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a16z Podcast: Principles and Algorithms for Work and Life

a16z Podcast: Principles and Algorithms for Work and Life

49:522018-04-21

Om avsnittet

with Ray Dalio (@raydalio), Alex Rampell (@arampell), and Sonal Chokshi (@smc90) Can one really apply the lessons of history and of the past to the present and the future, as a way to get what they want out of life? By deeply understanding cause-effect relationships -- clearly expressed, shared with others, overlaid with data, back-tested, modified -- you can build a set of principles/algorithms/recipes for dealing with the realities of your life, observes Ray Dalio in this episode of the a16z Podcast (in conversation with a16z general partner Alex Rampell and Sonal Chokshi). Dalio's book Principles: Life and Work originated as an internal company document that was posted online years ago and has been shared widely since; he is the founder, chairman, and co-chief investment officer of Bridgewater Associates -- one of the top five private companies in the U.S., which manages over $150 billion and has made more money for clients than any other hedge fund. "Is this is a duck, how do I deal with ducks; or this is a species I haven't seen before, and how do I deal with that?" In other words, when you see a particular thing coming over and over again, you can know what you're seeing and how to act on it. But what about timing, which is a huge factor when it comes to making various bets and decisions in both work and life? And what if a phenomenon is entirely new and hasn't been seen before (is there such a thing), and also, how do we avoid an overly pattern-matching/ pattern-recognition trap? Having a framework can still help -- even if the phenomena don't have a clear set of rules like chess -- because we can understand why things might be different. Knowing that is important, argues Dalio. The conversation covers everything from the differences between private and public investing, and between startups and big companies -- to how people, teams, organizations, and even nation-states can evolve through principles like "believability-weighted idea meritocracies" and more. But... can adults really change? What are the differences between the two you's, and between closed-minded and open-minded people, and how do they play out across the roles of a "teacher", "student", or "peer" in organizations of varying scale? It's not as obvious as you might think, and knowing how you know -- and what we don't know -- can help.

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