Kate Megaw

Jeff Bezos: “Any high-performing organization has to have mechanisms and a culture that supports truth telling” As Jeff explains in the clip below: “Truths often don’t want to be heard. Important truths can be uncomfortable, awkward, exhausting, 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, a political organization, or activist group—has to have mechanisms and a culture that supports truth telling.” And one of the things you have to do to support this kind of culture is talk about it: “You have to talk about the fact that it takes energy to do that. And you have to remind people that it’s ok that it’s uncomfortable. You have to literally tell people: it’s not what we’re designed to do as humans… we mostly survive by being social animals—cordial and cooperative.” He continues: “You also want to set up your culture so that the most junior person can overrule the most senior person.” And in every meeting Jeff attends, he always speaks last: “I know from experience that if I speak first, even very strong-willed, highly-intelligent participants of that meeting will [wonder if their ideas are incorrect because they’re different from Jeff’s]… Ideally you try to have the most junior go first and then go in order of seniority so that you can hear everyone’s opinion in an unfiltered way… Because we really do change our opinions—if somebody you really respect says something, it makes you change your mind a little.” Jeff also points out that a lot of the most powerful truths aren’t always based on data—they turn out to be hunches, are based on anecdotes, or are intuition-based: “You may feel yourself leaning in. It may resonate with a set of anecdotes you have. And then you may be able to say: ‘something about that feels right. Let’s go collect some data on that and try to see if we can know if it’s right.’” And lastly he discusses fighting inherent biases. For example, most companies usually have an optimism bias. As Jeff explains: “If there are two interpretations of a new set of data—one is happy and the other is unhappy—it’s a little dangerous to jump to the conclusion that the happy interpretation is right. You may want to compensate for that human bias of trying to find the silver lining and say ‘that might be good, but I’m gonna go with it’s bad for now until we’re sure.’”