Lenny Rachitsky: belief

Transcript context

…Okay. I got to read the last two. I haven't read those, but they sound perfect. Okay. Maybe I'll throw one more. Let's say we talked about this earlier, but actually read this in the post many years ago, is optimizing for the pace of learning and knowing that long-term massive thing that's going to drive impact. I think it can be hard if you're a PM for a feature team. You're part of a big company. I don't know. I'm making this up. You're on the AdWords team at Google and you're responsible for the bid input selector or something and probably is a whole team, honestly, now at this point. You've got such a set of blinders on that I think it can be hard to think about what else could this team become, what else could you drive beyond the thing that's right in front of you? Optimizing for learning, being willing to take those bolder bets, knowing you can be wrong in the short-term, but that you'll learn new levers that will be really fruitful in the long-term. It's a portfolio approach to product, but I think a really important one. I was just interviewing a product leader at Asana, Paige Costello. We were talking about how she's often the youngest person in the room and often manages people that are much older than her and more experienced than her and asked her just how do you that? How do you succeed in that environment? What she's found is just being the person that has the answers and the insights in meetings, people obviously run to her like, "Hey, what do you think of this?" Because she just knows what people are going to need. I think that's exactly what you're talking about here is just be the person that knows the most about the problem, the customers, the space. Yeah. Then I'll combine the last two just because I know time. But the combination of ... I wrote data fluency, which is not to say that every PM needs to be a statistician. I mean it's great. I mean you've had a lot of great posts about how to understand some of the basics of experimentation, correlation, causation and statistical significance. That's all great. But by data fluency, I think it's more actually what you were just saying, which is you know enough about the insights about your customers that it can then inform making higher likelihood product bets and that data can be quantitative, that data can be survey based, it can be from doing 100 meetings with customers yourself. Those are all types of data inputs to me. Being really fluent and then maybe combining that with great product taste. I know it's a controversial statement now to say that there is taste for product. But I do think in all the love of the frameworks and the analytics and everything else and in the field of product, I think people sometimes lose sight of, "It's a creative field." It's not art on its own. But you could get all the inspiration from art and I actually think there's a lot ... there's a book, I think it's called Creative Selection, I forget the exact name of it, about some of the early iPhone development teams at Apple and working with Steve Jobs there. I've never worked at Apple. But I actually think it's the best book I've read about the just iterating creative work of building new products and what it means to have taste, which is to say you've developed some amount of intuition for what people will likely love before you're able to test it. Anyway, I think taste plus fluency and data, that too is a combination, is a pretty powerful combo.…

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