This is a transcript of a conversation (recorded Dec 19, 2023) where Jeff Bezos discusses the difficulty and organizational importance of truth-telling. He argues humans are social (not truth-seeking) by default, explains why truth can be uncomfortable, and suggests cultural/mechanical practices (e.g., senior people speak last, allow junior people to overrule if they have data) and cautions about biases and the role of intuition versus data.
Chapters
You have just asked a million dollar question. So this is, this is what you're, if I generalize what you're asking, you're talking in general about truth telling.
Yeah.
And we humans are not really truth seeking animals. We are social animals.
Yeah, we are.
And you know, take you back in time 10,000 years and you're in a small village. If you go along to get along, you can survive, you can procreate. If you're the village truth teller, you might get clubbed to death in the middle of the night. Truths are often, they don't want to be heard because important truths can be uncomfortable, they can be awkward, they can be exhausting, impolite and all that kind of stuff. Challenging. They can make people defensive, even if that's not the intent. But any high performing organization, whether it's a sports team, a business, you know, a political organization, activist group, I don't care what it is, any high performing organization has to have mechanisms and a culture that supports truth telling. One of the things you have to do is you have to talk about that and you have to talk about the fact that it takes energy to do that and you have to talk to people. You have to remind people it's okay that it's uncomfortable. You literally tell people it's not what we're designed to do as humans. It's not really, it's kind of a side effect. You know, we can do that, but it's not how we survive. We mostly survive by being social animals and being cordial and cooperative. And that's really important. And so there's a, you know, you know, science is all about truth telling. It's actually a very formal mechanism for trying to tell the truth. And even in science you find that it's hard to tell the truth. Right. Even, you know, you're supposed to have hypothesis and test it and find data and reject the hypothesis and so on. It's not easy.
But even in science there's like the senior scientists and the junior scientists and then there's a hierarchy of humans where the somehow seniority matters in the scientific.
Process, which is, and that's true inside companies too. And so you want to set up your culture so that the most junior person can overrule the most senior person if they have data. And, and that really is about trying to, you know, there are little things you can do. So for example, in every meeting that I attend, I always speak last. And I know from experience that, you know, if I speak first, even very strong willed, highly intelligent, high judgment participants in that meeting will wonder, well, if Jeff thinks that I came in this meeting thinking one thing, but maybe I'm not right. And so you can do little things, like if you're the most senior person in the room, go last, let everybody else go first. In fact, ideally try to have the most senior person go first. And the second, then try to go in order of seniority so that you can hear everyone's opinion in a kind of unfiltered way. Because we really do. We actually literally change our opinions. If somebody who you really respect says something makes you change your mind a little.
So you're saying implicitly or explicitly give permission for people to have a strong opinion, that as long as it's backed by data.
Yes, and sometimes it can even. By the way, a lot of our most powerful truths turn out to be hunches. They turn out to be based on anecdotes. They're intuition based. And sometimes you don't even have strong data, but you may know the person well enough to trust their judgment. You may feel yourself leaning in. It may resonate with a set of anecdotes you have. And then you may be able to say, you know, something about that feels right. Let's go collect some data on that. Let's try to see if we can actually know whether it's right. But for now, let's not disregard it because it feels right. You can also fight inherent bias. There's an optimism bias. Like if there are two interpretations of a new set of data and one of them is happy and one of them is unhappy, it's a little dangerous to jump to the conclusion that the happy interpretation is right. You may want to sort of compensate for that human bias of looking for, you know, trying to find the silver lining and say, look, this, that might be good, but I'm going to go with it's bad for now until we're sure.
