out of here. That's nuts. But if it hadn't validated, that would've been a very cheap but very valuable learning. Instead, it was a very powerful cheap learning in the other direction. Okay, now we know we can invest in it. We know that's the right thing to do. So always better to be iterative so that you are letting failure work for you instead of having it be a trap that you fall into. I know that you just shared as per experiment, you're probably wrong 80% of the time. In my experience, launching a halt redesign is as negative a hundred percent of the time. I've grown weary to avoid that as much as possible, which is like, you know that, you're taught that as you go into growth and product, but you're just like, "Nah, come on, let's just make it awesome. Just redesign this whole thing." Especially your designers is always like, "No, let's start again. Let's make it amazing." But it always ends up being negative and you're like, "Okay, well, it's too late now we got to launch this thing, we don't have time to start again." Well, it's funny, in the articles, and you'll see it was written by somebody from Microsoft who built Implementation platform and did all these cool things, as he went into actually trying to apply a scientific method of figuring out how often people are wrong about their hypotheses and what they're planning to do. He's like, "I wonder if that applies to us here at Microsoft." Even for him, that question of [inaudible 00:37:16]. And I think it's challenging when there's a lot of smart people in this space doing things and it's very difficult to think, "Gosh, am I really wrong? 80 plus, 90% of the time?" But when you think about it, makes total sense because what has to happen for something to be successful? You have to understand the problem perfectly. You have to then understand who's having the problem perfectly, the customer. At what time they're having the problem. Then you've got to put the right solution in front of them to solve that problem. Maybe you've got the problem, all that stuff, but your solution something off. Or maybe your solution is right, but maybe it's just not presented, it communicated in the right way. You could have any one of those things off and it's not going to succeed. It's not going to have the metric impact you're expecting it to have. So in that context, it's almost like incredible. We do succeed 20 to 10% of the time given everything that has to line up. And so I think it's one of those things where you really want to go into it embracing that, "Okay, this isn't about how smart I am or how good my team is or any of that stuff. It's just the logic of this is challenging to get it right and let's embrace that and let's lean into that knowledge and make it a part of our strategy," instead of finding against it.…

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